Once you've chosen your scenario, download the data from the Iowa website in csv format. Start by loading the data with pandas. You may need to parse the date columns appropriately.
import pandas as pd
#sales = pd.read_csv("iowa_liquor_sales_proj_2.csv")
sales = pd.read_csv("Iowa_Liquor_sales_sample_10pct.csv")
## Load the data into a DataFrame
# pd.read_csv()
## Transform the dates if needed, e.g.
# df["Date"] = pd.to_datetime(df["Date"], format="%m-%d-%y")
Perform some exploratory statistical analysis and make some plots, such as histograms of transaction totals, bottles sold, etc.
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import scipy.stats as stats
plt.style.use('fivethirtyeight')
# plt.style.use('ggplot')
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
import sklearn.linear_model as skl
import statsmodels.api as sm
from sklearn import datasets, linear_model
from sklearn.model_selection import cross_val_score, cross_val_predict
from sklearn import metrics
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import roc_auc_score as roc_auc
from sklearn.model_selection import train_test_split
from matplotlib.colors import ListedColormap
from sklearn.preprocessing import StandardScaler
from sklearn.datasets import make_moons, make_circles, make_classification
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier
from sklearn.naive_bayes import GaussianNB
C:\ProgramData\Anaconda3\lib\site-packages\statsmodels\compat\pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead. from pandas.core import datetools
def eda_tool(df):
import pandas as pd
dict_list = []
for col in df.columns:
data = df[col]
dict_ = {}
# The null count for a column. Columns with no nulls are generally more interesting
dict_.update({"null_count" : data.isnull().sum()})
# Counting the unique values in a column
# This is useful for seeing how interesting the column might be as a feature
dict_.update({"unique_count" : len(data.unique())})
# Finding the types of data in the column
# This is useful for finding out potential problems with a column having strings and ints
dict_.update({"data_type" : set([type(d).__name__ for d in data])})
#dict_.update({"score" : match[1]})
dict_list.append(dict_)
eda_df = pd.DataFrame(dict_list)
eda_df.index = df.columns
eda_df = eda_df.sort_values(['null_count','unique_count'], ascending=[True, False])
print("dataframe shape \n", df.shape, '\n')
print("dataframe index \n", df.index[:5], '\n')
print("dataframe describe \n", df.describe())
return eda_df
eda_tool(sales)
dataframe shape (270955, 18) dataframe index RangeIndex(start=0, stop=5, step=1) dataframe describe Store Number County Number Category Vendor Number \ count 270955.000000 269878.000000 2.708870e+05 270955.00000 mean 3590.263701 57.231642 1.043888e+06 256.43443 std 947.662050 27.341205 5.018211e+04 141.01489 min 2106.000000 1.000000 1.011100e+06 10.00000 25% 2604.000000 31.000000 1.012200e+06 115.00000 50% 3722.000000 62.000000 1.031200e+06 260.00000 75% 4378.000000 77.000000 1.062310e+06 380.00000 max 9023.000000 99.000000 1.701100e+06 978.00000 Item Number Bottle Volume (ml) Bottles Sold Volume Sold (Liters) \ count 270955.000000 270955.000000 270955.000000 270955.000000 mean 45974.963300 924.830341 9.871285 8.981351 std 52757.043086 493.088489 24.040912 28.913690 min 168.000000 50.000000 1.000000 0.100000 25% 26827.000000 750.000000 2.000000 1.500000 50% 38176.000000 750.000000 6.000000 5.250000 75% 64573.000000 1000.000000 12.000000 10.500000 max 995507.000000 6000.000000 2508.000000 2508.000000 Volume Sold (Gallons) count 270955.000000 mean 2.372830 std 7.638182 min 0.030000 25% 0.400000 50% 1.390000 75% 2.770000 max 662.540000
data_type | null_count | unique_count | |
---|---|---|---|
Sale (Dollars) | {str} | 0 | 6580 |
Item Number | {int64} | 0 | 2696 |
Item Description | {str} | 0 | 2173 |
Store Number | {int64} | 0 | 1400 |
State Bottle Retail | {str} | 0 | 1112 |
State Bottle Cost | {str} | 0 | 1086 |
Zip Code | {str} | 0 | 415 |
City | {str} | 0 | 385 |
Date | {str} | 0 | 274 |
Volume Sold (Liters) | {float64} | 0 | 265 |
Volume Sold (Gallons) | {float64} | 0 | 261 |
Bottles Sold | {int64} | 0 | 137 |
Vendor Number | {int64} | 0 | 116 |
Bottle Volume (ml) | {int64} | 0 | 29 |
Category | {float64} | 68 | 84 |
Category Name | {str, float} | 632 | 72 |
County Number | {float64} | 1077 | 100 |
County | {str, float} | 1077 | 100 |
sales.isnull().sum()
Invoice/Item Number 0 Date 0 Store Number 0 Store Name 0 Address 0 City 0 Zip Code 0 Store Location 0 County Number 10913 County 10913 Category 779 Category Name 6109 Vendor Number 0 Vendor Name 0 Item Number 0 Item Description 0 Pack 0 Bottle Volume (ml) 0 State Bottle Cost 0 State Bottle Retail 0 Bottles Sold 0 Sale (Dollars) 0 Volume Sold (Liters) 0 Volume Sold (Gallons) 0 dtype: int64
There are around 10,000 null values, as this data frame is 2.7 million rows I am dropping the null rows
sales = sales.dropna()
sales.duplicated().sum()
35
There are no duplicated rows
sales.columns
Index(['Date', 'Store Number', 'City', 'Zip Code', 'County Number', 'County', 'Category', 'Category Name', 'Vendor Number', 'Item Number', 'Item Description', 'Bottle Volume (ml)', 'State Bottle Cost', 'State Bottle Retail', 'Bottles Sold', 'Sale (Dollars)', 'Volume Sold (Liters)', 'Volume Sold (Gallons)'], dtype='object')
Cleaning up the column names
sales.columns = map(str.lower, sales.columns)
sales.columns = sales.columns.str.replace(r"[()]", "")
sales.columns = sales.columns.str.replace(r"[ ]", "_")
sales.columns = sales.columns.str.replace(r"[/]", "_")
Cleaning the columns and switching them to the proper data type
for col in sales.select_dtypes([np.object]):
sales[col] = sales[col].str.lstrip('$')
sales["state_bottle_retail"] = sales["state_bottle_retail"].astype(float)
sales["sale_dollars"] = sales["sale_dollars"].astype(float)
Exploring the requirements of the project (Build models of total sales based on location, price per bottle, total bottles sold. You may find it useful to build models for each county, ZIP code, or city.) I will explore the following categories: Total Sales, County, Zip Code, City, Retail Price per bottle, Total Bottles Sold.
sales.columns
Index(['date', 'store_number', 'city', 'zip_code', 'county_number', 'county', 'category', 'category_name', 'vendor_number', 'item_number', 'item_description', 'bottle_volume_ml', 'state_bottle_cost', 'state_bottle_retail', 'bottles_sold', 'sale_dollars', 'volume_sold_liters', 'volume_sold_gallons'], dtype='object')
#Counting Total Sales
sales["sale_dollars"].value_counts()
162.00 3468 148.56 2536 64.80 2066 94.20 2006 70.56 1889 90.00 1772 60.12 1711 73.80 1626 62.28 1624 117.00 1594 60.72 1556 188.88 1556 64.56 1548 180.00 1516 135.00 1511 45.00 1446 126.00 1433 30.00 1363 270.00 1311 161.64 1281 99.00 1260 72.00 1246 81.00 1228 124.20 1182 132.78 1052 58.50 1051 22.50 1004 24.76 992 67.26 982 40.50 979 ... 139.44 1 36392.40 1 90.90 1 45.70 1 71.05 1 4500.00 1 1398.42 1 575.82 1 584.94 1 314.64 1 114.06 1 2052.48 1 6285.96 1 291.12 1 1137.24 1 967.20 1 661.50 1 547.20 1 327.12 1 7927.20 1 230.94 1 537.60 1 753.72 1 7198.50 1 180.45 1 8.46 1 109.04 1 1079.04 1 1107.12 1 127.20 1 Name: sale_dollars, Length: 6552, dtype: int64
#Counting individual Counties
sales["county"].value_counts()
Polk 48944 Linn 23462 Scott 16630 Black Hawk 15030 Johnson 13163 Pottawattamie 9088 Story 8944 Woodbury 8541 Dubuque 7739 Cerro Gordo 6360 Des Moines 4082 Muscatine 3975 Clinton 3569 Wapello 3522 Dickinson 3409 Lee 3319 Webster 3144 Marshall 2984 Jasper 2828 Buena Vista 2737 Dallas 2707 Marion 2601 Warren 2460 Bremer 2240 Boone 2105 Poweshiek 2087 Clay 1917 Carroll 1911 Jones 1871 O'Brien 1720 ... Greene 675 Wright 671 Shelby 661 Ida 634 Howard 606 Humboldt 588 Adair 584 Grundy 566 Pocahontas 525 Mills 507 Louisa 484 Lucas 475 Chickasaw 464 Guthrie 437 Calhoun 424 Butler 402 Worth 387 Hancock 363 Monroe 352 Osceola 351 Keokuk 343 Taylor 298 Van Buren 245 Adams 234 Audubon 227 Decatur 223 Davis 203 Ringgold 201 Wayne 160 Fremont 27 Name: county, Length: 99, dtype: int64
#Counting individual zip codes
sales["zip_code"].value_counts()
50010 7077 52402 6938 52240 6128 50613 5267 52001 4755 51501 4652 50314 4519 50317 4425 50265 4356 52404 4242 50401 4119 52722 3699 52807 3530 52405 3502 52241 3446 52761 3389 50311 3384 51503 3382 50320 3237 52501 3206 50702 3175 50315 3091 52804 2973 50501 2972 52601 2952 50703 2885 50322 2880 50266 2843 52732 2822 50158 2682 ... 52625 17 50514 17 50044 16 50541 16 52623 15 50542 15 51053 15 50261 15 51466 14 51002 14 50071 13 50251 12 50830 12 50150 11 52223 11 51005 11 51038 10 52337 9 51553 9 50452 8 51338 7 50162 6 50540 6 50061 6 50634 6 51535 5 51453 3 51530 3 52801 2 52328 2 Name: zip_code, Length: 412, dtype: int64
#Counting individual Cities
sales["city"].value_counts()
DES MOINES 23618 CEDAR RAPIDS 18736 DAVENPORT 11469 WATERLOO 8376 COUNCIL BLUFFS 8037 IOWA CITY 7938 SIOUX CITY 7888 AMES 7534 WEST DES MOINES 7148 DUBUQUE 6854 CEDAR FALLS 5719 ANKENY 4823 MASON CITY 4119 BETTENDORF 3699 CORALVILLE 3446 MUSCATINE 3389 BURLINGTON 3137 CLINTON 3077 FORT DODGE 2972 WINDSOR HEIGHTS 2797 MARSHALLTOWN 2682 NEWTON 2538 STORM LAKE 2522 MARION 2485 URBANDALE 2424 OTTUMWA 2290 JOHNSTON 2137 ALTOONA 2103 CLEAR LAKE 2080 SPENCER 1910 ... ARMSTRONG 17 DONNELLSON 17 GILMORE CITY 16 BUSSEY 16 GOLDFIELD 15 SCHALLER 15 DANVILLE 15 VAN METER 15 WASHBURN 15 WALL LAKE 14 ALTA 14 DOWS 13 AFTON 12 SULLY 12 AURELIA 11 LOVILIA 11 DELHI 11 MERRILL 10 STANWOOD 9 MINDEN 9 LATIMER 8 EVERLY 7 Cumming 6 MELBOURNE 6 GILBERTVILLE 6 FONDA 6 GRISWOLD 5 LOHRVILLE 3 ROBINS 2 Carroll 1 Name: city, Length: 382, dtype: int64
#Counting individual bottle retail price
sales["state_bottle_retail"].value_counts()
12.38 6110 9.75 4783 13.50 4415 15.00 4222 22.50 3814 15.74 3809 10.76 2991 11.21 2944 7.50 2796 9.45 2777 10.50 2761 7.85 2618 17.24 2498 10.35 2473 11.24 2342 5.01 2334 11.43 2282 10.38 2227 12.30 2162 13.47 2142 5.06 2122 18.75 2116 16.50 2063 27.74 2051 7.13 2024 10.80 1928 5.25 1911 8.25 1869 27.00 1855 15.75 1740 ... 19.00 1 8.75 1 42.11 1 76.50 1 109.82 1 93.03 1 75.00 1 12.45 1 42.75 1 6.90 1 57.08 1 103.41 1 149.97 1 16.44 1 23.70 1 19.38 1 286.86 1 9.27 1 27.95 1 22.02 1 1.98 1 17.22 1 20.01 1 78.70 1 21.54 1 111.32 1 17.36 1 48.26 1 180.33 1 32.33 1 Name: state_bottle_retail, Length: 1104, dtype: int64
#Counting individual bottles sold
sales["bottles_sold"].value_counts()
12 72607 6 51887 2 37321 1 31058 3 27758 4 15051 24 14798 48 3765 5 3189 36 2115 18 1734 10 1383 8 1227 60 1098 30 591 72 488 7 397 120 379 96 302 84 200 9 168 144 151 15 117 180 108 42 107 240 107 300 100 90 89 150 86 44 66 ... 1116 1 157 1 372 1 2400 1 88 1 354 1 594 1 81 1 615 1 504 1 588 1 75 1 840 1 1128 1 624 1 57 1 282 1 1080 1 378 1 564 1 816 1 390 1 396 1 39 1 37 1 97 1 402 1 28 1 1050 1 33 1 Name: bottles_sold, Length: 136, dtype: int64
#Most sales are under 1,000 dollars
pd.DataFrame.hist(sales, column='sale_dollars', bins=100)
plt.xlabel('Sale Dollars')
plt.ylabel('Number of Sales')
<matplotlib.text.Text at 0x2246e493d30>
#Most bottles sold are under quantity of 100
pd.DataFrame.hist(sales, column='bottles_sold', bins=100)
plt.xlabel('Sale Dollars')
plt.ylabel('Number of Bottles')
<matplotlib.text.Text at 0x2246e6b09e8>
Now you are ready to compute the variables you will use for your regression from the data. For example, you may want to compute total sales per store from Jan to March of 2015, mean price per bottle, etc. Refer to the readme for more ideas appropriate to your scenario.
Pandas is your friend for this task. Take a look at the operations here for ideas on how to make the best use of pandas and feel free to search for blog and Stack Overflow posts to help you group data by certain variables and compute sums, means, etc. You may find it useful to create a new data frame to house this summary data.
variables = sales[['state_bottle_retail', 'bottles_sold', 'sale_dollars']]
variables.head()
state_bottle_retail | bottles_sold | sale_dollars | |
---|---|---|---|
0 | 6.75 | 12 | 81.00 |
1 | 20.63 | 2 | 41.26 |
2 | 18.89 | 24 | 453.36 |
3 | 14.25 | 6 | 85.50 |
4 | 10.80 | 12 | 129.60 |
dummy_counties = pd.get_dummies(sales.county)
dummy_counties.head()
Adair | Adams | Allamakee | Appanoose | Audubon | Benton | Black Hawk | Boone | Bremer | Buchanan | ... | Wapello | Warren | Washington | Wayne | Webster | Winnebago | Winneshiek | Woodbury | Worth | Wright | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
2 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
5 rows × 99 columns
dummy_city = pd.get_dummies(sales.city)
dummy_city.head()
ACKLEY | ADAIR | ADEL | AFTON | AKRON | ALBIA | ALDEN | ALGONA | ALLISON | ALTA | ... | WEST UNION | WHEATLAND | WILLIAMSBURG | WILTON | WINDSOR HEIGHTS | WINTERSET | WINTHROP | WOODBINE | WOODWARD | ZWINGLE | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
5 rows × 382 columns
dummy_zip = pd.get_dummies(sales.zip_code)
dummy_zip.head()
50002 | 50003 | 50006 | 50009 | 50010 | 50014 | 50020 | 50021 | 50022 | 50023 | ... | 52777 | 52778 | 52801 | 52802 | 52803 | 52804 | 52806 | 52807 | 56201 | 712-2 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
3 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
5 rows × 412 columns
Look for any statistical relationships, correlations, or other relevant properties of the dataset.
sns.heatmap(variables.corr(), annot = True, linewidths = 0.5)
<matplotlib.axes._subplots.AxesSubplot at 0x2246d949240>
There appears to be a correlation between bottles sold and sale dollars
Using scikit-learn or statsmodels, build the necessary models for your scenario. Evaluate model fit.
