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Multiple linear regression results:


Dependent Variable: y
Independent Variable(s): x1, x2, x3
y = 117.08469 + 4.334092 x1 + -2.8568479 x2 + -2.1860603 x3

Parameter estimates:


ParameterEstimateStd. Err.AlternativeDFT-StatP-value
Intercept117.0846999.782403 ≠ 0161.17340020.2578
x14.3340923.0155114 ≠ 0161.4372660.1699
x2-2.85684792.5820153 ≠ 016-1.10644110.2849
x3-2.18606031.595499 ≠ 016-1.3701420.1896

Analysis of variance table for multiple regression model:


SourceDFSSMSF-statP-value
Model3396.98461132.328221.515712<0.0001
Error1698.4048886.1503055
Total19495.3895

Summary of fit:


Root MSE: 2.4799809
R-squared: 0.8014
R-squared (adjusted): 0.7641
Multiple Linear Regression
56067136_ecollege_uarkssoDec 4, 20194KB8

Multiple linear regression results:


Dependent Variable: Height
Independent Variable(s): Father's Height, Mother's Height, Shoe Size (mens), Handspan (cm), Wingspan (in)
Height = 17.281409 + 0.22037954 Father's Height + 0.17880911 Mother's Height + 0.79440418 Shoe Size (mens) + 0.20477103 Handspan (cm) + 0.18988829 Wingspan (in)

Parameter estimates:


ParameterEstimateStd. Err.AlternativeDFT-StatP-value
Intercept17.28140912.180321 ≠ 0291.41879750.1666
Father's Height0.220379540.11787872 ≠ 0291.86954470.0717
Mother's Height0.178809110.12442521 ≠ 0291.4370810.1614
Shoe Size (mens)0.794404180.15923442 ≠ 0294.9888972<0.0001
Handspan (cm)0.204771030.12144177 ≠ 0291.68616640.1025
Wingspan (in)0.189888290.094244867 ≠ 0292.01483960.0533

Analysis of variance table for multiple regression model:


SourceDFSSMSF-statP-value
Model5393.1169678.62339321.931264<0.0001
Error29103.964753.5849914
Total34497.08171

Summary of fit:


Root MSE: 1.8934073
R-squared: 0.7908
R-squared (adjusted): 0.7548
Multiple Linear Regression
6c0adecae08ec5d60d2b2991f149e71b728370ec_canvas_miamioxNov 21, 20195KB16

Multiple linear regression results:


Dependent Variable: Salary (Y)
Independent Variable(s): Height (X1), Weight (X2), Field Percentage (X3), Points Per Game (X4)
Salary (Y) = -6772409.5 + 65291.622 Height (X1) + 11921.006 Weight (X2) + -49953.819 Field Percentage (X3) + 877465.56 Points Per Game (X4)

Parameter estimates:


ParameterEstimateStd. Err.AlternativeDFT-StatP-value
Intercept-6772409.553625432 ≠ 035-0.1262910.9002
Height (X1)65291.622733213.41 ≠ 0350.0890485920.9296
Weight (X2)11921.00670164.62 ≠ 0350.169900520.8661
Field Percentage (X3)-49953.819126331.71 ≠ 035-0.395417890.6949
Points Per Game (X4)877465.56211840.83 ≠ 0354.14209850.0002

Analysis of variance table for multiple regression model:


SourceDFSSMSF-statP-value
Model41.5878298e153.9695746e145.28856390.0019
Error352.6270858e157.5059595e13
Total394.2149157e15

Summary of fit:


Root MSE: 8663694.1
R-squared: 0.3767
R-squared (adjusted): 0.3055
Residuals stored in new column: Residuals
Studentized residuals stored in new column: Stud. Resid.
Predicted values stored in new column: Pred. Values
Standard error for mean response stored in new column: Std. Err. Mean
95% lower limit for mean response stored in new column: 95% L. Limit Mean
95% upper limit for mean response stored in new column: 95% U. Limit Mean
Standard error for individual prediction stored in new column: Std. Err. Ind. Pred.
95% lower limit for individual prediction stored in new column: 95% L. Limit Ind.
95% upper limit for individual prediction stored in new column: 95% U. Limit Ind.
Cook's distance stored in new column: Cook's D
DFFITS stored in new column: DFFITS
Leverage stored in new column: Leverage
Multiple Linear Regression
ltt0006Nov 18, 20195KB5

