1. A regression of weekly shopping trip frequency on annual income (data entered in thousands of dollars) is performed on data collected from 24 respondents. The results are summarized below: Intercept 0.46 Slope 0.19 (Continued) Sum of squares df Mean square Regression Residual 1.7 Total 2.3 (a) Fill in the blanks in the ANOVA table.
1. A regression of weekly shopping trip frequency on annual income (data entered in thousands of dollars) is performed on data collected from 24 respondents. The results are summarized below: Intercept 0.46 Slope 0.19 (Continued) Sum of squares df Mean square Regression Residual 1.7 Total 2.3 (a) Fill in the blanks in the ANOVA table.
MATLAB: An Introduction with Applications
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Author:Amos Gilat
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
Transcribed Image Text:1. A regression of weekly shopping trip frequency on annual income (data entered in
thousands of dollars) is performed on data collected from 24 respondents. The
results are summarized below:
Intercept
0.46
Slope
0.19
(Continued)
Sum of squares
df
Mean square
Regression
Residual
1.7
Total
2.3
(a) Fill in the blanks in the ANOVA table.
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