I am going to start prepping my data to go into the model. I will need to drop polk county since in the eda I found that it was the largest contributor to counties and since I added dummy variables I need to remove the most represented county then add a constant.
dummy_counties.columns
Index(['Adair', 'Adams', 'Allamakee', 'Appanoose', 'Audubon', 'Benton', 'Black Hawk', 'Boone', 'Bremer', 'Buchanan', 'Buena Vista', 'Butler', 'Calhoun', 'Carroll', 'Cass', 'Cedar', 'Cerro Gordo', 'Cherokee', 'Chickasaw', 'Clarke', 'Clay', 'Clayton', 'Clinton', 'Crawford', 'Dallas', 'Davis', 'Decatur', 'Delaware', 'Des Moines', 'Dickinson', 'Dubuque', 'Emmet', 'Fayette', 'Floyd', 'Franklin', 'Fremont', 'Greene', 'Grundy', 'Guthrie', 'Hamilton', 'Hancock', 'Hardin', 'Harrison', 'Henry', 'Howard', 'Humboldt', 'Ida', 'Iowa', 'Jackson', 'Jasper', 'Jefferson', 'Johnson', 'Jones', 'Keokuk', 'Kossuth', 'Lee', 'Linn', 'Louisa', 'Lucas', 'Lyon', 'Madison', 'Mahaska', 'Marion', 'Marshall', 'Mills', 'Mitchell', 'Monona', 'Monroe', 'Montgomery', 'Muscatine', 'O'Brien', 'Osceola', 'Page', 'Palo Alto', 'Plymouth', 'Pocahontas', 'Polk', 'Pottawattamie', 'Poweshiek', 'Ringgold', 'Sac', 'Scott', 'Shelby', 'Sioux', 'Story', 'Tama', 'Taylor', 'Union', 'Van Buren', 'Wapello', 'Warren', 'Washington', 'Wayne', 'Webster', 'Winnebago', 'Winneshiek', 'Woodbury', 'Worth', 'Wright'], dtype='object')
dummy_counties = dummy_counties.drop('Polk', axis=1)
variables = variables.drop('sale_dollars', axis=1)
sales_county = pd.concat([variables, dummy_counties], axis=1)
sales_county.head()
state_bottle_retail | bottles_sold | Adair | Adams | Allamakee | Appanoose | Audubon | Benton | Black Hawk | Boone | ... | Wapello | Warren | Washington | Wayne | Webster | Winnebago | Winneshiek | Woodbury | Worth | Wright | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 6.75 | 12 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
1 | 20.63 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
2 | 18.89 | 24 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
3 | 14.25 | 6 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
4 | 10.80 | 12 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
5 rows × 100 columns
dummy_city = dummy_city.drop('DES MOINES', axis=1)
dummy_zip = dummy_zip.drop('50010', axis=1)
sales_city = pd.concat([variables, dummy_city], axis=1)
sales_city.head()
state_bottle_retail | bottles_sold | ACKLEY | ADAIR | ADEL | AFTON | AKRON | ALBIA | ALDEN | ALGONA | ... | WEST UNION | WHEATLAND | WILLIAMSBURG | WILTON | WINDSOR HEIGHTS | WINTERSET | WINTHROP | WOODBINE | WOODWARD | ZWINGLE | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 6.75 | 12 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
1 | 20.63 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
2 | 18.89 | 24 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
3 | 14.25 | 6 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
4 | 10.80 | 12 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
5 rows × 383 columns
sales_zip = pd.concat([variables, dummy_zip], axis=1)
sales_zip.head()
state_bottle_retail | bottles_sold | 50002 | 50003 | 50006 | 50009 | 50014 | 50020 | 50021 | 50022 | ... | 52777 | 52778 | 52801 | 52802 | 52803 | 52804 | 52806 | 52807 | 56201 | 712-2 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 6.75 | 12 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
1 | 20.63 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
2 | 18.89 | 24 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
3 | 14.25 | 6 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
4 | 10.80 | 12 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
5 rows × 413 columns
y=sales["sale_dollars"]
sales_county = sm.add_constant(sales_county)
X_county = sales_county
sales_city = sm.add_constant(sales_city)
X_city = sales_city
sales_zip = sm.add_constant(sales_zip)
X_zip = sales_zip
# Note the argument order
model_county = sm.OLS(y, X_county).fit() ## sm.OLS(output, input)
predictions_county = model_county.predict(X_county)
# Note the argument order
model_city = sm.OLS(y, X_city).fit() ## sm.OLS(output, input)
predictions_city = model_city.predict(X_city)
# Note the argument order
model_zip = sm.OLS(y, X_zip).fit() ## sm.OLS(output, input)
predictions_zip = model_zip.predict(X_zip)
# County statistics
model_county.summary()
Dep. Variable: | sale_dollars | R-squared: | 0.718 |
---|---|---|---|
Model: | OLS | Adj. R-squared: | 0.718 |
Method: | Least Squares | F-statistic: | 6854. |
Date: | Sun, 22 Oct 2017 | Prob (F-statistic): | 0.00 |
Time: | 22:31:25 | Log-Likelihood: | -1.8135e+06 |
No. Observations: | 269258 | AIC: | 3.627e+06 |
Df Residuals: | 269157 | BIC: | 3.628e+06 |
Df Model: | 100 | ||
Covariance Type: | nonrobust |
coef | std err | t | P>|t| | [0.025 | 0.975] | |
---|---|---|---|---|---|---|
const | -103.7964 | 1.105 | -93.925 | 0.000 | -105.962 | -101.630 |
state_bottle_retail | 6.8393 | 0.037 | 183.278 | 0.000 | 6.766 | 6.912 |
bottles_sold | 13.3495 | 0.016 | 813.993 | 0.000 | 13.317 | 13.382 |
Adair | -3.0402 | 8.478 | -0.359 | 0.720 | -19.657 | 13.577 |
Adams | -1.8369 | 13.347 | -0.138 | 0.891 | -27.996 | 24.322 |
Allamakee | 0.9446 | 6.371 | 0.148 | 0.882 | -11.542 | 13.431 |
Appanoose | -4.2531 | 6.383 | -0.666 | 0.505 | -16.763 | 8.257 |
Audubon | 1.0510 | 13.550 | 0.078 | 0.938 | -25.506 | 27.608 |
Benton | 1.6322 | 6.578 | 0.248 | 0.804 | -11.261 | 14.525 |
Black Hawk | -17.3436 | 1.900 | -9.129 | 0.000 | -21.067 | -13.620 |
Boone | 5.4154 | 4.534 | 1.194 | 0.232 | -3.472 | 14.302 |
Bremer | 3.3418 | 4.401 | 0.759 | 0.448 | -5.285 | 11.969 |
Buchanan | 1.5694 | 5.172 | 0.303 | 0.762 | -8.567 | 11.706 |
Buena Vista | -2.2199 | 4.002 | -0.555 | 0.579 | -10.063 | 5.623 |
Butler | 4.1962 | 10.200 | 0.411 | 0.681 | -15.796 | 24.188 |
Calhoun | 8.1722 | 9.935 | 0.823 | 0.411 | -11.299 | 27.644 |
Carroll | 18.1781 | 4.749 | 3.828 | 0.000 | 8.870 | 27.486 |
Cass | 2.6457 | 5.976 | 0.443 | 0.658 | -9.066 | 14.358 |
Cedar | 7.5866 | 6.345 | 1.196 | 0.232 | -4.850 | 20.023 |
Cerro Gordo | -4.8350 | 2.715 | -1.781 | 0.075 | -10.157 | 0.487 |
Cherokee | -12.0638 | 6.939 | -1.739 | 0.082 | -25.664 | 1.536 |
Chickasaw | 7.7258 | 9.500 | 0.813 | 0.416 | -10.894 | 26.346 |
Clarke | 12.8217 | 7.786 | 1.647 | 0.100 | -2.439 | 28.082 |
Clay | 1.3912 | 4.743 | 0.293 | 0.769 | -7.904 | 10.687 |
Clayton | 0.9740 | 5.812 | 0.168 | 0.867 | -10.418 | 12.366 |
Clinton | -0.7251 | 3.532 | -0.205 | 0.837 | -7.648 | 6.198 |
Crawford | 3.9955 | 6.160 | 0.649 | 0.517 | -8.079 | 16.070 |
Dallas | 53.1908 | 4.021 | 13.227 | 0.000 | 45.309 | 61.073 |
Davis | 4.6525 | 14.325 | 0.325 | 0.745 | -23.424 | 32.729 |
Decatur | -2.2049 | 13.670 | -0.161 | 0.872 | -28.998 | 24.588 |
Delaware | 3.1519 | 7.758 | 0.406 | 0.685 | -12.054 | 18.358 |
Des Moines | -5.8023 | 3.318 | -1.749 | 0.080 | -12.306 | 0.702 |
Dickinson | 5.0831 | 3.608 | 1.409 | 0.159 | -1.988 | 12.154 |
Dubuque | 2.6042 | 2.492 | 1.045 | 0.296 | -2.279 | 7.488 |
Emmet | -0.1118 | 7.309 | -0.015 | 0.988 | -14.438 | 14.215 |
Fayette | 2.7125 | 6.097 | 0.445 | 0.656 | -9.237 | 14.662 |
Floyd | 1.2147 | 6.506 | 0.187 | 0.852 | -11.537 | 13.966 |
Franklin | -3.3577 | 7.732 | -0.434 | 0.664 | -18.511 | 11.796 |
Fremont | -30.1678 | 39.207 | -0.769 | 0.442 | -107.013 | 46.677 |
Greene | 2.6740 | 7.893 | 0.339 | 0.735 | -12.797 | 18.145 |
Grundy | 4.1345 | 8.611 | 0.480 | 0.631 | -12.743 | 21.012 |
Guthrie | 0.1955 | 9.787 | 0.020 | 0.984 | -18.986 | 19.377 |
Hamilton | 3.8102 | 6.121 | 0.622 | 0.534 | -8.187 | 15.808 |
Hancock | -8.2983 | 10.730 | -0.773 | 0.439 | -29.328 | 12.732 |
Hardin | 8.7106 | 5.090 | 1.711 | 0.087 | -1.267 | 18.688 |
Harrison | 4.3795 | 6.214 | 0.705 | 0.481 | -7.799 | 16.558 |
Henry | 8.5531 | 6.210 | 1.377 | 0.168 | -3.618 | 20.724 |
Howard | 12.8337 | 8.325 | 1.542 | 0.123 | -3.482 | 29.150 |
Humboldt | -1.5956 | 8.450 | -0.189 | 0.850 | -18.157 | 14.966 |
Ida | 5.6097 | 8.141 | 0.689 | 0.491 | -10.347 | 21.566 |
Iowa | 13.5064 | 5.634 | 2.397 | 0.017 | 2.464 | 24.549 |
Jackson | 4.9213 | 5.199 | 0.947 | 0.344 | -5.268 | 15.111 |
Jasper | 1.2888 | 3.940 | 0.327 | 0.744 | -6.434 | 9.012 |
Jefferson | -5.1507 | 7.229 | -0.713 | 0.476 | -19.318 | 9.017 |
Johnson | 6.7503 | 2.000 | 3.376 | 0.001 | 2.831 | 10.670 |
Jones | 7.3444 | 4.799 | 1.530 | 0.126 | -2.062 | 16.751 |
Keokuk | 5.7851 | 11.037 | 0.524 | 0.600 | -15.846 | 27.416 |
Kossuth | 2.0699 | 5.061 | 0.409 | 0.683 | -7.850 | 11.989 |
Lee | 5.3702 | 3.653 | 1.470 | 0.142 | -1.790 | 12.530 |
Linn | 0.4465 | 1.618 | 0.276 | 0.783 | -2.724 | 3.617 |
Louisa | -36.1366 | 9.304 | -3.884 | 0.000 | -54.372 | -17.901 |
Lucas | 5.4799 | 9.390 | 0.584 | 0.560 | -12.925 | 23.885 |
Lyon | 0.9456 | 6.028 | 0.157 | 0.875 | -10.870 | 12.761 |
Madison | -1.6523 | 6.774 | -0.244 | 0.807 | -14.930 | 11.625 |
Mahaska | -4.9971 | 5.981 | -0.836 | 0.403 | -16.719 | 6.725 |
Marion | 5.0274 | 4.099 | 1.226 | 0.220 | -3.007 | 13.062 |
Marshall | 1.8498 | 3.841 | 0.482 | 0.630 | -5.678 | 9.378 |
Mills | -20.2372 | 9.092 | -2.226 | 0.026 | -38.057 | -2.417 |
Mitchell | 0.5647 | 6.476 | 0.087 | 0.931 | -12.127 | 13.257 |
Monona | -0.3446 | 5.824 | -0.059 | 0.953 | -11.759 | 11.070 |
Monroe | 5.6772 | 10.895 | 0.521 | 0.602 | -15.676 | 27.031 |
Montgomery | -4.7861 | 7.189 | -0.666 | 0.506 | -18.877 | 9.305 |
Muscatine | 3.4120 | 3.360 | 1.015 | 0.310 | -3.174 | 9.998 |
O'Brien | 8.7803 | 4.997 | 1.757 | 0.079 | -1.014 | 18.575 |
Osceola | 2.5360 | 10.910 | 0.232 | 0.816 | -18.848 | 23.920 |
Page | 5.1122 | 5.998 | 0.852 | 0.394 | -6.644 | 16.868 |
Palo Alto | 0.0944 | 6.258 | 0.015 | 0.988 | -12.171 | 12.360 |
Plymouth | 3.0856 | 5.232 | 0.590 | 0.555 | -7.169 | 13.340 |
Pocahontas | 4.5471 | 8.937 | 0.509 | 0.611 | -12.969 | 22.063 |
Pottawattamie | 7.6726 | 2.327 | 3.297 | 0.001 | 3.112 | 12.233 |
Poweshiek | 4.0197 | 4.553 | 0.883 | 0.377 | -4.905 | 12.944 |
Ringgold | 12.4488 | 14.395 | 0.865 | 0.387 | -15.766 | 40.663 |