Multiple linear regression results:


Dependent Variable: Salary (Y)
Independent Variable(s): Points Per Game (X4), Warriors, Pelicans, Height (X1), Weight (X2), Field Percentage (X3)
Salary (Y) = -14250408 + 982872.96 Points Per Game (X4) + -1191433.4 Warriors + -5352685.2 Pelicans + 166084.74 Height (X1) + 16849.349 Weight (X2) + -68808.982 Field Percentage (X3)

Parameter estimates:


ParameterEstimateStd. Err.AlternativeDFT-StatP-value
Intercept-1425040853993884 ≠ 033-0.263926330.7935
Points Per Game (X4)982872.96222049.85 ≠ 0334.4263618<0.0001
Warriors-1191433.43483223 ≠ 033-0.342049140.7345
Pelicans-5352685.23649710.2 ≠ 033-1.46660560.1519
Height (X1)166084.74739380.01 ≠ 0330.224627030.8237
Weight (X2)16849.34972692.864 ≠ 0330.231788210.8181
Field Percentage (X3)-68808.982131474.82 ≠ 033-0.523362450.6042

Analysis of variance table for multiple regression model:


SourceDFSSMSF-statP-value
Model61.7603392e152.9338987e143.9444140.0044
Error332.4545764e157.4381104e13
Total394.2149157e15

Summary of fit:


Root MSE: 8624448.1
R-squared: 0.4176
R-squared (adjusted): 0.3118
Residuals stored in new column: Residuals
Studentized residuals stored in new column: Stud. Resid.
Predicted values stored in new column: Pred. Values
Standard error for mean response stored in new column: Std. Err. Mean
95% lower limit for mean response stored in new column: 95% L. Limit Mean
95% upper limit for mean response stored in new column: 95% U. Limit Mean
Standard error for individual prediction stored in new column: Std. Err. Ind. Pred.
95% lower limit for individual prediction stored in new column: 95% L. Limit Ind.
95% upper limit for individual prediction stored in new column: 95% U. Limit Ind.
Cook's distance stored in new column: Cook's D
DFFITS stored in new column: DFFITS
Leverage stored in new column: Leverage
Multiple Linear Regression
ltt0006Nov 18, 20196KB3

Multiple linear regression results:


Dependent Variable: Salary (Y)
Independent Variable(s): Points Per Game (X4), Warriors, Pelicans, Height (X1), Weight (X2), Field Percentage (X3)
Salary (Y) = -14250408 + 982872.96 Points Per Game (X4) + -1191433.4 Warriors + -5352685.2 Pelicans + 166084.74 Height (X1) + 16849.349 Weight (X2) + -68808.982 Field Percentage (X3)

Parameter estimates:


ParameterEstimateStd. Err.AlternativeDFT-StatP-value
Intercept-1425040853993884 ≠ 033-0.263926330.7935
Points Per Game (X4)982872.96222049.85 ≠ 0334.4263618<0.0001
Warriors-1191433.43483223 ≠ 033-0.342049140.7345
Pelicans-5352685.23649710.2 ≠ 033-1.46660560.1519
Height (X1)166084.74739380.01 ≠ 0330.224627030.8237
Weight (X2)16849.34972692.864 ≠ 0330.231788210.8181
Field Percentage (X3)-68808.982131474.82 ≠ 033-0.523362450.6042

Analysis of variance table for multiple regression model:


SourceDFSSMSF-statP-value
Model61.7603392e152.9338987e143.9444140.0044
Error332.4545764e157.4381104e13
Total394.2149157e15

Summary of fit:


