3 Model Assessment

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School

University of Massachusetts, Amherst *

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297A

Subject

Marketing

Date

Apr 3, 2024

Type

pptx

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Uploaded by DeanEnergy11850

Assessing the Fit of the model 3 4 5 6 7 8 9 10 0 20 40 60 80 100 120 Sales 3 4 5 6 7 8 9 10 0 20 40 60 80 100 120 Sales 3 4 5 6 7 8 9 10 0 20 40 60 80 100 120 Sales A A C C B B Which model works best? Which worst? Which model works best? Which worst? the percentage of the total variation in y that can be explained by using the estimated regression equation. RMSE: root mean square error RMSE = R2=0.664 RMSE=10.76 R2=1 RMSE=0 R2=0.46 RMSE=17.18

Assessing the Fit of the model 3 4 5 6 7 8 9 10 0 20 40 60 80 100 120 Sales 3 4 5 6 7 8 9 10 0 20 40 60 80 100 120 Sales 3 4 5 6 7 8 9 10 0 20 40 60 80 100 120 Sales A A C C B B A: 66.4% of the variation in sales could be explained by advertising expenditure. R2=0.664 RMSE=10.76 R2=1 RMSE=0 R2=0.46 RMSE=17.18

Coefficients Predictor Estimate Confidence Interval: Lower Confidence Interval: Upper Standard Error T-Statistic P-Value Intercept 14.4016736 -24.63636291 53.43971019 16.92886667 0.850716939 0.4196588 Advertising Expenditures 9.79079498 4.113398391 15.46819157 2.462006248 3.976754725 0.0040802 ANOVA Source DF SS MS F-Statistic P-Value Regression 1 2291.046025 2291.046025 15.81457814 0.004080177 Error 8 1158.953975 144.8692469 N/A N/A Total 9 3450 383.3333333 N/A N/A P-value is the probability that the coefficient is 0. If p-value is less than 0.05 (5%), then the coefficient is significant. There is an impact of this variable on the dependent variable. You could explain the coefficient. Otherwise, there is no impact of the variable. If the p-value of Advertising expenditures is 0.6, then there is no impact of advertising expenditures on sales. If the p-value of Advertising expenditures is 0.6, then there is no impact of advertising expenditures on sales. coefficient coefficient Check p-value first Check p-value first

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3 Model Assessment

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