Sac | -4.5672 | 6.874 | -0.664 | 0.506 | -18.041 | 8.906 |
Scott | -11.6747 | 1.828 | -6.386 | 0.000 | -15.258 | -8.091 |
Shelby | 11.7838 | 7.975 | 1.478 | 0.140 | -3.847 | 27.415 |
Sioux | 18.1440 | 5.664 | 3.203 | 0.001 | 7.042 | 29.246 |
Story | 3.9777 | 2.343 | 1.698 | 0.090 | -0.614 | 8.569 |
Tama | 0.9065 | 6.562 | 0.138 | 0.890 | -11.955 | 13.768 |
Taylor | 3.8606 | 11.835 | 0.326 | 0.744 | -19.335 | 27.057 |
Union | 5.7824 | 6.286 | 0.920 | 0.358 | -6.538 | 18.102 |
Van Buren | 10.2299 | 13.045 | 0.784 | 0.433 | -15.338 | 35.797 |
Wapello | 9.0755 | 3.555 | 2.553 | 0.011 | 2.108 | 16.043 |
Warren | -0.0940 | 4.209 | -0.022 | 0.982 | -8.343 | 8.155 |
Washington | -2.8096 | 5.265 | -0.534 | 0.594 | -13.128 | 7.509 |
Wayne | -8.7035 | 16.128 | -0.540 | 0.589 | -40.314 | 22.907 |
Webster | -5.2554 | 3.747 | -1.402 | 0.161 | -12.600 | 2.089 |
Winnebago | -3.8053 | 6.669 | -0.571 | 0.568 | -16.876 | 9.265 |
Winneshiek | -1.1398 | 5.713 | -0.200 | 0.842 | -12.337 | 10.057 |
Woodbury | 0.3815 | 2.388 | 0.160 | 0.873 | -4.300 | 5.063 |
Worth | 8.8589 | 10.394 | 0.852 | 0.394 | -11.514 | 29.231 |
Wright | -0.8212 | 7.916 | -0.104 | 0.917 | -16.337 | 14.695 |
Omnibus: | 366597.951 | Durbin-Watson: | 2.001 |
---|---|---|---|
Prob(Omnibus): | 0.000 | Jarque-Bera (JB): | 66620643079.357 |
Skew: | 5.938 | Prob(JB): | 0.00 |
Kurtosis: | 2439.804 | Cond. No. | 2.68e+03 |
# City Statistics
model_city.summary()
Dep. Variable: | sale_dollars | R-squared: | 0.718 |
---|---|---|---|
Model: | OLS | Adj. R-squared: | 0.718 |
Method: | Least Squares | F-statistic: | 1791. |
Date: | Sun, 22 Oct 2017 | Prob (F-statistic): | 0.00 |
Time: | 22:32:18 | Log-Likelihood: | -1.8133e+06 |
No. Observations: | 269258 | AIC: | 3.627e+06 |
Df Residuals: | 268874 | BIC: | 3.631e+06 |
Df Model: | 383 | ||
Covariance Type: | nonrobust |
coef | std err | t | P>|t| | [0.025 | 0.975] | |
---|---|---|---|---|---|---|
const | -111.2714 | 1.465 | -75.970 | 0.000 | -114.142 | -108.401 |
state_bottle_retail | 6.8100 | 0.037 | 181.877 | 0.000 | 6.737 | 6.883 |
bottles_sold | 13.3641 | 0.016 | 812.038 | 0.000 | 13.332 | 13.396 |
ACKLEY | 12.6694 | 21.388 | 0.592 | 0.554 | -29.250 | 54.589 |
ADAIR | -16.2146 | 31.082 | -0.522 | 0.602 | -77.134 | 44.705 |
ADEL | 21.9562 | 11.496 | 1.910 | 0.056 | -0.576 | 44.488 |
AFTON | 9.7649 | 58.798 | 0.166 | 0.868 | -105.478 | 125.007 |
AKRON | 7.8456 | 24.550 | 0.320 | 0.749 | -40.273 | 55.964 |
ALBIA | 13.9177 | 11.107 | 1.253 | 0.210 | -7.852 | 35.688 |
ALDEN | 4.5391 | 18.484 | 0.246 | 0.806 | -31.689 | 40.768 |
ALGONA | 10.4988 | 6.497 | 1.616 | 0.106 | -2.236 | 23.233 |
ALLISON | 16.4433 | 22.391 | 0.734 | 0.463 | -27.443 | 60.329 |
ALTA | -16.2747 | 54.439 | -0.299 | 0.765 | -122.973 | 90.424 |
ALTOONA | 9.2230 | 4.634 | 1.990 | 0.047 | 0.140 | 18.306 |
AMES | 12.1338 | 2.696 | 4.501 | 0.000 | 6.850 | 17.418 |
ANAMOSA | 16.3067 | 8.538 | 1.910 | 0.056 | -0.427 | 33.040 |
ANITA | 10.1520 | 16.791 | 0.605 | 0.545 | -22.759 | 43.063 |
ANKENY | 17.5795 | 3.219 | 5.461 | 0.000 | 11.270 | 23.889 |
ANTHON | 13.1279 | 15.957 | 0.823 | 0.411 | -18.147 | 44.403 |
ARLINGTON | 28.7329 | 23.397 | 1.228 | 0.219 | -17.125 | 74.591 |
ARMSTRONG | -28.7348 | 49.406 | -0.582 | 0.561 | -125.568 | 68.099 |
ARNOLD'S PARK | 1.3154 | 13.158 | 0.100 | 0.920 | -24.475 | 27.106 |
ARNOLDS PARK | 11.8707 | 9.231 | 1.286 | 0.198 | -6.221 | 29.963 |
ATLANTIC | 10.5251 | 6.462 | 1.629 | 0.103 | -2.139 | 23.190 |
AUDUBON | 12.3132 | 14.284 | 0.862 | 0.389 | -15.684 | 40.310 |
AURELIA | -6.9803 | 61.411 | -0.114 | 0.910 | -127.344 | 113.384 |
AVOCA | 11.3464 | 9.618 | 1.180 | 0.238 | -7.505 | 30.197 |
BALDWIN | -19.7364 | 39.957 | -0.494 | 0.621 | -98.052 | 58.579 |
BANCROFT | 8.9044 | 10.655 | 0.836 | 0.403 | -11.980 | 29.789 |
BAXTER | 20.9895 | 26.772 | 0.784 | 0.433 | -31.482 | 73.461 |
BEDFORD | 17.5580 | 16.306 | 1.077 | 0.282 | -14.402 | 49.518 |
BELLE PLAINE | 4.5213 | 10.096 | 0.448 | 0.654 | -15.267 | 24.309 |
BELLEVUE | 7.7428 | 13.266 | 0.584 | 0.559 | -18.258 | 33.744 |
BELMOND | 0.3506 | 15.194 | 0.023 | 0.982 | -29.429 | 30.130 |
BETTENDORF | 16.6460 | 3.602 | 4.621 | 0.000 | 9.586 | 23.706 |
BEVINGTON | 14.0133 | 23.709 | 0.591 | 0.554 | -32.456 | 60.483 |
BLOOMFIELD | 12.4578 | 14.354 | 0.868 | 0.385 | -15.676 | 40.592 |
BLUE GRASS | 23.7859 | 9.969 | 2.386 | 0.017 | 4.247 | 43.325 |
BONDURANT | 18.6969 | 10.772 | 1.736 | 0.083 | -2.415 | 39.809 |
BOONE | 14.2969 | 5.528 | 2.586 | 0.010 | 3.462 | 25.132 |
BRITT | 6.4683 | 16.306 | 0.397 | 0.692 | -25.491 | 38.427 |
BROOKLYN | 8.1753 | 14.681 | 0.557 | 0.578 | -20.598 | 36.949 |
BUFFALO | -17.0115 | 33.965 | -0.501 | 0.616 | -83.581 | 49.558 |
BUFFALO CENTER | 10.2996 | 16.625 | 0.620 | 0.536 | -22.285 | 42.884 |
BURLINGTON | -0.6583 | 3.870 | -0.170 | 0.865 | -8.244 | 6.928 |
BUSSEY | -140.3028 | 50.926 | -2.755 | 0.006 | -240.116 | -40.489 |
CAMANCHE | -9.6778 | 17.323 | -0.559 | 0.576 | -43.630 | 24.274 |
CAMBRIDGE | 21.5206 | 28.546 | 0.754 | 0.451 | -34.428 | 77.469 |
CARLISLE | 7.7813 | 15.194 | 0.512 | 0.609 | -21.998 | 37.561 |
CARROLL | 30.8795 | 5.463 | 5.652 | 0.000 | 20.171 | 41.588 |
CARTER LAKE | -45.0795 | 15.909 | -2.834 | 0.005 | -76.260 | -13.899 |
CASCADE | 14.8590 | 16.679 | 0.891 | 0.373 | -17.832 | 47.550 |
CASEY | -17.8852 | 33.503 | -0.534 | 0.593 | -83.550 | 47.780 |
CEDAR FALLS | 8.2666 | 3.003 | 2.753 | 0.006 | 2.381 | 14.152 |
CEDAR RAPIDS | 8.5375 | 1.994 | 4.282 | 0.000 | 4.630 | 12.445 |
CENTER POINT | 18.8264 | 15.451 | 1.218 | 0.223 | -11.457 | 49.110 |
CENTERVILLE | 3.2231 | 6.662 | 0.484 | 0.629 | -9.835 | 16.281 |
CENTRAL CITY | -7.2243 | 17.774 | -0.406 | 0.684 | -42.061 | 27.612 |
CHARITON | 13.2592 | 9.437 | 1.405 | 0.160 | -5.238 | 31.756 |
CHARLES CITY | 6.2152 | 7.217 | 0.861 | 0.389 | -7.929 | 20.360 |
CHEROKEE | -6.3219 | 7.370 | -0.858 | 0.391 | -20.767 | 8.123 |
CLARINDA | 9.7266 | 9.115 | 1.067 | 0.286 | -8.138 | 27.591 |
CLARION | 8.2250 | 15.861 | 0.519 | 0.604 | -22.862 | 39.312 |
CLARKSVILLE | 9.4002 | 25.101 | 0.375 | 0.708 | -39.796 | 58.597 |
CLEAR LAKE | 8.4477 | 4.659 | 1.813 | 0.070 | -0.684 | 17.579 |
CLINTON | 6.7116 | 3.904 | 1.719 | 0.086 | -0.940 | 14.363 |
CLIVE | -1.0321 | 6.798 | -0.152 | 0.879 | -14.356 | 12.291 |
COLFAX | 12.9306 | 14.756 | 0.876 | 0.381 | -15.991 | 41.853 |
COLO | -7.7096 | 39.211 | -0.197 | 0.844 | -84.562 | 69.143 |
COLUMBUS JUNCTION | -56.5840 | 12.031 | -4.703 | 0.000 | -80.165 | -33.003 |
CONRAD | 10.9844 | 20.509 | 0.536 | 0.592 | -29.213 | 51.182 |
COON RAPIDS | 2.6825 | 18.793 | 0.143 | 0.886 | -34.151 | 39.516 |
CORALVILLE | 31.6748 | 3.714 | 8.528 | 0.000 | 24.395 | 38.954 |
CORNING | 9.1684 | 9.756 | 0.940 | 0.347 | -9.953 | 28.289 |
CORWITH | 23.9953 | 37.201 | 0.645 | 0.519 | -48.918 | 96.909 |
CORYDON | 1.7810 | 17.909 | 0.099 | 0.921 | -33.320 | 36.883 |
COUNCIL BLUFFS | 17.3579 | 2.631 | 6.599 | 0.000 | 12.202 | 22.514 |
CRESCENT | 8.6032 | 39.957 | 0.215 | 0.830 | -69.712 | 86.918 |
CRESCO | 19.7904 | 8.722 | 2.269 | 0.023 | 2.696 | 36.885 |
CRESTON | 13.6301 | 6.391 | 2.133 | 0.033 | 1.103 | 26.157 |
Carroll | 244.1771 | 203.635 | 1.199 | 0.230 | -154.941 | 643.295 |
Cumming | -127.8339 | 83.144 | -1.538 | 0.124 | -290.793 | 35.125 |
DAKOTA CITY | -89.7085 | 40.748 | -2.202 | 0.028 | -169.574 | -9.843 |
DANVILLE | -15.6578 | 52.594 | -0.298 | 0.766 | -118.741 | 87.425 |
DAVENPORT | -12.5572 | 2.318 | -5.417 | 0.000 | -17.101 | -8.014 |
DAYTON | 18.7383 | 21.158 | 0.886 | 0.376 | -22.730 | 60.206 |
DE SOTO | 7.0988 | 17.774 | 0.399 | 0.690 | -27.737 | 41.935 |
DECORAH | 4.9740 | 6.163 | 0.807 | 0.420 | -7.105 | 17.053 |
DELAWARE | 0.4450 | 46.735 | 0.010 | 0.992 | -91.154 | 92.044 |
DELHI | 21.3423 | 61.412 | 0.348 | 0.728 | -99.023 | 141.707 |
DELMAR | -6.1927 | 37.837 | -0.164 | 0.870 | -80.351 | 67.966 |
DENISON | 13.6400 | 6.879 | 1.983 | 0.047 | 0.157 | 27.123 |
DENVER | 13.7529 | 18.335 | 0.750 | 0.453 | -22.183 | 49.689 |
DEWITT | 23.1845 | 14.424 | 1.607 | 0.108 | -5.086 | 51.455 |
DONNELLSON | 10.5607 | 49.405 | 0.214 | 0.831 | -86.272 | 107.394 |
DOWS | -9.0705 | 56.493 | -0.161 | 0.872 | -119.794 | 101.653 |
DUBUQUE | 9.8759 | 2.795 | 3.534 | 0.000 | 4.398 | 15.353 |
DUMONT | 11.5202 | 25.896 | 0.445 | 0.656 | -39.234 | 62.275 |
DUNLAP | 8.8208 | 15.030 | 0.587 | 0.557 | -20.638 | 38.280 |
DURANT | 17.2065 | 13.026 | 1.321 | 0.187 | -8.324 | 42.737 |
DYERSVILLE | 17.7488 | 8.953 | 1.982 | 0.047 | 0.202 | 35.296 |
DYSART | 6.3149 | 33.503 | 0.188 | 0.850 | -59.350 | 71.980 |
Des Moines | -7.9060 | 20.719 | -0.382 | 0.703 | -48.514 | 32.702 |
Dubuque | -11.0442 | 26.106 | -0.423 | 0.672 | -62.211 | 40.123 |
EAGLE GROVE | 14.9995 | 11.910 | 1.259 | 0.208 | -8.344 | 38.343 |
EARLHAM | 11.3089 | 33.503 | 0.338 | 0.736 | -54.356 | 76.974 |
EARLY | 5.7175 | 36.597 | 0.156 | 0.876 | -66.012 | 77.447 |
EDDYVILLE | 1.9253 | 36.022 | 0.053 | 0.957 | -68.676 | 72.526 |
EDGEWOOD | 18.5049 | 21.999 | 0.841 | 0.400 | -24.612 | 61.621 |
ELDON | -7.9854 | 32.634 | -0.245 | 0.807 | -71.947 | 55.976 |
ELDORA | 14.5795 | 11.532 | 1.264 | 0.206 | -8.023 | 37.182 |
ELDRIDGE | 0.8908 | 7.855 | 0.113 | 0.910 | -14.504 | 16.285 |
ELKADER | 13.2149 | 18.560 | 0.712 | 0.476 | -23.162 | 49.591 |
ELLSWORTH | 21.6375 | 21.272 | 1.017 | 0.309 | -20.055 | 63.330 |
ELMA | 30.0380 | 29.422 | 1.021 | 0.307 | -27.627 | 87.703 |
ELY | 0.4269 | 31.082 | 0.014 | 0.989 | -60.492 | 61.346 |
EMMETSBURG | 7.5235 | 6.775 | 1.111 | 0.267 | -5.754 | 20.801 |
ESTHERVILLE | 8.5041 | 7.449 | 1.142 | 0.254 | -6.095 | 23.104 |
EVANSDALE | -10.0774 | 9.370 | -1.075 | 0.282 | -28.443 | 8.288 |
EVERLY | 4.7498 | 76.976 | 0.062 | 0.951 | -146.122 | 155.621 |
EXIRA | -23.9271 | 43.434 | -0.551 | 0.582 | -109.057 | 61.203 |
FAIRBANK | 5.2703 | 21.998 | 0.240 | 0.811 | -37.846 | 48.387 |
FAIRFAX | -6.9754 | 33.965 | -0.205 | 0.837 | -73.545 | 59.595 |
FAIRFIELD | 2.6637 | 7.290 | 0.365 | 0.715 | -11.625 | 16.952 |
FARLEY | -6.3658 | 37.837 | -0.168 | 0.866 | -80.525 | 67.794 |