Root MSE: 8624448.1
R-squared: 0.4176
R-squared (adjusted): 0.3118
Residuals stored in new column: Residuals
Studentized residuals stored in new column: Stud. Resid.
Predicted values stored in new column: Pred. Values
Standard error for mean response stored in new column: Std. Err. Mean
95% lower limit for mean response stored in new column: 95% L. Limit Mean
95% upper limit for mean response stored in new column: 95% U. Limit Mean
Standard error for individual prediction stored in new column: Std. Err. Ind. Pred.
95% lower limit for individual prediction stored in new column: 95% L. Limit Ind.
95% upper limit for individual prediction stored in new column: 95% U. Limit Ind.
Cook's distance stored in new column: Cook's D
DFFITS stored in new column: DFFITS
Leverage stored in new column: Leverage
Multiple Linear Regression
ltt0006Nov 18, 20196KB6

Multiple linear regression results:


Dependent Variable: GIFTAMNT
Independent Variable(s): HOMEOWNER, MALEVET, VIETVETS, WWIIVETS, LOCALGOV, STATEGOV, FEDGOV, AGE

Stepwise results:


P-value to enter: 0.15
P-value to leave: 0.25
StepVariableActionP-valueRMSER-squaredR-squared (adj)
1AGEEntered0.018212.0216050.00150.0013

GIFTAMNT = 17.57981 + -0.029877556 AGE

Parameter estimates:


ParameterEstimateStd. Err.DF95% L. Limit95% U. Limit
Intercept17.579810.81351162364615.98482719.174792
AGE-0.0298775560.0126487773646-0.054676934-0.0050781786

Analysis of variance table for multiple regression model:


SourceDFSSMSF-statP-value
Model1806.33955806.339555.57947160.0182
Error3646526916.2144.51898
Total3647527722.54

Summary of fit:


Root MSE: 12.021605
R-squared: 0.0015
R-squared (adjusted): 0.0013
Multiple Linear Regression
fa34e1a8-5f11-4da0-904a-7866a4faf313-6479_d2l_snhumlpOct 13, 20194KB19

Multiple linear regression results:


Dependent Variable: GIFTAMNT
Independent Variable(s): HOMEOWNER, NUMPROM, NGIFTALL, LASTGIFT, AVGGIFT
GIFTAMNT = 4.1704835 + -0.67343903 HOMEOWNER + 0.025644445 NUMPROM + -0.12885381 NGIFTALL + 0.63037978 LASTGIFT + 0.21911356 AVGGIFT

Parameter estimates:


ParameterEstimateStd. Err.AlternativeDFT-StatP-value
Intercept4.17048350.46627484 ≠ 036428.9442602<0.0001
HOMEOWNER-0.673439030.2888917 ≠ 03642-2.33111240.0198
NUMPROM0.0256444450.0098434731 ≠ 036422.60522320.0092
NGIFTALL-0.128853810.025114959 ≠ 03642-5.1305604<0.0001
LASTGIFT0.630379780.019765431 ≠ 0364231.893045<0.0001
AVGGIFT0.219113560.02826951 ≠ 036427.750879<0.0001

Analysis of variance table for multiple regression model:


SourceDFSSMSF-statP-value
Model5284909.2156981.841854.68068<0.0001
Error3642242813.3366.670328
Total3647527722.54

Summary of fit:


Root MSE: 8.16519
R-squared: 0.5399
R-squared (adjusted): 0.5393
Multiple Linear Regression
c54c1aad-e74b-47c9-bd9d-0118ff401a0a-145279_d2l_snhumlpJul 28, 20195KB38

Multiple linear regression results:


Dependent Variable: var1
Independent Variable(s): var2, var3, var4, var5, var6, var7

All subsets:


