Question:1) Using test data on 20 types of laundry detergent, an analyst fitted a regression to predict CostPerLoad (average cost per load in cents per load) using binary predictors TopLoad (1 if washer is a top-loading model, 0 otherwise) and Powder (if detergent was in powder form, 0 otherwise). Interpret the results. Give 10 points.
Question:1) Using test data on 20 types of laundry detergent, an analyst fitted a regression to predict CostPerLoad (average cost per load in cents per load) using binary predictors TopLoad (1 if washer is a top-loading model, 0 otherwise) and Powder (if detergent was in powder form, 0 otherwise). Interpret the results. Give 10 points.
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
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
Transcribed Image Text:Question:1) Using test data on 20 types of laundry detergent, an analyst fitted a regression
to predict Cost PerLoad (average cost per load in cents per load) using binary predictors
TopLoad (1 if washer is a top-loading model, 0 otherwise) and Powder (if detergent was in
powder form, 0 otherwise). Interpret the results. Give 10 points.
R²
Adjusted R²
R
Std. Error
ANOVA table
Source
Regression
Residual
Total
Regression output
variables
Intercept
Top-Load
Powder
0.117
0.006
0.341
5.915
coefficients
26.0000
-6.3000
-0.2714
SS
73.8699
559.8143
633.6842 18
n 19
k 2
Dep. Var. Cost Per Load
df MS
2 36.9350
16 34.9884
std. error t (df = 16)
4.1826
6.216
4.5818
-1.375
2.9150
-0.093
p-value
1.23E-05
.1881
.9270
F
1.06
confidence interval
95% lower
p-value
.3710
17.1333
-16.0130
-6.4509
95% upper
34.8667
3.4130
5.9081
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