FARMINGTON | 12.0277 | 36.022 | 0.334 | 0.738 | -58.574 | 82.629 |
FAYETTE | -4.8607 | 24.550 | -0.198 | 0.843 | -52.978 | 43.257 |
FLOYD | 26.8015 | 17.773 | 1.508 | 0.132 | -8.034 | 61.637 |
FONDA | -16.0151 | 83.142 | -0.193 | 0.847 | -178.972 | 146.942 |
FONTANELLE | 5.4821 | 25.896 | 0.212 | 0.832 | -45.273 | 56.237 |
FOREST CITY | 2.5327 | 8.460 | 0.299 | 0.765 | -14.048 | 19.114 |
FORT ATKINSON | 18.6753 | 16.154 | 1.156 | 0.248 | -12.985 | 50.336 |
FORT DODGE | 6.7896 | 3.964 | 1.713 | 0.087 | -0.979 | 14.558 |
FORT MADISON | 4.3457 | 5.635 | 0.771 | 0.441 | -6.698 | 15.390 |
FREDERICKSBURG | 20.6748 | 23.095 | 0.895 | 0.371 | -24.591 | 65.941 |
GARNER | -11.0150 | 15.407 | -0.715 | 0.475 | -41.211 | 19.181 |
GEORGE | 14.2376 | 18.637 | 0.764 | 0.445 | -22.290 | 50.765 |
GILBERTVILLE | 25.6181 | 83.142 | 0.308 | 0.758 | -137.339 | 188.575 |
GILMORE CITY | -33.7885 | 50.925 | -0.663 | 0.507 | -133.601 | 66.024 |
GLADBROOK | 22.4710 | 23.709 | 0.948 | 0.343 | -23.999 | 68.941 |
GLENWOOD | -2.3770 | 10.889 | -0.218 | 0.827 | -23.719 | 18.965 |
GLIDDEN | 16.4634 | 28.003 | 0.588 | 0.557 | -38.421 | 71.348 |
GOLDFIELD | -73.1030 | 52.594 | -1.390 | 0.165 | -176.186 | 29.980 |
GOWRIE | 2.4441 | 14.533 | 0.168 | 0.866 | -26.040 | 30.928 |
GRAETTINGER | 11.3875 | 20.719 | 0.550 | 0.583 | -29.220 | 51.995 |
GRAND JUNCTION | -23.8708 | 38.505 | -0.620 | 0.535 | -99.340 | 51.599 |
GRAND MOUND | 11.7441 | 25.489 | 0.461 | 0.645 | -38.213 | 61.701 |
GRANGER | 2.4954 | 25.896 | 0.096 | 0.923 | -48.259 | 53.250 |
GREENE | 10.9579 | 19.823 | 0.553 | 0.580 | -27.896 | 49.811 |
GREENFIELD | 4.3470 | 14.680 | 0.296 | 0.767 | -24.426 | 33.120 |
GRIMES | 10.5076 | 5.365 | 1.958 | 0.050 | -0.008 | 21.023 |
GRINNELL | 12.1846 | 5.451 | 2.235 | 0.025 | 1.500 | 22.869 |
GRISWOLD | 2.1828 | 91.076 | 0.024 | 0.981 | -176.323 | 180.689 |
GRUNDY CENTER | 14.6975 | 10.109 | 1.454 | 0.146 | -5.116 | 34.510 |
GUTHRIE CENTER | 17.6120 | 17.386 | 1.013 | 0.311 | -16.463 | 51.687 |
GUTTENBERG | 4.9218 | 12.897 | 0.382 | 0.703 | -20.355 | 30.199 |
GUTTENBURG | 20.4662 | 20.406 | 1.003 | 0.316 | -19.530 | 60.462 |
HAMBURG | -22.5931 | 39.211 | -0.576 | 0.564 | -99.446 | 54.260 |
HAMPTON | 5.1508 | 7.985 | 0.645 | 0.519 | -10.499 | 20.801 |
HARLAN | 19.5010 | 8.031 | 2.428 | 0.015 | 3.761 | 35.241 |
HARPERS FERRY | -21.1814 | 29.421 | -0.720 | 0.472 | -78.847 | 36.484 |
HARTLEY | 21.5626 | 11.891 | 1.813 | 0.070 | -1.744 | 44.869 |
HAWARDEN | 6.4730 | 15.539 | 0.417 | 0.677 | -23.982 | 36.929 |
HAZLETON | -138.5507 | 36.598 | -3.786 | 0.000 | -210.281 | -66.820 |
HIAWATHA | -24.0826 | 10.358 | -2.325 | 0.020 | -44.384 | -3.781 |
HOLSTEIN | 12.6384 | 11.461 | 1.103 | 0.270 | -9.825 | 35.102 |
HOLY CROSS | 17.5875 | 26.322 | 0.668 | 0.504 | -34.003 | 69.178 |
HOSPERS | 23.5566 | 39.211 | 0.601 | 0.548 | -53.297 | 100.410 |
HUBBARD | 21.4838 | 23.244 | 0.924 | 0.355 | -24.075 | 67.042 |
HUDSON | 5.3643 | 32.224 | 0.166 | 0.868 | -57.794 | 68.523 |
HUMBOLDT | 11.7142 | 8.808 | 1.330 | 0.184 | -5.548 | 28.977 |
HUMESTON | -12.5408 | 37.202 | -0.337 | 0.736 | -85.455 | 60.374 |
HUXLEY | 13.0762 | 18.714 | 0.699 | 0.485 | -23.603 | 49.755 |
IDA GROVE | 14.2222 | 11.568 | 1.229 | 0.219 | -8.450 | 36.895 |
INDEPENDENCE | 16.3160 | 6.928 | 2.355 | 0.019 | 2.738 | 29.894 |
INDIANOLA | 3.8883 | 5.030 | 0.773 | 0.439 | -5.970 | 13.747 |
INWOOD | 15.1291 | 13.436 | 1.126 | 0.260 | -11.204 | 41.463 |
IOWA CITY | 8.3816 | 2.642 | 3.172 | 0.002 | 3.203 | 13.560 |
IOWA FALLS | 18.5881 | 7.904 | 2.352 | 0.019 | 3.097 | 34.080 |
IRETON | 15.8366 | 33.964 | 0.466 | 0.641 | -50.733 | 82.406 |
Inwood | 21.0536 | 36.597 | 0.575 | 0.565 | -50.676 | 92.783 |
JEFFERSON | 12.2832 | 8.272 | 1.485 | 0.138 | -3.930 | 28.497 |
JESUP | 9.8436 | 15.195 | 0.648 | 0.517 | -19.937 | 39.625 |
JEWELL | 22.1007 | 21.999 | 1.005 | 0.315 | -21.017 | 65.218 |
JOHNSTON | 16.1114 | 4.602 | 3.501 | 0.000 | 7.091 | 25.132 |
KELLOG | 10.5823 | 45.553 | 0.232 | 0.816 | -78.700 | 99.865 |
KELLOGG | 5.9998 | 22.665 | 0.265 | 0.791 | -38.423 | 50.423 |
KEOKUK | 20.4061 | 5.105 | 3.997 | 0.000 | 10.400 | 30.412 |
KEOSAUQUA | 18.9282 | 14.016 | 1.350 | 0.177 | -8.543 | 46.399 |
KEOTA | 4.9876 | 30.728 | 0.162 | 0.871 | -55.238 | 65.213 |
KINGSLEY | 1.6400 | 28.828 | 0.057 | 0.955 | -54.863 | 58.143 |
KNOXVILLE | 12.4646 | 5.619 | 2.218 | 0.027 | 1.452 | 23.477 |
LA PORTE CITY | 16.4268 | 12.999 | 1.264 | 0.206 | -9.052 | 41.905 |
LAKE CITY | 10.7820 | 17.448 | 0.618 | 0.537 | -23.416 | 44.980 |
LAKE MILLS | 3.5394 | 14.250 | 0.248 | 0.804 | -24.390 | 31.469 |
LAKE PARK | 16.7145 | 35.472 | 0.471 | 0.637 | -52.810 | 86.239 |
LAKE VIEW | 0.8195 | 14.680 | 0.056 | 0.955 | -27.953 | 29.592 |
LAMONI | 12.2978 | 19.824 | 0.620 | 0.535 | -26.556 | 51.151 |
LANSING | 10.4263 | 16.680 | 0.625 | 0.532 | -22.266 | 43.118 |
LARCHWOOD | 8.2965 | 11.755 | 0.706 | 0.480 | -14.742 | 31.336 |
LATIMER | 17.8635 | 72.006 | 0.248 | 0.804 | -123.267 | 158.994 |
LAURENS | 10.1463 | 14.911 | 0.680 | 0.496 | -19.079 | 39.372 |
LAWLER | 44.4770 | 31.829 | 1.397 | 0.162 | -17.908 | 106.862 |
LE CLAIRE | 3.4458 | 12.397 | 0.278 | 0.781 | -20.851 | 27.743 |
LE GRAND | 15.9082 | 12.243 | 1.299 | 0.194 | -8.088 | 39.904 |
LE MARS | 4.6337 | 9.990 | 0.464 | 0.643 | -14.945 | 24.213 |
LECLAIRE | 34.4375 | 11.496 | 2.996 | 0.003 | 11.906 | 56.969 |
LEMARS | 14.0501 | 6.988 | 2.011 | 0.044 | 0.353 | 27.747 |
LENOX | 5.1152 | 17.201 | 0.297 | 0.766 | -28.598 | 38.828 |
LEON | -0.6040 | 18.872 | -0.032 | 0.974 | -37.593 | 36.386 |
LISBON | 12.1819 | 17.323 | 0.703 | 0.482 | -21.771 | 46.134 |
LOGAN | 17.3734 | 20.109 | 0.864 | 0.388 | -22.039 | 56.786 |
LOHRVILLE | 26.8624 | 117.573 | 0.228 | 0.819 | -203.578 | 257.303 |
LOST NATION | 5.2824 | 35.472 | 0.149 | 0.882 | -64.243 | 74.807 |
LOVILIA | -1.3982 | 61.411 | -0.023 | 0.982 | -121.763 | 118.966 |
MADRID | 15.4158 | 15.237 | 1.012 | 0.312 | -14.447 | 45.279 |
MALVERN | -47.8482 | 37.202 | -1.286 | 0.198 | -120.763 | 25.067 |
MANCHESTER | 11.0398 | 7.984 | 1.383 | 0.167 | -4.608 | 26.688 |
MANLY | -18.0850 | 32.225 | -0.561 | 0.575 | -81.244 | 45.074 |
MANNING | 9.6254 | 12.627 | 0.762 | 0.446 | -15.124 | 34.375 |
MANSON | 18.7842 | 15.584 | 1.205 | 0.228 | -11.760 | 49.328 |
MAPLETON | 9.3938 | 9.854 | 0.953 | 0.340 | -9.920 | 28.708 |
MAQUOKETA | 15.4257 | 5.869 | 2.628 | 0.009 | 3.922 | 26.929 |
MARCUS | 16.8299 | 23.244 | 0.724 | 0.469 | -28.728 | 62.388 |
MARENGO | 27.6627 | 9.956 | 2.779 | 0.005 | 8.150 | 47.175 |
MARION | 12.5049 | 4.296 | 2.911 | 0.004 | 4.086 | 20.924 |
MARSHALLTOWN | 8.9378 | 4.150 | 2.154 | 0.031 | 0.804 | 17.072 |
MARTENSDALE | -6.4502 | 45.552 | -0.142 | 0.887 | -95.732 | 82.831 |
MASON CITY | -0.5532 | 3.439 | -0.161 | 0.872 | -7.294 | 6.188 |
MAXWELL | 16.8655 | 39.957 | 0.422 | 0.673 | -61.450 | 95.181 |
MECHANICSVILLE | -42.9215 | 36.022 | -1.192 | 0.233 | -113.523 | 27.680 |
MEDIAPOLIS | 5.8980 | 11.076 | 0.533 | 0.594 | -15.811 | 27.607 |
MELBOURNE | 18.2901 | 83.142 | 0.220 | 0.826 | -144.666 | 181.246 |
MELCHER-DALLAS | 6.9854 | 15.629 | 0.447 | 0.655 | -23.646 | 37.617 |
MERRILL | 1.0184 | 64.407 | 0.016 | 0.987 | -125.218 | 127.255 |
MILFORD | 15.0328 | 6.742 | 2.230 | 0.026 | 1.819 | 28.247 |
MINDEN | 14.2626 | 67.890 | 0.210 | 0.834 | -118.799 | 147.325 |
MISSOURI VALLEY | 10.5286 | 8.062 | 1.306 | 0.192 | -5.273 | 26.330 |
MONONA | 3.1677 | 9.877 | 0.321 | 0.748 | -16.191 | 22.526 |
MONROE | 9.5374 | 18.560 | 0.514 | 0.607 | -26.840 | 45.914 |
MONTEZUMA | 11.2643 | 11.044 | 1.020 | 0.308 | -10.381 | 32.910 |
MONTICELLO | 14.6337 | 5.830 | 2.510 | 0.012 | 3.208 | 26.060 |
MONTROSE | -14.5274 | 33.061 | -0.439 | 0.660 | -79.325 | 50.271 |
MORAVIA | 7.9187 | 24.913 | 0.318 | 0.751 | -40.911 | 56.748 |
MOUNT AYR | 20.2394 | 14.425 | 1.403 | 0.161 | -8.032 | 48.511 |
MOUNT PLEASANT | 15.2371 | 6.815 | 2.236 | 0.025 | 1.879 | 28.595 |
MOUNT VERNON | 8.7258 | 6.191 | 1.409 | 0.159 | -3.409 | 20.860 |
MT PLEASANT | 21.9054 | 15.583 | 1.406 | 0.160 | -8.637 | 52.448 |
MT VERNON | -35.2418 | 24.036 | -1.466 | 0.143 | -82.351 | 11.867 |
MUSCATINE | 10.5240 | 3.742 | 2.812 | 0.005 | 3.189 | 17.859 |
NASHUA | 11.7494 | 21.998 | 0.534 | 0.593 | -31.367 | 54.866 |
NEOLA | 23.4626 | 25.690 | 0.913 | 0.361 | -26.889 | 73.814 |
NEVADA | 9.0361 | 6.831 | 1.323 | 0.186 | -4.353 | 22.425 |
NEW HAMPTON | 10.6192 | 12.723 | 0.835 | 0.404 | -14.317 | 35.556 |
NEW SHARON | 11.7852 | 38.505 | 0.306 | 0.760 | -63.684 | 87.255 |
NEW VIRGINIA | 17.2334 | 34.446 | 0.500 | 0.617 | -50.279 | 84.746 |
NEWTON | 5.1702 | 4.256 | 1.215 | 0.224 | -3.171 | 13.512 |
NORA SPRINGS | 6.5939 | 30.727 | 0.215 | 0.830 | -53.631 | 66.819 |
NORTH ENGLISH | 20.1190 | 36.022 | 0.559 | 0.576 | -50.484 | 90.721 |
NORTH LIBERTY | -0.5936 | 5.789 | -0.103 | 0.918 | -11.940 | 10.753 |
NORTHWOOD | 16.5553 | 13.919 | 1.189 | 0.234 | -10.726 | 43.837 |
NORWALK | 23.7449 | 9.628 | 2.466 | 0.014 | 4.875 | 42.615 |
Northwood | 27.3407 | 17.841 | 1.532 | 0.125 | -7.626 | 62.308 |
OAKLAND | 3.9275 | 12.821 | 0.306 | 0.759 | -21.202 | 29.057 |
OELWEIN | 8.9083 | 8.398 | 1.061 | 0.289 | -7.551 | 25.368 |
OGDEN | 9.9022 | 18.409 | 0.538 | 0.591 | -26.179 | 45.983 |
OKOBOJI | 13.8150 | 32.224 | 0.429 | 0.668 | -49.344 | 76.974 |
ONAWA | 6.4833 | 7.236 | 0.896 | 0.370 | -7.698 | 20.665 |
ORANGE CITY | 31.1245 | 11.870 | 2.622 | 0.009 | 7.859 | 54.390 |
OSAGE | 13.0410 | 7.368 | 1.770 | 0.077 | -1.399 | 27.481 |
OSCEOLA | 20.6072 | 7.843 | 2.627 | 0.009 | 5.235 | 35.980 |
OSKALOOSA | 3.0222 | 5.400 | 0.560 | 0.576 | -7.561 | 13.605 |
OTHO | -21.9789 | 45.553 | -0.482 | 0.629 | -111.261 | 67.303 |
OTTUMWA | 17.3559 | 4.459 | 3.893 | 0.000 | 8.617 | 26.095 |
OTTUWMA | 19.6167 | 6.859 | 2.860 | 0.004 | 6.173 | 33.060 |
PACIFIC JUNCTION | -33.2145 | 18.484 | -1.797 | 0.072 | -69.442 | 3.013 |
PALO | -26.9541 | 33.060 | -0.815 | 0.415 | -91.750 | 37.842 |
PANORA | 6.4980 | 12.651 | 0.514 | 0.607 | -18.297 | 31.293 |