VariablesNumberRMSER-squaredR-squared (adj)Rank
var2, var3, var4, var5, var6, var760.0573242250.5920.54672
var2, var3, var4, var5, var650.0570331490.58870.55131
var2, var4, var5, var6, var750.0585248990.56690.52754
var3, var4, var5, var6, var750.0597826930.54810.5075
var2, var3, var4, var6, var750.0608151370.53230.48989
var2, var3, var4, var5, var750.0627620430.50190.456614
var2, var3, var5, var6, var750.0657314730.45370.40421
var2, var4, var5, var640.0583091370.56230.5313
var3, var4, var5, var640.0600761590.53530.50216
var2, var3, var4, var640.0603269490.53140.4987
var2, var4, var6, var740.06101310.52070.486510
var4, var5, var6, var740.0619586560.50580.470411
var2, var3, var4, var540.0623287870.49980.464112
var2, var4, var5, var740.0634533060.48160.444616
var2, var3, var4, var740.065210090.45250.413420
var3, var4, var5, var740.0662066580.43570.395322
var2, var5, var6, var740.0663616370.4330.392523
var2, var3, var5, var640.0667138880.4270.38624
var3, var4, var6, var740.0671222620.41990.378526
var3, var5, var6, var740.0676768330.41030.368229
var2, var3, var5, var740.0692477980.38260.338534
var2, var3, var6, var740.0708777180.35320.30740
var2, var4, var630.0605755880.51910.49388
var4, var5, var630.0625298740.48760.460613
var2, var4, var530.063078040.47860.451115
var2, var3, var430.0646606930.45210.423317
var2, var4, var730.0651865770.44310.413819
var3, var5, var630.0671148840.40970.378625
var2, var5, var630.0674609210.40360.372227
var3, var4, var530.0675073690.40280.371428
var4, var5, var730.0678533280.39660.364930
var4, var6, var730.0678573470.39660.364831
var5, var6, var730.0691100110.37410.341233
var2, var5, var730.0695750660.36560.332336
var2, var3, var530.0698023530.36150.327937
var2, var6, var730.0705402530.34790.313639
var2, var3, var630.0713290070.33330.298242
var3, var5, var730.0720584970.31950.283744
var3, var4, var730.0724728140.31170.275545
var2, var3, var730.0732548770.29680.259851
var3, var4, var630.0745190220.27230.23453
var3, var6, var730.0772742430.21750.176355
var2, var420.0646735740.44230.42318
var5, var620.0685116560.37410.352532
var4, var520.0695030250.35580.333635
var2, var520.0702375640.34220.319538
var2, var620.0710786240.32630.303141
var3, var520.0715729770.31690.293443
var2, var720.0728428550.29250.268146
var4, var720.072942650.29050.26648
var5, var720.0731917370.28570.26150
var2, var320.0734443770.28070.255952
var4, var620.0751672140.24660.220654
var6, var720.0773045440.20310.175656
var3, var620.0797333650.15230.12357
var3, var720.0804855370.13620.106460
var3, var420.0825542880.09120.059961
var510.0728545510.280.267847
var210.0731064420.2750.262749
var610.0798568140.1350.120358
var710.0804150870.12280.10859
var410.0828926920.0680.052262
var310.0851275520.0170.000463


Results for model with maximum adjusted R-squared:


var1 = 0.013735833 + 0.00097342053 var2 + 0.00024341648 var3 + -0.0026060199 var4 + 0.0022739525 var5 + 0.18757069 var6

Parameter estimates:


ParameterEstimateStd. Err.AlternativeDFT-StatP-value
Intercept0.0137358330.0336826 ≠ 0550.407802050.685
var20.000973420530.00036441619 ≠ 0552.6711780.0099
var30.000243416480.00012948824 ≠ 0551.87983470.0654
var4-0.00260601990.00056041262 ≠ 055-4.6501806<0.0001
var50.00227395250.00082187824 ≠ 0552.76677540.0077
var60.187570690.054414691 ≠ 0553.44705960.0011

Analysis of variance table for multiple regression model:


SourceDFSSMSF-statP-value
Model50.256054870.05121097315.743755<0.0001
Error550.17890290.0032527801
Total600.43495777

Summary of fit:


Root MSE: 0.057033149
R-squared: 0.5887
R-squared (adjusted): 0.5513
Multiple Linear Regression
56066897_ecollege_uarkssoApr 29, 201922KB65

Multiple linear regression results:


Dependent Variable: Obese
Independent Variable(s): Meat, Veggies, Seafood, Sweets, InsufAct, FoodCost%

All subsets:


