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 SweetnessBrandLiking = 68.62 + 4.38 SweetnessTerm 95% CI P-ValueConstant (50.16, 87.09) 0.000Sweetness (-1.46, 10.21) 0.130S R-sq R-sq(adj)10.8915 15.57% 9.54% BrandLiking= 37.65 + 4.425 Moisture + 4.375 SweetnessCoefficientsTermCoef SE Coef 95% CIConstant37.653.00 (31.18, 44.12)Moisture 4.425 0.301 (3.774, 5.076)Sweetness 4.3750.673 (2.920, 5.830)Model SummaryS R-sq R-sq(adj)2.69330 95.21%T-Value P-Value12.570.00014.700.0001.006.500.000 1.0094.47% 148.378VIFPRESS R-sq(pred) AICC BIC92.46% 85.42 84.88 BrandLiking = 50.78 + 4.425 MoistureCoefficientsTermCoef SE Coef 95% CI4.39 (41.35, 60.20)Constant 50.78Moisture 4.425 0.598 (3.142, 5.708)Model SummaryT-Value P-Value VIF11.550.0007.400.000 1.00S R-sq R-sq(adj) PRESS R-sq(pred)5.34890 79.64% 78.18% 536.698AICCBIC72.71% 104.93 105.25

Given three regression output for Brand liking with respect to independent variables sweetness,…

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%

Glencoe Algebra 1, Student Edition, 9780079039897, 0079039898, 2018

18th Edition

ISBN:9780079039897

Author:Carter

Publisher:Carter

Chapter10: Statistics

Section10.1: Measures Of Center

Problem 3CYU

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