PARKERSBURG | 11.0946 | 22.127 | 0.501 | 0.616 | -32.273 | 54.462 |
PAULLINA | -4.4437 | 18.048 | -0.246 | 0.806 | -39.817 | 30.929 |
PELLA | 21.8905 | 7.074 | 3.095 | 0.002 | 8.027 | 35.754 |
PEOSTA | 13.3592 | 27.244 | 0.490 | 0.624 | -40.038 | 66.756 |
PERRY | 11.9243 | 7.265 | 1.641 | 0.101 | -2.315 | 26.164 |
PLEASANT HILL | 0.2158 | 6.905 | 0.031 | 0.975 | -13.319 | 13.750 |
PLEASANTVILLE | -10.8642 | 16.005 | -0.679 | 0.497 | -42.234 | 20.506 |
POCAHONTAS | 13.8810 | 11.891 | 1.167 | 0.243 | -9.424 | 37.186 |
POLK CITY | 16.2014 | 12.396 | 1.307 | 0.191 | -8.094 | 40.497 |
POSTVILLE | -1.3631 | 30.053 | -0.045 | 0.964 | -60.266 | 57.540 |
PRAIRIE CITY | -15.9101 | 35.473 | -0.449 | 0.654 | -85.436 | 53.616 |
PRIMGHAR | 5.7114 | 19.640 | 0.291 | 0.771 | -32.782 | 44.205 |
PRINCETON | 9.8593 | 32.635 | 0.302 | 0.763 | -54.104 | 73.822 |
RAYMOND | 0.4309 | 17.022 | 0.025 | 0.980 | -32.931 | 33.793 |
RED OAK | 3.0798 | 7.348 | 0.419 | 0.675 | -11.322 | 17.482 |
REINBECK | 5.7782 | 35.472 | 0.163 | 0.871 | -63.747 | 75.303 |
REMSEN | 15.5809 | 17.841 | 0.873 | 0.382 | -19.387 | 50.549 |
RICEVILLE | 13.8661 | 35.472 | 0.391 | 0.696 | -55.659 | 83.391 |
RIVERSIDE | 17.8288 | 13.731 | 1.298 | 0.194 | -9.084 | 44.741 |
ROBINS | 25.6121 | 143.994 | 0.178 | 0.859 | -256.613 | 307.837 |
ROCK RAPIDS | 3.8871 | 9.370 | 0.415 | 0.678 | -14.478 | 22.252 |
ROCK VALLEY | 5.8394 | 21.045 | 0.277 | 0.781 | -35.408 | 47.087 |
ROCKWELL | 21.8659 | 16.104 | 1.358 | 0.175 | -9.698 | 53.429 |
ROCKWELL CITY | 17.4914 | 19.288 | 0.907 | 0.364 | -20.313 | 55.296 |
ROLFE | 15.4339 | 34.948 | 0.442 | 0.659 | -53.063 | 83.930 |
RUTHVEN | 9.3762 | 30.053 | 0.312 | 0.755 | -49.527 | 68.280 |
SAC CITY | 3.5511 | 8.159 | 0.435 | 0.663 | -12.441 | 19.543 |
SANBORN | 22.6244 | 15.408 | 1.468 | 0.142 | -7.574 | 52.823 |
SCHALLER | 8.8142 | 52.594 | 0.168 | 0.867 | -94.268 | 111.897 |
SCHLESWIG | 3.5061 | 14.183 | 0.247 | 0.805 | -24.292 | 31.304 |
SCRANTON | 4.1263 | 40.748 | 0.101 | 0.919 | -75.739 | 83.991 |
SERGEANT BLUFF | 9.4991 | 10.586 | 0.897 | 0.370 | -11.249 | 30.247 |
SHEFFIELD | -16.7184 | 39.211 | -0.426 | 0.670 | -93.572 | 60.135 |
SHELDON | 18.7288 | 6.652 | 2.815 | 0.005 | 5.691 | 31.767 |
SHELLSBURG | 11.3929 | 11.306 | 1.008 | 0.314 | -10.767 | 33.553 |
SHENANDOAH | 15.2834 | 7.973 | 1.917 | 0.055 | -0.342 | 30.909 |
SIBLEY | 10.3781 | 10.950 | 0.948 | 0.343 | -11.084 | 31.840 |
SIGOURNEY | 14.8261 | 11.852 | 1.251 | 0.211 | -8.403 | 38.056 |
SIOUX CENTER | 31.8050 | 7.810 | 4.072 | 0.000 | 16.497 | 47.113 |
SIOUX CITY | 7.8343 | 2.649 | 2.958 | 0.003 | 2.643 | 13.025 |
SIOUX RAPIDS | 8.1861 | 14.425 | 0.567 | 0.570 | -20.086 | 36.459 |
SLATER | 7.9728 | 25.292 | 0.315 | 0.753 | -41.600 | 57.545 |
SLOAN | 16.8442 | 19.203 | 0.877 | 0.380 | -20.792 | 54.481 |
SOLON | 9.0008 | 15.628 | 0.576 | 0.565 | -21.630 | 39.632 |
SPENCER | 9.2005 | 4.846 | 1.899 | 0.058 | -0.297 | 18.698 |
SPIRIT LAKE | 13.5214 | 5.189 | 2.606 | 0.009 | 3.352 | 23.691 |
SPRINGVILLE | 4.1382 | 39.211 | 0.106 | 0.916 | -72.715 | 80.991 |
ST ANSGAR | -12.1602 | 14.951 | -0.813 | 0.416 | -41.463 | 17.143 |
ST CHARLES | 0.6915 | 38.506 | 0.018 | 0.986 | -74.778 | 76.161 |
ST LUCAS | 18.4430 | 27.006 | 0.683 | 0.495 | -34.487 | 71.373 |
STANWOOD | -86.2965 | 67.890 | -1.271 | 0.204 | -219.359 | 46.766 |
STATE CENTER | 25.8882 | 15.676 | 1.652 | 0.099 | -4.835 | 56.612 |
STORM LAKE | 5.5457 | 4.268 | 1.299 | 0.194 | -2.820 | 13.911 |
STORY CITY | 13.1286 | 12.974 | 1.012 | 0.312 | -12.300 | 38.557 |
STRATFORD | 1.0744 | 22.949 | 0.047 | 0.963 | -43.905 | 46.054 |
STRAWBERRY POINT | 10.7485 | 26.771 | 0.402 | 0.688 | -41.722 | 63.219 |
STUART | 7.9827 | 12.135 | 0.658 | 0.511 | -15.802 | 31.768 |
SULLY | 1.1542 | 58.798 | 0.020 | 0.984 | -114.088 | 116.397 |
SUMNER | 8.0987 | 11.222 | 0.722 | 0.471 | -13.897 | 30.094 |
SUTHERLAND | -6.2071 | 34.445 | -0.180 | 0.857 | -73.719 | 61.305 |
SWEA CITY | -0.6337 | 35.472 | -0.018 | 0.986 | -70.158 | 68.891 |
SWISHER | 58.1100 | 13.131 | 4.425 | 0.000 | 32.373 | 83.847 |
TIFFIN | 5.0404 | 17.774 | 0.284 | 0.777 | -29.796 | 39.877 |
TIPTON | 16.0052 | 9.177 | 1.744 | 0.081 | -1.981 | 33.991 |
TOLEDO | 11.9659 | 7.454 | 1.605 | 0.108 | -2.643 | 26.575 |
TRAER | -5.3410 | 15.450 | -0.346 | 0.730 | -35.623 | 24.942 |
TREYNOR | -3.9643 | 41.587 | -0.095 | 0.924 | -85.474 | 77.545 |
TRIPOLI | 15.1197 | 13.266 | 1.140 | 0.254 | -10.882 | 41.122 |
URBANA | -1.5880 | 37.837 | -0.042 | 0.967 | -75.747 | 72.571 |
URBANDALE | 12.8917 | 4.344 | 2.968 | 0.003 | 4.378 | 21.405 |
Urbandale | 3.6420 | 33.060 | 0.110 | 0.912 | -61.154 | 68.438 |
VAN METER | -84.4213 | 52.594 | -1.605 | 0.108 | -187.504 | 18.662 |
VICTOR | 18.0625 | 25.489 | 0.709 | 0.479 | -31.895 | 68.020 |
VILLISCA | 1.0963 | 43.434 | 0.025 | 0.980 | -84.034 | 86.227 |
VINTON | 17.6707 | 14.250 | 1.240 | 0.215 | -10.258 | 45.599 |
WALFORD | 16.5690 | 37.201 | 0.445 | 0.656 | -56.345 | 89.483 |
WALKER | -16.2928 | 45.553 | -0.358 | 0.721 | -105.574 | 72.989 |
WALL LAKE | 13.4953 | 54.439 | 0.248 | 0.804 | -93.203 | 120.193 |
WALNUT | 0.8355 | 28.270 | 0.030 | 0.976 | -54.572 | 56.243 |
WAPELLO | 13.6309 | 14.681 | 0.928 | 0.353 | -15.143 | 42.405 |
WASHBURN | -64.1395 | 52.594 | -1.220 | 0.223 | -167.222 | 38.943 |
WASHINGTON | 2.8315 | 5.756 | 0.492 | 0.623 | -8.450 | 14.113 |
WATERLOO | -22.7595 | 2.590 | -8.787 | 0.000 | -27.836 | -17.683 |
WAUKEE | 18.2832 | 5.611 | 3.258 | 0.001 | 7.285 | 29.281 |
WAUKON | 10.8178 | 7.321 | 1.478 | 0.140 | -3.532 | 25.167 |
WAVERLY | 10.9909 | 5.350 | 2.054 | 0.040 | 0.505 | 21.477 |
WEBSTER CITY | 10.4118 | 7.008 | 1.486 | 0.137 | -3.323 | 24.147 |
WELLMAN | 7.5048 | 13.078 | 0.574 | 0.566 | -18.128 | 33.137 |
WELLSBURG | -29.4621 | 44.456 | -0.663 | 0.508 | -116.594 | 57.670 |
WESLEY | 9.1705 | 17.261 | 0.531 | 0.595 | -24.661 | 43.002 |
WEST BEND | 11.4648 | 19.823 | 0.578 | 0.563 | -27.388 | 50.317 |
WEST BRANCH | 22.8898 | 12.652 | 1.809 | 0.070 | -1.908 | 47.687 |
WEST BURLINGTON | 14.0926 | 8.509 | 1.656 | 0.098 | -2.584 | 30.770 |
WEST DES MOINES | 30.2788 | 2.750 | 11.011 | 0.000 | 24.889 | 35.669 |
WEST LIBERTY | 8.6568 | 12.419 | 0.697 | 0.486 | -15.683 | 32.997 |
WEST POINT | 17.8887 | 15.450 | 1.158 | 0.247 | -12.393 | 48.171 |
WEST UNION | 10.8489 | 11.172 | 0.971 | 0.332 | -11.048 | 32.746 |
WHEATLAND | 11.2630 | 39.957 | 0.282 | 0.778 | -67.053 | 89.578 |
WILLIAMSBURG | 16.4116 | 12.626 | 1.300 | 0.194 | -8.336 | 41.159 |
WILTON | 20.7052 | 11.570 | 1.790 | 0.074 | -1.971 | 43.382 |
WINDSOR HEIGHTS | 32.6892 | 4.072 | 8.028 | 0.000 | 24.708 | 40.670 |
WINTERSET | 5.3052 | 7.402 | 0.717 | 0.474 | -9.202 | 19.813 |
WINTHROP | 15.6299 | 28.002 | 0.558 | 0.577 | -39.254 | 70.514 |
WOODBINE | 19.7643 | 16.411 | 1.204 | 0.228 | -12.400 | 51.929 |
WOODWARD | 11.6388 | 34.446 | 0.338 | 0.735 | -55.874 | 79.151 |
ZWINGLE | -15.5083 | 28.270 | -0.549 | 0.583 | -70.916 | 39.900 |
Omnibus: | 364812.861 | Durbin-Watson: | 2.001 |
---|---|---|---|
Prob(Omnibus): | 0.000 | Jarque-Bera (JB): | 67102567296.771 |
Skew: | 5.870 | Prob(JB): | 0.00 |
Kurtosis: | 2448.603 | Cond. No. | 1.39e+04 |
# Zip Code Statistics
model_zip.summary()
Dep. Variable: | sale_dollars | R-squared: | 0.719 |
---|---|---|---|
Model: | OLS | Adj. R-squared: | 0.719 |
Method: | Least Squares | F-statistic: | 1670. |
Date: | Sun, 22 Oct 2017 | Prob (F-statistic): | 0.00 |
Time: | 22:32:28 | Log-Likelihood: | -1.8128e+06 |
No. Observations: | 269258 | AIC: | 3.626e+06 |
Df Residuals: | 268844 | BIC: | 3.631e+06 |
Df Model: | 413 | ||
Covariance Type: | nonrobust |
coef | std err | t | P>|t| | [0.025 | 0.975] | |
---|---|---|---|---|---|---|
const | -96.5788 | 2.494 | -38.719 | 0.000 | -101.468 | -91.690 |
state_bottle_retail | 6.7432 | 0.038 | 179.585 | 0.000 | 6.670 | 6.817 |
bottles_sold | 13.3491 | 0.017 | 808.482 | 0.000 | 13.317 | 13.381 |
50002 | -29.9087 | 31.092 | -0.962 | 0.336 | -90.848 | 31.030 |
50003 | 8.2845 | 11.652 | 0.711 | 0.477 | -14.554 | 31.123 |
50006 | -8.8378 | 18.561 | -0.476 | 0.634 | -45.216 | 27.541 |
50009 | -4.3451 | 5.048 | -0.861 | 0.389 | -14.240 | 5.550 |
50014 | -22.4837 | 9.811 | -2.292 | 0.022 | -41.712 | -3.255 |
50020 | -3.5390 | 16.882 | -0.210 | 0.834 | -36.628 | 29.550 |
50021 | 19.1865 | 4.715 | 4.069 | 0.000 | 9.945 | 28.428 |
50022 | -3.0223 | 6.759 | -0.447 | 0.655 | -16.269 | 10.225 |
50023 | -12.3539 | 4.878 | -2.533 | 0.011 | -21.914 | -2.794 |
50025 | -1.3936 | 14.401 | -0.097 | 0.923 | -29.619 | 26.832 |
50028 | 7.1533 | 26.799 | 0.267 | 0.790 | -45.373 | 59.679 |
50033 | 0.1676 | 23.753 | 0.007 | 0.994 | -46.387 | 46.722 |
50035 | 4.8937 | 10.940 | 0.447 | 0.655 | -16.548 | 26.336 |
50036 | 0.6791 | 5.877 | 0.116 | 0.908 | -10.839 | 12.197 |
50044 | -154.1943 | 50.875 | -3.031 | 0.002 | -253.907 | -54.481 |
50046 | 7.6630 | 28.565 | 0.268 | 0.788 | -48.324 | 63.650 |
50047 | -5.8497 | 15.301 | -0.382 | 0.702 | -35.839 | 24.140 |
50048 | -31.6252 | 33.504 | -0.944 | 0.345 | -97.292 | 34.042 |
50049 | -0.3689 | 9.634 | -0.038 | 0.969 | -19.252 | 18.514 |
50054 | -0.8693 | 14.868 | -0.058 | 0.953 | -30.010 | 28.271 |
50056 | -21.3511 | 39.193 | -0.545 | 0.586 | -98.168 | 55.465 |
50058 | -10.8854 | 18.867 | -0.577 | 0.564 | -47.865 | 26.094 |
50060 | -11.7605 | 17.990 | -0.654 | 0.513 | -47.021 | 23.500 |
50061 | -141.0714 | 83.019 | -1.699 | 0.089 | -303.786 | 21.643 |
50069 | -6.6335 | 17.856 | -0.371 | 0.710 | -41.631 | 28.364 |
50071 | -22.8323 | 56.427 | -0.405 | 0.686 | -133.428 | 87.763 |
50072 | -2.4535 | 33.504 | -0.073 | 0.942 | -68.120 | 63.213 |
50075 | 7.8137 | 21.330 | 0.366 | 0.714 | -33.992 | 49.619 |
50076 | -37.6038 | 43.403 | -0.866 | 0.386 | -122.673 | 47.466 |
50107 | -37.4722 | 38.489 | -0.974 | 0.330 | -112.910 | 37.965 |
50109 | -11.3427 | 25.928 | -0.437 | 0.662 | -62.161 | 39.475 |
50111 | -3.0709 | 5.723 | -0.537 | 0.592 | -14.288 | 8.147 |
50112 | -1.4448 | 5.804 | -0.249 | 0.803 | -12.820 | 9.930 |