VariablesNumberRMSER-squaredR-squared (adj)Rank
Meat, Veggies, Seafood, Sweets, InsufAct, FoodCost%60.0573242250.5920.54672
Meat, Veggies, Seafood, Sweets, InsufAct50.0570331490.58870.55131
Meat, Seafood, Sweets, InsufAct, FoodCost%50.0585248990.56690.52754
Veggies, Seafood, Sweets, InsufAct, FoodCost%50.0597826930.54810.5075
Meat, Veggies, Seafood, InsufAct, FoodCost%50.0608151370.53230.48989
Meat, Veggies, Seafood, Sweets, FoodCost%50.0627620430.50190.456614
Meat, Veggies, Sweets, InsufAct, FoodCost%50.0657314730.45370.40421
Meat, Seafood, Sweets, InsufAct40.0583091370.56230.5313
Veggies, Seafood, Sweets, InsufAct40.0600761590.53530.50216
Meat, Veggies, Seafood, InsufAct40.0603269490.53140.4987
Meat, Seafood, InsufAct, FoodCost%40.06101310.52070.486510
Seafood, Sweets, InsufAct, FoodCost%40.0619586560.50580.470411
Meat, Veggies, Seafood, Sweets40.0623287870.49980.464112
Meat, Seafood, Sweets, FoodCost%40.0634533060.48160.444616
Meat, Veggies, Seafood, FoodCost%40.065210090.45250.413420
Veggies, Seafood, Sweets, FoodCost%40.0662066580.43570.395322
Meat, Sweets, InsufAct, FoodCost%40.0663616370.4330.392523
Meat, Veggies, Sweets, InsufAct40.0667138880.4270.38624
Veggies, Seafood, InsufAct, FoodCost%40.0671222620.41990.378526
Veggies, Sweets, InsufAct, FoodCost%40.0676768330.41030.368229
Meat, Veggies, Sweets, FoodCost%40.0692477980.38260.338534
Meat, Veggies, InsufAct, FoodCost%40.0708777180.35320.30740
Meat, Seafood, InsufAct30.0605755880.51910.49388
Seafood, Sweets, InsufAct30.0625298740.48760.460613
Meat, Seafood, Sweets30.063078040.47860.451115
Meat, Veggies, Seafood30.0646606930.45210.423317
Meat, Seafood, FoodCost%30.0651865770.44310.413819
Veggies, Sweets, InsufAct30.0671148840.40970.378625
Meat, Sweets, InsufAct30.0674609210.40360.372227
Veggies, Seafood, Sweets30.0675073690.40280.371428
Seafood, Sweets, FoodCost%30.0678533280.39660.364930
Seafood, InsufAct, FoodCost%30.0678573470.39660.364831
Sweets, InsufAct, FoodCost%30.0691100110.37410.341233
Meat, Sweets, FoodCost%30.0695750660.36560.332336
Meat, Veggies, Sweets30.0698023530.36150.327937
Meat, InsufAct, FoodCost%30.0705402530.34790.313639
Meat, Veggies, InsufAct30.0713290070.33330.298242
Veggies, Sweets, FoodCost%30.0720584970.31950.283744
Veggies, Seafood, FoodCost%30.0724728140.31170.275545
Meat, Veggies, FoodCost%30.0732548770.29680.259851
Veggies, Seafood, InsufAct30.0745190220.27230.23453
Veggies, InsufAct, FoodCost%30.0772742430.21750.176355
Meat, Seafood20.0646735740.44230.42318
Sweets, InsufAct20.0685116560.37410.352532
Seafood, Sweets20.0695030250.35580.333635
Meat, Sweets20.0702375640.34220.319538
Meat, InsufAct20.0710786240.32630.303141
Veggies, Sweets20.0715729770.31690.293443
Meat, FoodCost%20.0728428550.29250.268146
Seafood, FoodCost%20.072942650.29050.26648
Sweets, FoodCost%20.0731917370.28570.26150
Meat, Veggies20.0734443770.28070.255952
Seafood, InsufAct20.0751672140.24660.220654
InsufAct, FoodCost%20.0773045440.20310.175656
Veggies, InsufAct20.0797333650.15230.12357
Veggies, FoodCost%20.0804855370.13620.106460
Veggies, Seafood20.0825542880.09120.059961
Sweets10.0728545510.280.267847
Meat10.0731064420.2750.262749
InsufAct10.0798568140.1350.120358
FoodCost%10.0804150870.12280.10859
Seafood10.0828926920.0680.052262
Veggies10.0851275520.0170.000463


