Suppose we are working on the same data set. Two multiple linear regressions have been done with following
Printout, where “ “ means blank space.
Analysis of Variance Table 1
Response: y
Df Sum Sq Mean Sq F value Pr(>F)
x1 1 --------- ---------- -------- ------------- ***
x2 1 80.206 80.206 17.892 0.0001474 ***
Residuals 37 165.864 4.483
Analysis of Variance Table 2
Response: y
Df Sum Sq Mean Sq F value Pr(>F)
x1 1 --------- ----------- -------- --------------- ***
x2 1 80.206 80.206 1563.7 < 2.2e-16 ***
x3 1 --------- ----------- -------- ---------------
Residuals 36 1.847 0.051
a. How many observations we have in the data set?
b. Clearly the ANOVA Table 2 is associated with the regression model:
E[y|x]= β0+β1x1+β2x2+ β3x3. Suppose you want to test H0: β3=0. Please carry out an F-test?
c. For the model
E[y|x]= β0+β1x1+β2x2+ β3x3
Suppose you want to test H0: β1= β3=0.In order to carry out an F-test for that, what is the other regression
ANOVA table we need?
This question is mostly illegible, but i will still try to answer.
a.

b.
c.
You need the value of SS(X1) in order to carry out the F-test. If Total Sum Of Squares are given you could also use that to calculate SS(X1) and carry out F-test.
Suppose we are working on the same data set. Two multiple linear regressions have been done...
31. Suppose you fit a multiple linear regression model y = β0 + β1x1 + β2x2 + β3x3 + β4x4 + ε to n = 30 data points and obtain SSE = 282 and R^2 = 0.8266 a.) Find an estimate of s^2 for the multiple regression model (a) s^2 ≈ 30.9856 (b) s^2 ≈ 28.6021 (c) s^2 ≈ 1.3111 (d) s^2 ≈ 29.7938 (d) b.) Based on the data information given in a.), you use F-test to test H0...
Suppose you fit the multiple regression model y = β0 + β1x1 + β2x2 + ϵ to n = 30 data points and obtain the following result: y ̂=3.4-4.6x_1+2.7x_2+0.93x_3 The estimated standard errors of β ̂_2 and β ̂_3 are 1.86 and .29, respectively. Test the null hypothesis H0: β2 = 0 against the alternative hypothesis Ha: β2 ≠0. Use α = .05. Test the null hypothesis H0: β3 = 0 against the alternative hypothesis Ha: β3 ≠0. Use α...
Suppose you fit the multiple regression model y = β0 + β1x1 + β2x2 + ϵ to n = 30 data points and obtain the following result: y ̂=3.4-4.6x_1+2.7x_2+0.93x_3 The estimated standard errors of β ̂_2 and β ̂_3 are 1.86 and .29, respectively. Test the null hypothesis H0: β2 = 0 against the alternative hypothesis Ha: β2 ≠0. Use α = .05. Test the null hypothesis H0: β3 = 0 against the alternative hypothesis Ha: β3 ≠0. Use α...
When estimating y = β0 + β1x1 + β2x2 + β3x3 + ε, you wish to test H0: β1 = β2 = 0 versus HA: At least one βi ≠ 0. The value of the test statistic is F(2,20) = 2.50 and its associated p-value is 0.1073. At the 5% significance level, the conclusion is to ________. Multiple Choice a. reject the null hypothesis; we can conclude that x1 and x2 are jointly significant b. not reject the null hypothesis;...
When estimating y = β0 + β1x1 + β2x2 + β3x3 + ε, you wish to test H0: β1 = β2 = 0 versus HA: At least one βi ≠ 0. The value of the test statistic is F(2,20) = 2.50 and its associated p-value is 0.1073. At the 5% significance level, the conclusion is to ________. Multiple Choice a. reject the null hypothesis; we can conclude that x1 and x2 are jointly significant b. not reject the null hypothesis;...
Problem 3. A department store investigated the effects of advertising expenditure on the weekly sales for its men's wear, children's wear, and women's wear departments. Five weeks were randomly selected for each department to be used in the analysis (this makes 15 weeks in total, ?n). The variables are as follows: ?y = weekly sales ?1x1 = advertising expenditure ?2x2 = 1 if it is the children's wear department and a 0 otherwise ?3x3 = 1 if it is the...
Suppose that we want to find a regression equation relating systolic blood pressure (y) to weight (x1), age (x2) and smoking status (0 = does not smoke, 1 = smokes less than one pack per day, 2 = smokes one or more packs per day). Use the Minitab outputs below to test whether or not the smoking status variable adds to the predictive value of a model which already contains weight and age, using α = .05. i.e., test the...
(a) Using the above t-test data to determine whether or not there
is a linear relationship between the two variables.
(b) Using the above ANOVA F-test data to determine whether or not
there is a linear relationship between the two variables.
(c) How do the results in (a) compare to those in (b)?
We were unable to transcribe this imageAnalysis of Variance Table Response: DatSGPA Dat $ACT 1 3. 588 3. 5878 9. 2402 0.002917 Df Sum Sq Mean sq...
Oehlert provides data from a small experiment with n = 16 observations on baking packaged cake mixes. Two factors, X1 = baking time in minutes and X2 = baking temperature in degrees F, were varied in the experiment. The response Y was the average palatability score of four cakes baked at a given combination of (X1, X2), with higher values desirable. We fit the full second model Model I: Y; = Bo + B1211 + B22:2 +B112 + B2222 Model...
Use the Excel output in the below table to do (1) through (6) for each ofβ0, β1, β2, and β3. y = β0 + β1x1 + β2x2 + β3x3 + ε df = n – (k + 1) = 16 – (3 + 1) = 12 Excel output for the hospital labor needs case (sample size: n = 16) Coefficients Standard Error t Stat p-value Lower 95% Upper 95% Intercept 1946.8020 504.1819 3.8613 0.0023 848.2840 3045.3201 XRay (x1) 0.0386...