2. Multivariate Regression. Now add to your above regression in a price variable, wit 8, 7.5, 7.25, 7.25, 6, 6.75, 6, 5, 4.4, 5.2. Estimate the new regression equation. Print re results. Write out the estimated demand function, as in 1 above. Equation: Std Errors R² =

Calculus For The Life Sciences
2nd Edition
ISBN:9780321964038
Author:GREENWELL, Raymond N., RITCHEY, Nathan P., Lial, Margaret L.
Publisher:GREENWELL, Raymond N., RITCHEY, Nathan P., Lial, Margaret L.
Chapter1: Functions
Section1.2: The Least Square Line
Problem 8E
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question one is answered need help with #2

Consider the following data
Sales
Adv
Y₁
X₁
mtontonage
INSoroad
3
4
6
5
7
6
5
9
10
9
1
2
3
4
5
6
7
8
9
10
1. Input this data on a spreadsheet. Using the regression option, generate regression
predictions. Write your results as an equation, as we did in class. In particular,
(a) Write the regression equation, with estimated coefficients,
(b) Below the regression equation, list in parentheses the standard errors of the coefficient
estimates
(c) To the right of the estimated equation write R² = .
Equation: Yi=2.734 + .667Xi
R² = .7575
Std Errors: (0.827) (0.134)
Transcribed Image Text:Consider the following data Sales Adv Y₁ X₁ mtontonage INSoroad 3 4 6 5 7 6 5 9 10 9 1 2 3 4 5 6 7 8 9 10 1. Input this data on a spreadsheet. Using the regression option, generate regression predictions. Write your results as an equation, as we did in class. In particular, (a) Write the regression equation, with estimated coefficients, (b) Below the regression equation, list in parentheses the standard errors of the coefficient estimates (c) To the right of the estimated equation write R² = . Equation: Yi=2.734 + .667Xi R² = .7575 Std Errors: (0.827) (0.134)
2. Multivariate Regression. Now add to your above regression in a price variable, with values:
8, 7.5, 7.25, 7.25, 6, 6.75, 6, 5, 4.4, 5.2. Estimate the new regression equation. Print regression
results. Write out the estimated demand function, as in 1 above.
Equation:
Std Errors
R²
=
Transcribed Image Text:2. Multivariate Regression. Now add to your above regression in a price variable, with values: 8, 7.5, 7.25, 7.25, 6, 6.75, 6, 5, 4.4, 5.2. Estimate the new regression equation. Print regression results. Write out the estimated demand function, as in 1 above. Equation: Std Errors R² =
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