Results for model with maximum adjusted R-squared:


Obese = 0.013735833 + 0.00097342053 Meat + 0.00024341648 Veggies + -0.0026060199 Seafood + 0.0022739525 Sweets + 0.18757069 InsufAct

Parameter estimates:


ParameterEstimateStd. Err.AlternativeDFT-StatP-value
Intercept0.0137358330.0336826 ≠ 0550.407802050.685
Meat0.000973420530.00036441619 ≠ 0552.6711780.0099
Veggies0.000243416480.00012948824 ≠ 0551.87983470.0654
Seafood-0.00260601990.00056041262 ≠ 055-4.6501806<0.0001
Sweets0.00227395250.00082187824 ≠ 0552.76677540.0077
InsufAct0.187570690.054414691 ≠ 0553.44705960.0011

Analysis of variance table for multiple regression model:


SourceDFSSMSF-statP-value
Model50.256054870.05121097315.743755<0.0001
Error550.17890290.0032527801
Total600.43495777

Summary of fit:


Root MSE: 0.057033149
R-squared: 0.5887
R-squared (adjusted): 0.5513
Residuals stored in new column: Residuals
Multiple Linear Regression
56066670_ecollege_uarkssoApr 25, 201923KB60

Multiple linear regression results:


Dependent Variable: Residuals
Independent Variable(s): 2016, 2019
Residuals = -47874.133 + -0.11229636 2016 + 1 2019

Parameter estimates:


ParameterEstimateStd. Err.AlternativeDFT-StatP-value
Intercept-47874.1331.1897923e-9 ≠ 02-4.0237388e13<0.0001
2016-0.112296367.0431931e-15 ≠ 02-1.5943957e13<0.0001
201912.4309636e-14 ≠ 024.1135952e13<0.0001

Analysis of variance table for multiple regression model:


SourceDFSSMSF-statP-value
Model223022715115113588.4608327e26<0.0001
Error22.7210933e-201.3605467e-20
Total423022715

Summary of fit:


Root MSE: 1.1664247e-10
R-squared: 1
R-squared (adjusted): 1
Residuals stored in new column: Residuals
Multiple Linear Regression
bcd9eac4-38da-4210-98be-740e75a6d779-47818_d2l_snhumlpApr 19, 20194KB60

Multiple linear regression results:


Dependent Variable: var4
Independent Variable(s): var1, var2, var3

All subsets:


VariablesNumberRMSER-squaredR-squared (adj)Rank
var1, var2, var332.47998090.80140.76411
var1, var322.49628150.78620.7612
var1, var222.5431660.77810.75194
var2, var322.55653470.77570.74935
var212.51024220.7710.75833
var112.81976850.71110.6956
var315.19261170.0203-0.03417


Results for model with maximum adjusted R-squared:


var4 = 117.08469 + 4.334092 var1 + -2.8568479 var2 + -2.1860603 var3

Parameter estimates:


ParameterEstimateStd. Err.AlternativeDFT-StatP-value
Intercept117.0846999.782403 ≠ 0161.17340020.2578
var14.3340923.0155114 ≠ 0161.4372660.1699
var2-2.85684792.5820153 ≠ 016-1.10644110.2849
var3-2.18606031.595499 ≠ 016-1.3701420.1896

Analysis of variance table for multiple regression model:


SourceDFSSMSF-statP-value
Model3396.98461132.328221.515712<0.0001
Error1698.4048886.1503055
Total19495.3895

Summary of fit:


Root MSE: 2.4799809
R-squared: 0.8014
R-squared (adjusted): 0.7641
Multiple Linear Regression
53673285_ecollege_uarkssoApr 19, 20196KB54

Multiple linear regression results:


Dependent Variable: var4
Independent Variable(s): var1, var2, var3

All subsets:


