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Fit these three regression models and then discuss the similarities and differences between them, particularly as relates to slope estimates (use CI’s) and R2. Also address why this is a “special case” and we wouldn’t necessarily expect to see these model characteristics for a typical dataset. a) Additive model including both predictors (output attached) b) Model including only Moisture (output attached) c) Model including only Sweetness BrandLiking = 68.62 + 4.38 Sweetness Term                     95% CI                     P-Value Constant          (50.16, 87.09)                 0.000 Sweetness       (-1.46, 10.21)                   0.130 S                            R-sq                     R-sq(adj) 10.8915                15.57%                    9.54%

Fit these three regression models and then discuss the similarities and differences between them, particularly as relates to slope estimates (use CI’s) and R2. Also address why this is a “special case” and we wouldn’t necessarily expect to see these model characteristics for a typical dataset. a) Additive model including both predictors (output attached) b) Model including only Moisture (output attached) c) Model including only Sweetness BrandLiking = 68.62 + 4.38 Sweetness Term                     95% CI                     P-Value Constant          (50.16, 87.09)                 0.000 Sweetness       (-1.46, 10.21)                   0.130 S                            R-sq                     R-sq(adj) 10.8915                15.57%                    9.54%

MATLAB: An Introduction with Applications
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
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  1. Fit these three regression models and then discuss the similarities and differences between them, particularly as relates to slope estimates (use CI’s) and R2. Also address why this is a “special case” and we wouldn’t necessarily expect to see these model characteristics for a typical dataset.

a) Additive model including both predictors (output attached)

b) Model including only Moisture (output attached)

c) Model including only Sweetness

BrandLiking = 68.62 + 4.38 Sweetness

Term                     95% CI                     P-Value
Constant          (50.16, 87.09)                 0.000
Sweetness       (-1.46, 10.21)                   0.130

S                            R-sq                     R-sq(adj)
10.8915                15.57%                    9.54%

 

 






 

 

 

BrandLiking = 37.65 + 4.425 Moisture + 4.375 Sweetness Coefficients Term Coef SE Coef 95% CI Constant 37.65 3.00 (31.18, 44.12) Moisture 4.425 0.301 (3.774, 5.076) Sweetness 4.375 0.673 (2.920, 5.830) Model Summary S R-sq R-sq(adj) 2.69330 95.21% T-Value P-Value 12.57 0.000 14.70 0.000 1.00 6.50 0.000 1.00 94.47% 148.378 VIF PRESS R-sq(pred) AICC BIC 92.46% 85.42 84.88
Transcribed Image Text:BrandLiking = 37.65 + 4.425 Moisture + 4.375 Sweetness Coefficients Term Coef SE Coef 95% CI Constant 37.65 3.00 (31.18, 44.12) Moisture 4.425 0.301 (3.774, 5.076) Sweetness 4.375 0.673 (2.920, 5.830) Model Summary S R-sq R-sq(adj) 2.69330 95.21% T-Value P-Value 12.57 0.000 14.70 0.000 1.00 6.50 0.000 1.00 94.47% 148.378 VIF PRESS R-sq(pred) AICC BIC 92.46% 85.42 84.88
BrandLiking = 50.78 + 4.425 Moisture Coefficients Term Coef SE Coef 95% CI 4.39 (41.35, 60.20) Constant 50.78 Moisture 4.425 0.598 (3.142, 5.708) Model Summary T-Value P-Value VIF 11.55 0.000 7.40 0.000 1.00 S R-sq R-sq(adj) PRESS R-sq(pred) 5.34890 79.64% 78.18% 536.698 AICC BIC 72.71% 104.93 105.25
Transcribed Image Text:BrandLiking = 50.78 + 4.425 Moisture Coefficients Term Coef SE Coef 95% CI 4.39 (41.35, 60.20) Constant 50.78 Moisture 4.425 0.598 (3.142, 5.708) Model Summary T-Value P-Value VIF 11.55 0.000 7.40 0.000 1.00 S R-sq R-sq(adj) PRESS R-sq(pred) 5.34890 79.64% 78.18% 536.698 AICC BIC 72.71% 104.93 105.25
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