The images provided are an Output from a Forward Regression model built on the Donations dataset using TargetB within Sas E Miner. Provide an explanation as to why the final model turned out this way. Explain the relationship between the input variables in the model and the target variable Analysis of Maximum Likelihood EstimatesStandardWaldStandardizedPr > ChisqParameterDFEstimateErrorChi-SquareEstimateExp (Est)Intercept1.1.94820.227773. 20<.00017.016FirstTime10.0000580.0000274.640.03130.03681.000LOG_AverageGift1-0. 43470.066842.33<.0001-0.11400.647LastTime-0.001950.00024165.25<.0001-0.13400.998Odds Ratio EstimatesPointEffectEstimateFirstTime1.000LOG_AverageGift0.647LastTime0.998 Summary of Forward SelectionEffectNumberScoreValidationStepEnteredDFInChi-SquarePr > ChisqProfitLastTime82.7504<.0001-264.32LOG_AverageGift260.1450<.0001-212.713FirstTime134.64060.0312-194. 4The selected nodel, based on the total profit for the validation data, is the model trained in Step 3. It consists of the following effects:Intercept FirstTime LOG_AverageGift LastTineLikelihood Ratio Test for Global Null Hypothesis: BETA=0-2 Log LikelihoodLikelihoodInterceptIntercept aRatioOnlyPr > ChisqCovariatesChi-SquareDF6758.0586608.891149.16673<.0001Type 3 Analysis of EffectsWaldEffectDFChi-SquarePr > ChisqFirstTime4.63770.0313LOG_AverageGift42.3317<.0001LastTime165.2528<.0001

Given information: The output of multiple linear regression model is given.

Answered: The images provided are an Output from…

The images provided are an Output from a Forward Regression model built on the Donations dataset using TargetB within Sas E Miner. Provide an e

A First Course in Probability (10th Edition)

10th Edition

ISBN:9780134753119

Author:Sheldon Ross

Publisher:Sheldon Ross

Chapter1: Combinatorial Analysis

Section: Chapter Questions

Problem 1.1P: a. How many different 7-place license plates are possible if the first 2 places are for letters and...

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Question

The images provided are an Output from a Forward Regression model built on the Donations dataset using TargetB within Sas E Miner.

Provide an explanation as to why the final model turned out this way.

Explain the relationship between the input variables in the model and the target variable

Analysis of Maximum Likelihood Estimates
Standard
Wald
Standardized
Pr > Chisq
Parameter
DF
Estimate
Error
Chi-Square
Estimate
Exp (Est)
Intercept
1.
1.9482
0.2277
73. 20
<.0001
7.016
FirstTime
1
0.000058
0.000027
4.64
0.0313
0.0368
1.000
LOG_AverageGift
1
-0. 4347
0.0668
42.33
<.0001
-0.1140
0.647
LastTime
-0.00195
0.000241
65.25
<.0001
-0.1340
0.998
Odds Ratio Estimates
Point
Effect
Estimate
FirstTime
1.000
LOG_AverageGift
0.647
LastTime
0.998

Summary of Forward Selection
Effect
Number
Score
Validation
Step
Entered
DF
In
Chi-Square
Pr > Chisq
Profit
LastTime
82.7504
<.0001
-264.3
2
LOG_AverageGift
2
60.1450
<.0001
-212.7
1
3
FirstTime
1
3
4.6406
0.0312
-194. 4
The selected nodel, based on the total profit for the validation data, is the model trained in Step 3. It consists of the following effects:
Intercept FirstTime LOG_AverageGift LastTine
Likelihood Ratio Test for Global Null Hypothesis: BETA=0
-2 Log Likelihood
Likelihood
Intercept
Intercept a
Ratio
Only
Pr > Chisq
Covariates
Chi-Square
DF
6758.058
6608.891
149.1667
3
<.0001
Type 3 Analysis of Effects
Wald
Effect
DF
Chi-Square
Pr > Chisq
FirstTime
4.6377
0.0313
LOG_AverageGift
42.3317
<.0001
LastTime
1
65.2528
<.0001

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