Question

A credit card company is trying to understand and even predict annual charges that a customer will make on their cards using 5 variables: annual income (in $1000), household size, years of post-high school education (labeled simply "Education"), hours per week watching television (labeled "TV Hours") and age of the primary cardholder. They collect data on 100 randomly selected customers and measure the variables described above. They then perform a multiple regression analysis and find the results shown in the questions below. SUMMARY OUTPUT Regression Statistics Multiple R 0.507 R Square 0.257 Adjusted R Square 0.217 Standard Error 5319.659 Observations 100 ANOVA df SS MS F Significance F Regression 5 918149733.6 183629946.7 6.489 3.175E-05 Residual 94 2660085040 28298777.02 Total 99 3578234773 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept 9714.244 3289.053 2.954 0.0040 3183.751 16244.736 Annual Income ($1000) 87.421 18.063 4.840 5.082E-06 51.557 123.284 Household Size -145.809 270.677 -0.539 0.5914 -683.244 391.627 Education -676.427 335.456 -2.016 0.0466 -1342.483 -10.371 TV Hours 42.242 31.228 1.353 0.1794 -19.762 104.247 Age -72.153 52.101 -1.385 0.1694 -175.601 31.295 A. Develop the estimated regression equation relating all of the independent variables included in the data to annual charges. If required, round your answer to three decimal places. For subtractive or negative numbers use a minus sign even if there is a + sign before the blank. (Example: -300) Estimated Amount Charged = Intercept + Coefficient1 * Annual Income ($1000s) + Coefficient2 * Household Size + Coefficient3 * Education + Coefficient4 * TV Hours + Coefficient5 * Age

          A credit card company is trying to understand and even predict annual charges that a customer will make on their cards using 5 variables: annual income (in $1000), household size, years of post-high school education (labeled simply "Education"), hours per week watching television (labeled "TV Hours") and age of the primary cardholder.
They collect data on 100 randomly selected customers and measure the variables described above. They then perform a multiple regression analysis and find the results shown in the questions below.

SUMMARY OUTPUT
Regression Statistics
Multiple R
0.507
R Square
0.257
Adjusted R Square
0.217
Standard Error
5319.659
Observations
100
ANOVA
df
SS
MS
F
Significance F
Regression
5
918149733.6
183629946.7
6.489
3.175E-05
Residual
94
2660085040
28298777.02
Total
99
3578234773
Coefficients
Standard Error
t Stat
P-value
Lower 95%
Upper 95%
Intercept
9714.244
3289.053
2.954
0.0040
3183.751
16244.736
Annual Income ($1000)
87.421
18.063
4.840
5.082E-06
51.557
123.284
Household Size
-145.809
270.677
-0.539
0.5914
-683.244
391.627
Education
-676.427
335.456
-2.016
0.0466
-1342.483
-10.371
TV Hours
42.242
31.228
1.353
0.1794
-19.762
104.247
Age
-72.153
52.101
-1.385
0.1694
-175.601
31.295

A. Develop the estimated regression equation relating all of the independent variables included in the data to annual charges. If required, round your answer to three decimal places. For subtractive or negative numbers use a minus sign even if there is a + sign before the blank. (Example: -300)
Estimated Amount Charged = Intercept + Coefficient1 * Annual Income ($1000s) + Coefficient2 * Household Size + Coefficient3 * Education + Coefficient4 * TV Hours + Coefficient5 * Age

Added by Angela S.

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