VariablesNumberRMSER-squaredR-squared (adj)Rank
var1, var2, var332.47998090.80140.76411
var1, var322.49628150.78620.7612
var1, var222.5431660.77810.75194
var2, var322.55653470.77570.74935
var212.51024220.7710.75833
var112.81976850.71110.6956
var315.19261170.0203-0.03417


Results for model with maximum adjusted R-squared:


var4 = 117.08469 + 4.334092 var1 + -2.8568479 var2 + -2.1860603 var3

Parameter estimates:


ParameterEstimateStd. Err.AlternativeDFT-StatP-value
Intercept117.0846999.782403 ≠ 0161.17340020.2578
var14.3340923.0155114 ≠ 0161.4372660.1699
var2-2.85684792.5820153 ≠ 016-1.10644110.2849
var3-2.18606031.595499 ≠ 016-1.3701420.1896

Analysis of variance table for multiple regression model:


SourceDFSSMSF-statP-value
Model3396.98461132.328221.515712<0.0001
Error1698.4048886.1503055
Total19495.3895

Summary of fit:


Root MSE: 2.4799809
R-squared: 0.8014
R-squared (adjusted): 0.7641
Multiple Linear Regression
53673285_ecollege_uarkssoApr 19, 20196KB57

Multiple linear regression results:


Dependent Variable: var1
Independent Variable(s): var2, var3, var4, var5
var1 = -18.981345 + -1.184609 var2 + 1.2042393 var3 + -0.08662215 var4 + 0.011361886 var5

Parameter estimates:


ParameterEstimateStd. Err.AlternativeDFT-StatP-value
Intercept-18.981345248.0125 ≠ 015-0.0765338250.94
var2-1.1846097.6422744 ≠ 015-0.155007390.8789
var31.20423936.3894333 ≠ 0150.188473570.853
var4-0.086622154.0223627 ≠ 015-0.0215351420.9831
var50.0113618860.5962575 ≠ 0150.0190553350.985

Analysis of variance table for multiple regression model:


SourceDFSSMSF-statP-value
Model4140.2219735.0554931.00200920.437
Error15524.7780334.985202
Total19665

Summary of fit:


Root MSE: 5.914829
R-squared: 0.2109
R-squared (adjusted): 0.0004
Multiple Linear Regression project
36686717_ecollege_uarkssoApr 17, 20194KB48

Multiple linear regression results:


Dependent Variable: PTS
Independent Variable(s): 3P%, FG%, FT%
PTS = -35.314109 + 2.2866363 3P% + 61.307925 FG% + 33.385445 FT%

Parameter estimates:


ParameterEstimateStd. Err.AlternativeDFT-StatP-value
Intercept-35.31410910.83819 ≠ 036-3.25830330.0024
3P%2.28663634.791553 ≠ 0360.477222370.6361
FG%61.30792512.617139 ≠ 0364.8590989<0.0001
FT%33.3854458.3310071 ≠ 0364.0073720.0003

Analysis of variance table for multiple regression model:


SourceDFSSMSF-statP-value
Model3198.5263766.1754579.050920.0001
Error36263.212637.3114619
Total39461.739

Summary of fit:


Root MSE: 2.7039715
R-squared: 0.43
R-squared (adjusted): 0.3824
Multiple Linear Regression
bms0062Apr 16, 20194KB57

Multiple linear regression results:


Dependent Variable: Time spent on social media/ day
Independent Variable(s): Age, Weight
Time spent on social media/ day = 318.7384 + -15.734061 Age + 3.2930058 Weight

Parameter estimates:


ParameterEstimateStd. Err.AlternativeDFT-StatP-value
Intercept318.7384293.76396 ≠ 0221.08501530.2897
Age-15.73406114.371044 ≠ 022-1.09484470.2854
Weight3.29300581.1602285 ≠ 0222.8382390.0096

Analysis of variance table for multiple regression model:


SourceDFSSMSF-statP-value
Model245891.56822945.7844.03851640.0321
Error22124998.195681.736
Total24170889.76

Summary of fit:


Root MSE: 75.377291
R-squared: 0.2685
R-squared (adjusted): 0.202
Multiple Linear Regression=
34772087_ecollege_msumlpApr 16, 20193KB55

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