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Case 49

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Descriptive Statistics for Crimes

Variable N N* Mean SE Mean StDev Minimum Median Maximum Range Mode
CRIMES 50 0 4559 174 1232 2107 4366 7820 5713 5705.7

N for
Variable Mode
CRIMES 2

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One-Sample Z

The assumed standard deviation = 1232

N Mean SE Mean 95% CI
50 4559 174 (4218, 4900)

One-Sample T

N Mean StDev SE Mean 95% CI
50 4559 1232 174 (4209, 4909)

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Regression Analysis: CRIMES versus URBAN

The regression equation is
CRIMES = 737.0 + 57.18 URBAN

S = 917.074 R-Sq = 45.7% R-Sq(adj) = 44.6%

Analysis of Variance

Source DF SS MS F P
Regression 1 33997185 33997185 40.42 0.000
Error 48 40369214 841025
Total 49 74366399

Regression Analysis: CRIMES versus PINCOME

The regression equation is
CRIMES = 2650 + 0.124 PINCOME

Predictor Coef SE Coef T P
Constant 2649.8 964.0 2.75 0.008
PINCOME 0.12365 0.06146 2.01 0.050

S = 1195.33 R-Sq = 7.8% R-Sq(adj) = 5.9%

Analysis of Variance

Source DF SS MS F P
Regression 1 5783269 5783269 4.05 0.050
Residual Error 48 68583130 1428815
Total 49 74366399

Unusual Observations

Obs PINCOME CRIMES Fit SE Fit Residual St Resid 3 14887 6861 4491 172 2371 2.00R 7 22761 4642 5464 481 -822 -0.75 X 9 16546 7820 4696 182 3124 2.64R 30 21882 4713 5356 430 -643 -0.58 X 43 14640 7365 4460 176 2905 2.46R

R denotes an observation with a large standardized residual.
X denotes an observation whose X value gives it large leverage.

Regression Analysis: CRIMES versus DROPOUT

The regression equation is
CRIMES = 2833 + 71.7 DROPOUT

Predictor Coef SE Coef T P
Constant 2832.6 577.5 4.90 0.000
DROPOUT 71.72 23.04 3.11 0.003

S = 1135.38 R-Sq = 16.8% R-Sq(adj) = 15.1%

Analysis of Variance

Source DF SS MS F P
Regression 1 12489680 12489680 9.69 0.003
Residual Error 48 61876719 1289098
Total 49 74366399

Unusual Observations

Obs DROPOUT CRIMES Fit SE Fit Residual St Resid 9 36.5 7820 5450 328 2370 2.18R 17 32.1 2805 5135 245 -2330 -2.10R 48 22.9 2107 4475 163 -2368 -2.11R

R denotes an observation with a large standardized residual.

Regression Analysis: CRIMES versus UNEMPLOY

The regression equation is
CRIMES = 4364 + 35.5 UNEMPLOY

Predictor Coef SE Coef T P
Constant 4364.2 539.8 8.08 0.000
UNEMPLOY 35.55 93.00 0.38 0.704

S = 1242.82 R-Sq = 0.3% R-Sq(adj) = 0.0%

Analysis of Variance

Source DF SS MS F P
Regression 1 225660 225660 0.15 0.704
Residual Error 48 74140739 1544599
Total 49 74366399

Unusual Observations

Obs UNEMPLOY CRIMES Fit SE Fit Residual St Resid 9 5.0 7820 4542 182 3278 2.67R 18 10.9 5043 4752 533 292 0.26 X 43 7.3 7365 4624 243 2741 2.25R 48 9.9 2107 4716 446 -2609 -2.25RX

R denotes an observation with a large standardized residual.
X denotes an observation whose X value gives it large leverage.

General Regression Analysis: CRIMES versus PINCOME, DROPOUT, PUBAID, KIDS, ...

Regression Equation

CRIMES = 578.407 - 0.104278 PINCOME + 84.142 DROPOUT - 200.182 PUBAID + 2.00721 KIDS + 36.4708 UNEMPLOY + 56.6547 URBAN

Coefficients

Term Coef SE Coef T P
Constant 578.407 978.813 0.59093 0.558
PINCOME -0.104 0.071 -1.47660 0.147
DROPOUT 84.142 21.719 3.87412 0.000
PUBAID -200.182 72.884 -2.74658 0.009
KIDS 2.007 1.455 1.37928 0.175
UNEMPLOY 36.471 78.681 0.46353 0.645
URBAN 56.655 9.840 5.75736 0.000

Summary of Model

S = 798.032 R-Sq = 63.18% R-Sq(adj) = 58.04%
PRESS = 38368077 R-Sq(pred) = 48.41%

Analysis of Variance

Source DF Seq SS Adj SS Adj MS F P
Regression 6 46981622 46981622 7830270 12.2952 0.000000 PINCOME 1 5783269 1388565 1388565 2.1803 0.147070 DROPOUT 1 12862014 9558418 9558418 15.0088 0.000360 PUBAID 1 4797787 4804235 4804235 7.5437 0.008760 KIDS 1 1372113 1211566 1211566 1.9024 0.174940 UNEMPLOY 1 1056475 136833 136833 0.2149 0.645325 URBAN 1 21109963 21109963 21109963 33.1472 0.000001
Error 43 27384777 27384777 636855
Total 49 74366399

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