A multiple regression analysis between yearly income (y in $1000s), college grade point average (x1), age of the individuals (x2), and gender of the individual (3, 0 representing female and 1 representing male) was performed on a sample of 10 people, and the following results were obtained using Excel ANOVA Regression Residual Intercept X1 x2 x3 df Coefficients 4.0928 10.0230 0.1020 44811 $$ 360.59 23.91 Standard Error 1.4400 1.6512 0.1225 14400 MS F Write out the missing values in the estimated regression equation y hat X2 - X3 (Note. Use a minus sign if the coefficient is a negative number and use the exact number as in the table) X1+

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A multiple regression analysis between yearly income (y in $1000s), college grade point average (x1), age of the individuals (x2), and gender of the
individual (x3,0 representing female and 1 representing male) was performed on a sample of 10 people, and the following results were obtained using
Excel
ANOVA
Regression
Residual
Intercept
x1
12
x3
Coefficients Standard Error
4.0928
1.4400
1.6512
10.0230
0.1020
$$
360.59
23.91
44811
(0.1225
14400
MS
F
Write out the missing values in the estimated regression equation y hat
x2
x3
(Note. Use a minus sign if the coefficient is a negative number and use the exact number as in the table)
X1 →
Transcribed Image Text:A multiple regression analysis between yearly income (y in $1000s), college grade point average (x1), age of the individuals (x2), and gender of the individual (x3,0 representing female and 1 representing male) was performed on a sample of 10 people, and the following results were obtained using Excel ANOVA Regression Residual Intercept x1 12 x3 Coefficients Standard Error 4.0928 1.4400 1.6512 10.0230 0.1020 $$ 360.59 23.91 44811 (0.1225 14400 MS F Write out the missing values in the estimated regression equation y hat x2 x3 (Note. Use a minus sign if the coefficient is a negative number and use the exact number as in the table) X1 →
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