50115 | 3.8363 | 17.471 | 0.220 | 0.826 | -30.407 | 38.080 |
50122 | 7.6937 | 23.290 | 0.330 | 0.741 | -37.954 | 53.342 |
50123 | -26.4626 | 37.190 | -0.712 | 0.477 | -99.354 | 46.429 |
50124 | -0.5241 | 18.789 | -0.028 | 0.978 | -37.350 | 36.302 |
50125 | -9.7245 | 5.412 | -1.797 | 0.072 | -20.332 | 0.883 |
50126 | 5.1077 | 8.144 | 0.627 | 0.531 | -10.855 | 21.070 |
50129 | 0.4383 | 7.387 | 0.059 | 0.953 | -14.039 | 14.916 |
50130 | 8.1887 | 22.052 | 0.371 | 0.710 | -35.033 | 51.410 |
50131 | 2.4712 | 5.018 | 0.493 | 0.622 | -7.363 | 12.306 |
50135 | -7.7712 | 22.714 | -0.342 | 0.732 | -52.290 | 36.748 |
50136 | -3.3111 | 45.516 | -0.073 | 0.942 | -92.520 | 85.898 |
50138 | -1.2468 | 5.961 | -0.209 | 0.834 | -12.930 | 10.436 |
50140 | -1.5484 | 19.891 | -0.078 | 0.938 | -40.534 | 37.437 |
50142 | 2.1466 | 12.386 | 0.173 | 0.862 | -22.130 | 26.423 |
50144 | -14.2001 | 18.946 | -0.749 | 0.454 | -51.335 | 22.934 |
50150 | -15.2315 | 61.334 | -0.248 | 0.804 | -135.444 | 104.981 |
50156 | 1.6367 | 15.342 | 0.107 | 0.915 | -28.434 | 31.707 |
50158 | -4.6139 | 4.609 | -1.001 | 0.317 | -13.648 | 4.420 |
50160 | -20.0637 | 45.515 | -0.441 | 0.659 | -109.272 | 69.145 |
50161 | 3.1519 | 39.936 | 0.079 | 0.937 | -75.122 | 81.426 |
50162 | 4.6876 | 83.017 | 0.056 | 0.955 | -158.023 | 167.398 |
50163 | -6.5949 | 15.731 | -0.419 | 0.675 | -37.426 | 24.237 |
50170 | -4.2230 | 18.636 | -0.227 | 0.821 | -40.749 | 32.303 |
50171 | -2.3062 | 11.207 | -0.206 | 0.837 | -24.272 | 19.659 |
50201 | -4.6950 | 7.111 | -0.660 | 0.509 | -18.632 | 9.242 |
50207 | -1.8600 | 38.489 | -0.048 | 0.961 | -77.297 | 73.577 |
50208 | -4.7194 | 4.871 | -0.969 | 0.333 | -14.266 | 4.827 |
50210 | 3.4820 | 34.443 | 0.101 | 0.919 | -64.025 | 70.989 |
50211 | 10.1313 | 9.821 | 1.032 | 0.302 | -9.118 | 29.380 |
50212 | -3.7959 | 18.486 | -0.205 | 0.837 | -40.029 | 32.437 |
50213 | 7.0022 | 8.085 | 0.866 | 0.386 | -8.845 | 22.849 |
50216 | -7.1645 | 12.788 | -0.560 | 0.575 | -32.229 | 17.900 |
50219 | 8.3708 | 7.344 | 1.140 | 0.254 | -6.023 | 22.765 |
50220 | -1.7786 | 7.528 | -0.236 | 0.813 | -16.533 | 12.976 |
50225 | -24.5009 | 16.103 | -1.521 | 0.128 | -56.063 | 7.061 |
50226 | 2.5729 | 12.537 | 0.205 | 0.837 | -22.000 | 27.145 |
50228 | -29.7824 | 35.467 | -0.840 | 0.401 | -99.296 | 39.731 |
50240 | -13.0806 | 38.489 | -0.340 | 0.734 | -88.519 | 62.357 |
50244 | -5.7635 | 25.327 | -0.228 | 0.820 | -55.405 | 43.877 |
50247 | 12.0218 | 15.777 | 0.762 | 0.446 | -18.900 | 42.944 |
50248 | 1.2860 | 11.399 | 0.113 | 0.910 | -21.056 | 23.628 |
50249 | -12.3985 | 22.996 | -0.539 | 0.590 | -57.471 | 32.674 |
50250 | -5.7329 | 12.281 | -0.467 | 0.641 | -29.803 | 18.337 |
50251 | -12.4684 | 58.727 | -0.212 | 0.832 | -127.572 | 102.635 |
50261 | -98.1211 | 52.538 | -1.868 | 0.062 | -201.095 | 4.853 |
50263 | 4.7477 | 5.954 | 0.797 | 0.425 | -6.923 | 16.418 |
50265 | -1.8658 | 3.914 | -0.477 | 0.634 | -9.538 | 5.806 |
50266 | 45.0208 | 4.514 | 9.973 | 0.000 | 36.173 | 53.868 |
50273 | -8.2800 | 7.660 | -1.081 | 0.280 | -23.293 | 6.733 |
50276 | -2.1760 | 34.443 | -0.063 | 0.950 | -69.683 | 65.331 |
50300 | -12.2810 | 16.663 | -0.737 | 0.461 | -44.940 | 20.378 |
50309 | -102.6366 | 10.485 | -9.789 | 0.000 | -123.187 | -82.086 |
50310 | -31.7304 | 4.916 | -6.454 | 0.000 | -41.366 | -22.095 |
50311 | 7.3928 | 4.249 | 1.740 | 0.082 | -0.935 | 15.721 |
50312 | -15.1329 | 6.365 | -2.377 | 0.017 | -27.609 | -2.657 |
50313 | -26.0639 | 7.411 | -3.517 | 0.000 | -40.590 | -11.538 |
50314 | 5.2666 | 3.875 | 1.359 | 0.174 | -2.329 | 12.862 |
50315 | -23.8340 | 4.384 | -5.437 | 0.000 | -32.426 | -15.242 |
50316 | -60.5910 | 6.187 | -9.793 | 0.000 | -72.717 | -48.465 |
50317 | -25.3308 | 3.896 | -6.501 | 0.000 | -32.968 | -17.694 |
50320 | 32.8486 | 4.323 | 7.599 | 0.000 | 24.376 | 41.321 |
50321 | -1.1673 | 5.480 | -0.213 | 0.831 | -11.908 | 9.573 |
50322 | -3.8985 | 4.493 | -0.868 | 0.386 | -12.704 | 4.907 |
50323 | -2.7250 | 15.301 | -0.178 | 0.859 | -32.714 | 27.264 |
50324 | -25.0191 | 15.262 | -1.639 | 0.101 | -54.932 | 4.894 |
50325 | -14.1999 | 7.134 | -1.990 | 0.047 | -28.183 | -0.217 |
50327 | -4.3109 | 18.486 | -0.233 | 0.816 | -40.544 | 31.922 |
50401 | -14.1112 | 3.984 | -3.542 | 0.000 | -21.919 | -6.303 |
50421 | -13.2058 | 15.300 | -0.863 | 0.388 | -43.194 | 16.783 |
50423 | -7.2069 | 16.401 | -0.439 | 0.660 | -39.353 | 24.939 |
50424 | -3.3914 | 16.717 | -0.203 | 0.839 | -36.157 | 29.374 |
50428 | -5.5920 | 5.152 | -1.085 | 0.278 | -15.689 | 4.505 |
50430 | 10.3952 | 37.189 | 0.280 | 0.780 | -62.494 | 83.285 |
50435 | 13.3039 | 17.856 | 0.745 | 0.456 | -21.693 | 48.301 |
50436 | -10.9910 | 8.683 | -1.266 | 0.206 | -28.009 | 6.027 |
50438 | -24.6533 | 15.511 | -1.589 | 0.112 | -55.055 | 5.748 |
50441 | -8.4437 | 8.222 | -1.027 | 0.304 | -24.558 | 7.671 |
50450 | -10.1527 | 14.367 | -0.707 | 0.480 | -38.311 | 18.006 |
50452 | 4.2868 | 71.905 | 0.060 | 0.952 | -136.645 | 145.218 |
50456 | -31.9335 | 32.230 | -0.991 | 0.322 | -95.103 | 31.236 |
50458 | -7.1546 | 30.738 | -0.233 | 0.816 | -67.401 | 53.092 |
50459 | 6.9788 | 11.176 | 0.624 | 0.532 | -14.926 | 28.884 |
50461 | -0.6935 | 7.626 | -0.091 | 0.928 | -15.640 | 14.253 |
50466 | 0.2075 | 35.466 | 0.006 | 0.995 | -69.305 | 69.720 |
50469 | 8.0932 | 16.201 | 0.500 | 0.617 | -23.661 | 39.847 |
50472 | -25.5296 | 15.059 | -1.695 | 0.090 | -55.046 | 3.987 |
50475 | -30.4922 | 39.193 | -0.778 | 0.437 | -107.309 | 46.325 |
50483 | -4.1310 | 17.348 | -0.238 | 0.812 | -38.133 | 29.871 |
50501 | -6.7320 | 4.443 | -1.515 | 0.130 | -15.441 | 1.977 |
50511 | -3.0320 | 6.793 | -0.446 | 0.655 | -16.346 | 10.282 |
50514 | -42.4734 | 49.358 | -0.861 | 0.390 | -139.214 | 54.267 |
50517 | -4.5785 | 10.826 | -0.423 | 0.672 | -25.797 | 16.640 |
50525 | -5.4497 | 15.960 | -0.341 | 0.733 | -36.732 | 25.832 |
50529 | -103.5983 | 40.726 | -2.544 | 0.011 | -183.419 | -23.777 |
50530 | 5.1168 | 21.216 | 0.241 | 0.809 | -36.465 | 46.699 |
50533 | 1.4513 | 12.059 | 0.120 | 0.904 | -22.184 | 25.087 |
50535 | -8.0749 | 36.587 | -0.221 | 0.825 | -79.785 | 63.635 |
50536 | -6.0549 | 7.057 | -0.858 | 0.391 | -19.886 | 7.776 |
50540 | -29.3117 | 83.017 | -0.353 | 0.724 | -192.023 | 133.400 |
50541 | -47.6681 | 50.874 | -0.937 | 0.349 | -147.379 | 52.043 |
50542 | -86.8969 | 52.539 | -1.654 | 0.098 | -189.871 | 16.077 |
50543 | -11.1202 | 14.646 | -0.759 | 0.448 | -39.826 | 17.586 |
50548 | -1.8959 | 9.021 | -0.210 | 0.834 | -19.576 | 15.784 |
50554 | -3.6233 | 15.021 | -0.241 | 0.809 | -33.063 | 25.817 |
50563 | 5.0879 | 15.686 | 0.324 | 0.746 | -25.657 | 35.832 |
50569 | -35.8643 | 45.516 | -0.788 | 0.431 | -125.074 | 53.345 |
50574 | 0.1913 | 12.040 | 0.016 | 0.987 | -23.406 | 23.789 |
50579 | 3.5673 | 19.359 | 0.184 | 0.854 | -34.376 | 41.511 |
50581 | 1.8230 | 34.943 | 0.052 | 0.958 | -66.664 | 70.310 |
50583 | -10.0252 | 8.390 | -1.195 | 0.232 | -26.470 | 6.420 |
50585 | -5.4941 | 14.540 | -0.378 | 0.706 | -33.991 | 23.003 |
50588 | -8.0050 | 4.714 | -1.698 | 0.089 | -17.245 | 1.235 |
50590 | -14.1602 | 35.466 | -0.399 | 0.690 | -83.672 | 55.352 |
50595 | -3.2744 | 7.281 | -0.450 | 0.653 | -17.545 | 10.996 |
50597 | -1.9411 | 19.890 | -0.098 | 0.922 | -40.925 | 37.043 |
50601 | -1.0390 | 21.445 | -0.048 | 0.961 | -43.070 | 40.992 |
50602 | 0.6020 | 17.052 | 0.035 | 0.972 | -32.820 | 34.024 |
50606 | 14.7401 | 23.442 | 0.629 | 0.529 | -31.206 | 60.686 |
50613 | -5.4575 | 3.699 | -1.475 | 0.140 | -12.707 | 1.792 |
50616 | -7.3308 | 7.482 | -0.980 | 0.327 | -21.996 | 7.334 |
50619 | -4.3782 | 25.137 | -0.174 | 0.862 | -53.646 | 44.889 |
50621 | -2.6679 | 20.571 | -0.130 | 0.897 | -42.987 | 37.651 |
50622 | 0.0443 | 18.413 | 0.002 | 0.998 | -36.045 | 36.133 |
50627 | 0.9066 | 11.687 | 0.078 | 0.938 | -22.000 | 23.813 |
50628 | 16.6751 | 29.438 | 0.566 | 0.571 | -41.022 | 74.372 |
50629 | -8.3797 | 22.051 | -0.380 | 0.704 | -51.600 | 34.840 |
50630 | 6.9327 | 23.142 | 0.300 | 0.765 | -38.425 | 52.290 |
50634 | 11.7941 | 83.017 | 0.142 | 0.887 | -150.917 | 174.505 |
50635 | 8.6252 | 23.753 | 0.363 | 0.717 | -37.929 | 55.180 |
50636 | -2.7311 | 19.890 | -0.137 | 0.891 | -41.715 | 36.253 |
50638 | 0.9179 | 10.291 | 0.089 | 0.929 | -19.251 | 21.087 |
50641 | -152.2637 | 36.588 | -4.162 | 0.000 | -223.976 | -80.552 |
50643 | -8.0216 | 32.229 | -0.249 | 0.803 | -71.190 | 55.147 |
50644 | 2.7410 | 7.204 | 0.380 | 0.704 | -11.379 | 16.861 |
50647 | -3.3971 | 9.862 | -0.344 | 0.730 | -22.726 | 15.932 |
50648 | -3.7996 | 15.301 | -0.248 | 0.804 | -33.788 | 26.189 |
50651 | 2.7037 | 13.132 | 0.206 | 0.837 | -23.034 | 28.442 |
50658 | -1.9036 | 22.051 | -0.086 | 0.931 | -45.124 | 41.316 |
50659 | -3.0345 | 12.859 | -0.236 | 0.813 | -28.239 | 22.170 |
50662 | -4.6819 | 8.624 | -0.543 | 0.587 | -21.584 | 12.220 |
50665 | -2.5488 | 22.179 | -0.115 | 0.909 | -46.019 | 40.922 |
50667 | -13.2569 | 17.110 | -0.775 | 0.438 | -46.793 | 20.279 |
50669 | -7.9526 | 35.466 | -0.224 | 0.823 | -77.465 | 61.560 |
50674 | -5.5210 | 11.382 | -0.485 | 0.628 | -27.829 | 16.787 |
50675 | -19.0237 | 15.554 | -1.223 | 0.221 | -49.510 | 11.462 |
50676 | 1.4611 | 13.396 | 0.109 | 0.913 | -24.794 | 27.716 |
50677 | -2.5882 | 5.710 | -0.453 | 0.650 | -13.779 | 8.602 |
50680 | -43.2726 | 44.422 | -0.974 | 0.330 | -130.338 | 43.793 |
50682 | 2.0910 | 28.025 | 0.075 | 0.941 | -52.836 | 57.018 |
50701 | -16.7178 | 4.837 | -3.457 | 0.001 | -26.197 | -7.238 |
50702 | -16.4732 | 4.342 | -3.794 | 0.000 | -24.984 | -7.962 |
50703 | -72.2323 | 4.494 | -16.072 | 0.000 | -81.041 | -63.424 |
50707 | -40.2384 | 8.129 | -4.950 | 0.000 | -56.171 | -24.305 |
50801 | 0.0630 | 6.692 | 0.009 | 0.992 | -13.053 | 13.179 |
50830 | -3.8187 | 58.727 | -0.065 | 0.948 | -118.922 | 111.285 |
50833 | 3.8418 | 16.401 | 0.234 | 0.815 | -28.305 | 35.988 |
50841 | -7.5872 | 13.506 | -0.562 | 0.574 | -34.058 | 18.884 |
50846 | -7.7487 | 25.928 | -0.299 | 0.765 | -58.566 | 43.069 |
50849 | -9.2956 | 14.792 | -0.628 | 0.530 | -38.288 | 19.697 |
50851 | -8.6087 | 17.288 | -0.498 | 0.619 | -42.492 | 25.275 |
50854 | 6.5800 | 14.539 | 0.453 | 0.651 | -21.917 | 35.077 |
50864 | -12.5812 | 43.403 | -0.290 | 0.772 | -97.650 | 72.488 |
51001 | -5.9024 | 24.589 | -0.240 | 0.810 | -54.097 | 42.292 |
51002 | -29.9874 | 54.378 | -0.551 | 0.581 | -136.567 | 76.592 |
51004 | -0.5980 | 16.055 | -0.037 | 0.970 | -32.066 | 30.870 |
51005 | -20.6372 | 61.334 | -0.336 | 0.737 | -140.850 | 99.575 |
51012 | -19.9132 | 7.629 | -2.610 | 0.009 | -34.866 | -4.960 |
51023 | -7.1054 | 15.642 | -0.454 | 0.650 | -37.762 | 23.552 |
51025 | -0.8733 | 11.617 | -0.075 | 0.940 | -23.642 | 21.896 |
51027 | 2.2879 | 33.963 | 0.067 | 0.946 | -64.279 | 68.855 |
51028 | -12.0640 | 28.847 | -0.418 | 0.676 | -68.604 | 44.476 |
51031 | -2.5559 | 6.126 | -0.417 | 0.676 | -14.562 | 9.450 |
51034 | -4.1896 | 10.041 | -0.417 | 0.676 | -23.870 | 15.491 |
51035 | 3.1191 | 23.290 | 0.134 | 0.893 | -42.528 | 48.767 |
51038 | -12.7361 | 64.323 | -0.198 | 0.843 | -138.807 | 113.335 |
51040 | -7.2080 | 7.499 | -0.961 | 0.336 | -21.906 | 7.490 |
51041 | 17.6005 | 12.021 | 1.464 | 0.143 | -5.959 | 41.160 |
51046 | -18.0042 | 18.128 | -0.993 | 0.321 | -53.534 | 17.526 |
51050 | 1.9184 | 17.923 | 0.107 | 0.915 | -33.210 | 37.047 |
51053 | -4.7779 | 52.538 | -0.091 | 0.928 | -107.751 | 98.195 |
51054 | -4.1676 | 10.758 | -0.387 | 0.698 | -25.252 | 16.917 |
51055 | 3.0083 | 19.274 | 0.156 | 0.876 | -34.769 | 40.785 |
51058 | -19.7716 | 34.443 | -0.574 | 0.566 | -87.278 | 47.735 |
51101 | -19.7871 | 8.101 | -2.442 | 0.015 | -35.665 | -3.909 |
51103 | -9.8981 | 6.168 | -1.605 | 0.109 | -21.988 | 2.192 |
51104 | -8.7683 | 5.678 | -1.544 | 0.123 | -19.897 | 2.360 |
51105 | -21.7183 | 6.601 | -3.290 | 0.001 | -34.657 | -8.780 |
51106 | 10.4248 | 4.836 | 2.156 | 0.031 | 0.947 | 19.902 |
51108 | -6.1111 | 8.038 | -0.760 | 0.447 | -21.866 | 9.643 |
51109 | -6.3463 | 14.755 | -0.430 | 0.667 | -35.267 | 22.574 |
51201 | 5.1171 | 6.941 | 0.737 | 0.461 | -8.486 | 18.721 |
51237 | 0.5687 | 18.712 | 0.030 | 0.976 | -36.107 | 37.244 |
51238 | 9.6976 | 39.193 | 0.247 | 0.805 | -67.119 | 86.515 |
51240 | 2.2007 | 12.765 | 0.172 | 0.863 | -22.818 | 27.219 |
51241 | -5.2087 | 11.906 | -0.437 | 0.662 | -28.544 | 18.126 |
51245 | -7.7163 | 19.708 | -0.392 | 0.695 | -46.343 | 30.910 |
51246 | -9.6877 | 9.569 | -1.012 | 0.311 | -28.442 | 9.066 |
51247 | -7.8392 | 21.104 | -0.371 | 0.710 | -49.202 | 33.524 |
51248 | 8.8231 | 15.512 | 0.569 | 0.569 | -21.579 | 39.225 |
51249 | -3.1822 | 11.115 | -0.286 | 0.775 | -24.968 | 18.603 |
51250 | 18.2911 | 8.054 | 2.271 | 0.023 | 2.506 | 34.076 |
51301 | -4.4180 | 5.241 | -0.843 | 0.399 | -14.691 | 5.855 |
51331 | -5.1645 | 7.858 | -0.657 | 0.511 | -20.566 | 10.237 |
51334 | -5.0916 | 7.704 | -0.661 | 0.509 | -20.192 | 10.009 |
51338 | -8.8283 | 76.864 | -0.115 | 0.909 | -159.480 | 141.823 |
51342 | -2.3078 | 20.779 | -0.111 | 0.912 | -43.035 | 38.419 |
51346 | 7.8156 | 12.040 | 0.649 | 0.516 | -15.783 | 31.414 |
51347 | 3.0485 | 35.466 | 0.086 | 0.932 | -66.464 | 72.561 |
51351 | 1.5295 | 7.027 | 0.218 | 0.828 | -12.243 | 15.302 |
51355 | 0.1004 | 32.230 | 0.003 | 0.998 | -63.069 | 63.269 |
51358 | -4.2979 | 30.067 | -0.143 | 0.886 | -63.228 | 54.632 |
51360 | 0.0190 | 5.560 | 0.003 | 0.997 | -10.878 | 10.916 |
51401 | 17.5053 | 5.815 | 3.010 | 0.003 | 6.108 | 28.903 |
51442 | 0.0771 | 7.159 | 0.011 | 0.991 | -13.953 | 14.108 |
51443 | 2.7054 | 28.025 | 0.097 | 0.923 | -52.223 | 57.633 |
51445 | 0.6973 | 11.723 | 0.059 | 0.953 | -22.279 | 23.673 |
51449 | -2.8651 | 17.533 | -0.163 | 0.870 | -37.230 | 31.500 |
51450 | -12.7259 | 14.792 | -0.860 | 0.390 | -41.718 | 16.266 |
51453 | 12.9900 | 117.379 | 0.111 | 0.912 | -217.069 | 243.049 |
51455 | -4.0115 | 12.765 | -0.314 | 0.753 | -29.030 | 21.007 |
51461 | -9.9745 | 14.300 | -0.698 | 0.485 | -38.002 | 18.053 |
51462 | -9.7198 | 40.725 | -0.239 | 0.811 | -89.539 | 70.099 |
51466 | -0.1239 | 54.378 | -0.002 | 0.998 | -106.703 | 106.455 |
51501 | 2.3809 | 3.837 | 0.620 | 0.535 | -5.140 | 9.902 |
51503 | 5.6901 | 4.249 | 1.339 | 0.181 | -2.638 | 14.018 |
51510 | -58.8503 | 16.008 | -3.676 | 0.000 | -90.226 | -27.475 |
51521 | -2.2436 | 9.811 | -0.229 | 0.819 | -21.472 | 16.985 |
51526 | -5.0958 | 39.936 | -0.128 | 0.898 | -83.370 | 73.178 |
51530 | 14.2455 | 117.379 | 0.121 | 0.903 | -215.814 | 244.305 |
51534 | -15.9805 | 11.055 | -1.445 | 0.148 | -37.649 | 5.688 |
51535 | -11.4494 | 90.934 | -0.126 | 0.900 | -189.677 | 166.779 |
51537 | 5.9062 | 8.267 | 0.714 | 0.475 | -10.298 | 22.110 |
51546 | 3.5996 | 20.174 | 0.178 | 0.858 | -35.940 | 43.140 |
51551 | -61.7359 | 37.190 | -1.660 | 0.097 | -134.628 | 11.156 |
51553 | 0.5729 | 67.797 | 0.008 | 0.993 | -132.308 | 133.454 |
51555 | -3.2086 | 8.297 | -0.387 | 0.699 | -19.470 | 13.053 |
51559 | 9.6207 | 25.723 | 0.374 | 0.708 | -40.796 | 60.037 |
51560 | -9.7561 | 12.956 | -0.753 | 0.451 | -35.150 | 15.638 |
51561 | -46.6442 | 18.561 | -2.513 | 0.012 | -83.022 | -10.266 |
51566 | -10.4752 | 7.607 | -1.377 | 0.169 | -25.386 | 4.435 |
51575 | -17.5749 | 41.561 | -0.423 | 0.672 | -99.034 | 63.884 |
51577 | -12.7977 | 28.291 | -0.452 | 0.651 | -68.247 | 42.652 |
51579 | 5.9828 | 16.505 | 0.362 | 0.717 | -26.366 | 38.332 |
51601 | 1.6640 | 8.211 | 0.203 | 0.839 | -14.429 | 17.757 |
51632 | -3.0163 | 7.951 | -0.379 | 0.704 | -18.601 | 12.568 |
51640 | -36.3935 | 39.193 | -0.929 | 0.353 | -113.211 | 40.424 |
52001 | -4.2894 | 3.812 | -1.125 | 0.260 | -11.760 | 3.181 |
52002 | 4.0070 | 6.282 | 0.638 | 0.524 | -8.305 | 16.319 |
52003 | -11.8010 | 7.086 | -1.665 | 0.096 | -25.690 | 2.088 |
52031 | -5.8589 | 13.395 | -0.437 | 0.662 | -32.113 | 20.396 |
52033 | 1.2119 | 16.771 | 0.072 | 0.942 | -31.660 | 34.084 |
52036 | -13.0730 | 46.694 | -0.280 | 0.780 | -104.593 | 78.447 |
52037 | -19.9528 | 37.822 | -0.528 | 0.598 | -94.084 | 54.178 |
52040 | 4.1247 | 9.162 | 0.450 | 0.653 | -13.833 | 22.082 |
52042 | 4.7953 | 22.051 | 0.217 | 0.828 | -38.425 | 48.015 |
52043 | -0.4586 | 18.636 | -0.025 | 0.980 | -36.984 | 36.067 |
52046 | -20.2794 | 37.823 | -0.536 | 0.592 | -94.411 | 53.853 |
52052 | -4.1976 | 11.100 | -0.378 | 0.705 | -25.954 | 17.558 |
52053 | 3.9616 | 26.352 | 0.150 | 0.881 | -47.688 | 55.611 |
52057 | 4.0086 | 12.493 | 0.321 | 0.748 | -20.478 | 28.495 |
52060 | 1.7497 | 6.197 | 0.282 | 0.778 | -10.396 | 13.896 |
52068 | -0.4140 | 27.270 | -0.015 | 0.988 | -53.862 | 53.034 |
52076 | -2.8712 | 26.799 | -0.107 | 0.915 | -55.396 | 49.654 |
52079 | -29.2503 | 28.291 | -1.034 | 0.301 | -84.700 | 26.200 |
52084 | -75.9708 | 10.855 | -6.999 | 0.000 | -97.246 | -54.696 |
52087 | -7.0022 | 10.521 | -0.666 | 0.506 | -27.624 | 13.619 |
52101 | -8.5698 | 6.475 | -1.324 | 0.186 | -21.260 | 4.120 |
52136 | 6.2559 | 8.938 | 0.700 | 0.484 | -11.262 | 23.773 |
52142 | -18.3814 | 24.589 | -0.748 | 0.455 | -66.576 | 29.813 |
52144 | 5.0966 | 16.250 | 0.314 | 0.754 | -26.753 | 36.946 |
52146 | -34.7268 | 29.438 | -1.180 | 0.238 | -92.424 | 22.970 |
52151 | -3.3330 | 16.772 | -0.199 | 0.842 | -36.205 | 29.539 |
52154 | 31.1452 | 31.836 | 0.978 | 0.328 | -31.253 | 93.543 |
52159 | -10.3523 | 10.063 | -1.029 | 0.304 | -30.076 | 9.371 |
52162 | -14.9808 | 30.067 | -0.498 | 0.618 | -73.911 | 43.949 |
52166 | 5.3714 | 27.032 | 0.199 | 0.842 | -47.611 | 58.354 |
52172 | -2.7345 | 7.582 | -0.361 | 0.718 | -17.595 | 12.126 |
52175 | -2.7737 | 11.333 | -0.245 | 0.807 | -24.986 | 19.439 |
52205 | 2.7027 | 8.758 | 0.309 | 0.758 | -14.463 | 19.869 |
52207 | -33.4341 | 39.936 | -0.837 | 0.402 | -111.708 | 44.840 |
52208 | -9.0768 | 10.278 | -0.883 | 0.377 | -29.221 | 11.068 |
52211 | -5.4183 | 14.792 | -0.366 | 0.714 | -34.411 | 23.574 |
52213 | 5.0373 | 15.555 | 0.324 | 0.746 | -25.450 | 35.524 |
52214 | -21.0266 | 17.857 | -1.178 | 0.239 | -56.025 | 13.972 |
52223 | 7.3775 | 61.334 | 0.120 | 0.904 | -112.835 | 127.591 |
52224 | -7.3855 | 33.504 | -0.220 | 0.826 | -73.052 | 58.281 |
52227 | -13.2876 | 31.092 | -0.427 | 0.669 | -74.226 | 47.651 |
52228 | -20.8334 | 33.964 | -0.613 | 0.540 | -87.402 | 45.735 |
52233 | -37.8710 | 10.536 | -3.595 | 0.000 | -58.520 | -17.221 |
52240 | -6.9281 | 3.548 | -1.953 | 0.051 | -13.881 | 0.025 |
52241 | 18.2449 | 4.222 | 4.321 | 0.000 | 9.969 | 26.521 |
52245 | -2.4160 | 7.422 | -0.326 | 0.745 | -16.963 | 12.131 |
52246 | 3.8799 | 6.956 | 0.558 | 0.577 | -9.755 | 17.514 |
52248 | -8.8408 | 30.739 | -0.288 | 0.774 | -69.088 | 51.406 |
52253 | -1.5028 | 17.409 | -0.086 | 0.931 | -35.624 | 32.619 |
52254 | -8.4216 | 35.466 | -0.237 | 0.812 | -77.934 | 61.091 |
52301 | 13.9906 | 10.141 | 1.380 | 0.168 | -5.885 | 33.866 |
52302 | -1.7461 | 4.912 | -0.355 | 0.722 | -11.374 | 7.882 |
52303 | 6.3331 | 10.913 | 0.580 | 0.562 | -15.055 | 27.721 |
52306 | -56.6558 | 36.014 | -1.573 | 0.116 | -127.242 | 13.930 |
52310 | 0.9092 | 6.159 | 0.148 | 0.883 | -11.162 | 12.980 |
52314 | -7.1847 | 6.334 | -1.134 | 0.257 | -19.598 | 5.229 |
52316 | 6.2083 | 36.014 | 0.172 | 0.863 | -64.378 | 76.794 |
52317 | -14.1641 | 6.122 | -2.314 | 0.021 | -26.162 | -2.166 |
52324 | -40.5398 | 33.062 | -1.226 | 0.220 | -105.341 | 24.261 |
52327 | 4.2951 | 13.854 | 0.310 | 0.757 | -22.859 | 31.449 |
52328 | 11.7279 | 143.749 | 0.082 | 0.935 | -270.016 | 293.472 |
52332 | -2.3405 | 11.465 | -0.204 | 0.838 | -24.811 | 20.130 |
52333 | -4.5720 | 15.731 | -0.291 | 0.771 | -35.404 | 26.260 |
52336 | -9.5144 | 39.193 | -0.243 | 0.808 | -86.331 | 67.302 |
52337 | -99.9479 | 67.798 | -1.474 | 0.140 | -232.831 | 32.935 |
52338 | 44.7882 | 13.262 | 3.377 | 0.001 | 18.795 | 70.781 |
52340 | -8.7741 | 17.857 | -0.491 | 0.623 | -43.773 | 26.225 |
52342 | -1.7379 | 7.709 | -0.225 | 0.822 | -16.848 | 13.372 |
52345 | -15.4194 | 37.823 | -0.408 | 0.684 | -89.551 | 58.712 |
52347 | 4.3374 | 25.523 | 0.170 | 0.865 | -45.686 | 54.361 |
52349 | 4.0480 | 14.367 | 0.282 | 0.778 | -24.111 | 32.207 |
52351 | 2.9520 | 37.189 | 0.079 | 0.937 | -69.938 | 75.842 |
52352 | -30.0249 | 45.515 | -0.660 | 0.509 | -119.234 | 59.184 |
52353 | -10.7119 | 6.090 | -1.759 | 0.079 | -22.649 | 1.225 |
52356 | -6.1172 | 13.209 | -0.463 | 0.643 | -32.007 | 19.772 |
52358 | 9.0290 | 12.789 | 0.706 | 0.480 | -16.038 | 34.096 |
52361 | 2.7874 | 12.765 | 0.218 | 0.827 | -22.231 | 27.806 |
52401 | 10.6881 | 6.225 | 1.717 | 0.086 | -1.513 | 22.889 |
52402 | -4.6156 | 3.435 | -1.344 | 0.179 | -11.348 | 2.117 |
52403 | -9.0701 | 6.396 | -1.418 | 0.156 | -21.607 | 3.466 |
52404 | -14.8756 | 3.948 | -3.768 | 0.000 | -22.613 | -7.138 |
52405 | -3.3481 | 4.202 | -0.797 | 0.426 | -11.584 | 4.888 |
52411 | 4.8312 | 6.146 | 0.786 | 0.432 | -7.214 | 16.877 |
52501 | 4.3080 | 4.328 | 0.995 | 0.320 | -4.175 | 12.791 |
52531 | 0.2523 | 11.270 | 0.022 | 0.982 | -21.836 | 22.341 |
52537 | -1.2295 | 14.470 | -0.085 | 0.932 | -29.590 | 27.131 |
52544 | -10.4153 | 6.950 | -1.499 | 0.134 | -24.037 | 3.207 |
52553 | -11.5573 | 36.013 | -0.321 | 0.748 | -82.142 | 59.028 |
52554 | -21.6532 | 32.638 | -0.663 | 0.507 | -85.622 | 42.316 |
52556 | -10.7956 | 7.552 | -1.429 | 0.153 | -25.598 | 4.006 |
52565 | 5.2998 | 14.136 | 0.375 | 0.708 | -22.405 | 33.005 |
52571 | -5.4152 | 24.950 | -0.217 | 0.828 | -54.317 | 43.486 |
52577 | -10.5555 | 5.756 | -1.834 | 0.067 | -21.837 | 0.726 |
52591 | 1.0685 | 12.001 | 0.089 | 0.929 | -22.454 | 24.591 |
52601 | -11.7506 | 4.454 | -2.638 | 0.008 | -20.480 | -3.021 |
52623 | -29.5609 | 52.538 | -0.563 | 0.574 | -132.535 | 73.413 |
52625 | -2.9976 | 49.358 | -0.061 | 0.952 | -99.737 | 93.742 |
52626 | -1.7682 | 36.014 | -0.049 | 0.961 | -72.354 | 68.817 |
52627 | -9.2686 | 5.976 | -1.551 | 0.121 | -20.982 | 2.445 |
52632 | 6.9464 | 5.483 | 1.267 | 0.205 | -3.799 | 17.692 |
52637 | -5.7046 | 11.925 | -0.478 | 0.632 | -29.077 | 17.668 |
52639 | -28.4896 | 33.063 | -0.862 | 0.389 | -93.292 | 36.313 |
52641 | 2.6482 | 6.588 | 0.402 | 0.688 | -10.264 | 15.561 |
52653 | -0.1926 | 14.793 | -0.013 | 0.990 | -29.186 | 28.801 |
52655 | -12.7123 | 7.811 | -1.628 | 0.104 | -28.021 | 2.596 |
52656 | 4.4909 | 15.554 | 0.289 | 0.773 | -25.995 | 34.977 |
52722 | 3.2800 | 4.125 | 0.795 | 0.426 | -4.804 | 11.364 |
52726 | 9.9088 | 10.153 | 0.976 | 0.329 | -9.991 | 29.809 |
52728 | -30.7783 | 33.964 | -0.906 | 0.365 | -97.346 | 35.790 |
52730 | -23.3074 | 17.409 | -1.339 | 0.181 | -57.429 | 10.814 |
52732 | -5.5050 | 4.526 | -1.216 | 0.224 | -14.376 | 3.366 |
52733 | -22.7024 | 12.957 | -1.752 | 0.080 | -48.097 | 2.693 |
52738 | -70.2835 | 12.179 | -5.771 | 0.000 | -94.154 | -46.413 |
52742 | 9.6636 | 14.540 | 0.665 | 0.506 | -18.833 | 38.161 |
52747 | 3.3977 | 13.158 | 0.258 | 0.796 | -22.392 | 29.187 |
52748 | -12.7320 | 8.096 | -1.573 | 0.116 | -28.600 | 3.136 |
52751 | -2.0188 | 25.523 | -0.079 | 0.937 | -52.043 | 48.005 |
52753 | 6.5331 | 8.703 | 0.751 | 0.453 | -10.525 | 23.591 |
52761 | -3.0989 | 4.247 | -0.730 | 0.466 | -11.422 | 5.224 |
52768 | -4.0831 | 32.638 | -0.125 | 0.900 | -68.054 | 59.887 |
52772 | 2.2827 | 9.380 | 0.243 | 0.808 | -16.102 | 20.668 |
52776 | -4.9321 | 12.559 | -0.393 | 0.695 | -29.548 | 19.684 |
52777 | -2.4986 | 39.936 | -0.063 | 0.950 | -80.773 | 75.776 |
52778 | 6.8460 | 11.724 | 0.584 | 0.559 | -16.133 | 29.825 |
52801 | -23.7006 | 143.749 | -0.165 | 0.869 | -305.444 | 258.043 |
52802 | -60.1258 | 5.848 | -10.282 | 0.000 | -71.587 | -48.665 |
52803 | -111.9066 | 7.277 | -15.378 | 0.000 | -126.169 | -97.644 |
52804 | -27.7476 | 4.444 | -6.244 | 0.000 | -36.457 | -19.038 |
52806 | -13.1925 | 4.913 | -2.685 | 0.007 | -22.823 | -3.562 |
52807 | 7.5408 | 4.189 | 1.800 | 0.072 | -0.670 | 15.751 |
56201 | -7.0070 | 42.452 | -0.165 | 0.869 | -90.212 | 76.198 |
712-2 | -4.7017 | 15.138 | -0.311 | 0.756 | -34.372 | 24.969 |
Omnibus: | 367270.389 | Durbin-Watson: | 2.000 |
---|---|---|---|
Prob(Omnibus): | 0.000 | Jarque-Bera (JB): | 67275238939.284 |
Skew: | 5.961 | Prob(JB): | 0.00 |
Kurtosis: | 2451.746 | Cond. No. | 9.85e+03 |
#County Scatterplot
plt.scatter(predictions_county, y, s=30, c='r', marker='+', zorder=10)
plt.xlabel("Variables")
plt.ylabel("Sale Dollars")
plt.plot(predictions_county, np.poly1d(np.polyfit(predictions_county, y, 1))(predictions_county))
plt.show()
print("MSE:", model_county.mse_model) ## mean squared error
MSE: 325336187.622
#City Scatterplot
plt.scatter(predictions_city, y, s=30, c='r', marker='+', zorder=10)
plt.xlabel("Variables")
plt.ylabel("Sale Dollars")
plt.plot(predictions_city, np.poly1d(np.polyfit(predictions_city, y, 1))(predictions_city))
plt.show()
print("MSE:", model_city.mse_model) ## mean squared error
MSE: 85316215.6492
#Zip Code Scatterplot
plt.scatter(predictions_zip, y, s=30, c='r', marker='+', zorder=10)
plt.xlabel("Variables")
plt.ylabel("Sale Dollars")
plt.plot(predictions_zip, np.poly1d(np.polyfit(predictions_zip, y, 1))(predictions_zip))
plt.show()
print("MSE:", model_zip.mse_model) ## mean squared error
MSE: 79648697.4319
In the graph above bottles retail and bottles sold are still in a linear configuration.
# Split my data into training and test.
x_train_county, x_test_county, y_train_county, y_test_county = train_test_split(X_county, y, test_size = 0.3, random_state = 1234)
x_train_county = sm.add_constant(x_train_county)
x_test_county = sm.add_constant(x_test_county)
# Split my data into training and test.
x_train_city, x_test_city, y_train_city, y_test_city = train_test_split(X_city, y, test_size = 0.3, random_state = 1234)
x_train_city = sm.add_constant(x_train_city)
x_test_city = sm.add_constant(x_test_city)
# Split my data into training and test.
x_train_zip, x_test_zip, y_train_zip, y_test_zip = train_test_split(X_zip, y, test_size = 0.3, random_state = 1234)
x_train_zip = sm.add_constant(x_train_zip)
x_test_zip = sm.add_constant(x_test_zip)
print(x_train.shape)
print(x_test.shape)
print(X.shape)
print(y_train.shape)
print(y_test.shape)
print(y.shape)
(1884821, 101) (807781, 101) (2692602, 100) (1884821,) (807781,) (2692602,)
#County
lm = linear_model.LinearRegression()
model_lin_county = lm.fit(x_train_county, y_train_county)
predictions_county = lm.predict(x_test_county)
#City
lm = linear_model.LinearRegression()
model_lin_city = lm.fit(x_train_city, y_train_city)
predictions_city = lm.predict(x_test_city)
#Zip Code
lm = linear_model.LinearRegression()
model_lin_zip = lm.fit(x_train_zip, y_train_zip)
predictions_zip = lm.predict(x_test_zip)
Creating a kfolds cross validation test
#County
scores_county = cross_val_score(model_lin_county, x_train_county, y_train_county, cv=5)
print("Cross validated scores:", scores_county)
print("Average: ", scores_county.mean())
Cross validated scores: [ 0.75449393 0.74423201 0.72962293 0.71827552 0.72573546] Average: 0.734471967462
#City
scores_city = cross_val_score(model_lin_city, x_train_city, y_train_city, cv=5)
print("Cross validated scores:", scores_city)
print("Average: ", scores_city.mean())
Cross validated scores: [ 7.54656701e-01 7.44332737e-01 7.30246737e-01 7.18273801e-01 -8.12869612e+05] Average: -162573.332904
#Zip Codes
scores_zip = cross_val_score(model_lin_zip, x_train_zip, y_train_zip, cv=5)
print("Cross validated scores:", scores_zip)
print("Average: ", scores_zip.mean())
Cross validated scores: [ 0.75582441 0.74495646 0.73070775 0.71923674 0.72683249] Average: 0.735511569804
Again make sure that you record any valuable information. For example, in the tax scenario, did you find the sales from the first three months of the year to be a good predictor of the total sales for the year? Plot the predictions versus the true values and discuss the successes and limitations of your models
Testing Model:
#County
predictions_county = cross_val_predict(model_lin_county, X_county, y, cv=5)
plt.scatter(y, predictions_county)
plt.xlabel('Actual')
plt.ylabel('Predicted')
accuracy_county = metrics.r2_score(y, predictions_county)
print("Cross Predicted Accuracy:", accuracy_county)
Cross Predicted Accuracy: 0.715240830688
#City
predictions_city = cross_val_predict(model_lin_city, X_city, y, cv=5)
plt.scatter(y, predictions_city)
plt.xlabel('Actual')
plt.ylabel('Predicted')
accuracy_city = metrics.r2_score(y, predictions_city)
print("Cross Predicted Accuracy:", accuracy_city)
Cross Predicted Accuracy: -2.77554534782e+13
#Zip Code
predictions_zip = cross_val_predict(model_lin_zip, X_zip, y, cv=5)
plt.scatter(y, predictions_zip)
plt.xlabel('Actual')
plt.ylabel('Predicted')
accuracy_zip = metrics.r2_score(y, predictions_zip)
print("Cross Predicted Accuracy:", accuracy_zip)
Cross Predicted Accuracy: 0.716408801447
Present your conclusions and results. If you have more than one interesting model feel free to include more than one along with a discussion. Use your work in this notebook to prepare your write-up.
This report provides an analysis and evaluation of the 3 best location within Iowa to build a liquor store. The methods of analysis were putting together linear regression models and running them through a K folds cross validation test. The specific things we were interested in measuring was sale price, number of bottles sold, and bottle retail price. We then separated all variables by County, City, and Zip Code. After running the initial tests we decided to drop city and continue the test via Zip Code and County.
The top 5 counties with the best sales/ bottle ratio for their size were Dallas County, Carroll County, Sioux County, Iowa County, Howard County. The top five zip codes were 50266, 52338, 50320, 52154, 50021. Since zip code 52388 has a winery that is responsible for a majority of the sale we will be dropping it from this list which leaves us with the top three zip codes 50266, 50320, 52154. My first suggestion for a new liquor store would be in 50266.
Additional information that would be needed to perform a more detailed analysis and provide a more accurate location within the zip code would be demographics and population data. With the additional information we should be able to narrow down the location to within a few blocks.