Consider the following two a.m. peak work trip generation models, estimated by household linear regression: T = 0.62 + 3.1 X1 + 1.4 X2            R2= 0.590        (2.3)     (7.1)       (5.9) T = 0.01 + 2.4 X1 + 1.2 Z1 + 4.0 Z2    R2= 0.598        (0.8)     (4.2)       (1.7)       (3.1) X1 = number of workers in the household X2 = number of cars in the household, Z1 is a dummy variable which takes the value 1 if the household has one car, Z2 is a dummy variable which takes the value 1 if the household has two or more cars.

Consider the following population linear regression model of individual food expenditure: Y = 50 + 0.5X + u, where Y is weekly food expenditure in dollars, X is the individual’s age, and 50+0.5X is the population regression line. Suppose we  generate artificial data for 3 individuals using this model. This artificial sample, which consists of 3 observations, is shown in the following table:   Answer the following questions. Show your working. (a) What are the values of V1 and V4? (b) Suppose we know that in this artificial sample, the sample covariance between X and Y is 150, and the sample variance of X is 100. Compute the OLS regression line of the regression of Y on X. (Hint: Assume these summary statistics and the OLS regression line continue to hold in parts (c)-(e).) (c) What are the values of V5 and V7?

Consider the following estimated regression model relating annual salary to years of education and work experience. Estimated Salary = 10,815.11 +2563.46 (Education) +897.49(Experience) Suppose two employees at the company have been working there for five years. One has a bachelor's degree (8 years of education) and one has a master's degree ( 10 years of education). How much more money would we expect the employee with a master's degree to make? Answer Tables Keypac Keyboard Shortcu

Cascade Pharmaceuticals Company developed the following regression model, using time-series data from the past 33 quarters, for one of its nonprescription cold remedies: Y = -1.04 +0.24X₁-0.27X2 where Y = quarterly sales (in thousands of cases) of the cold remedy X₁ = Cascade's quarterly advertising (x $1,000) for the cold remedy X₂ = competitors' advertising for similar products (x $10,000) Here is additional information concerning the regression model: Sb1 = 0.052, Sb2 = 0.070, R² = 0.640, se = 1.63, F-statistic = 31.402, and Durbin-Watson (d) statistic = 0.499. Which of the independent variables (if any) appears to be statistically significant (at the 0.05 level) in explaining sales of the cold remedy? (Hint: to.05/2,33-3= 2.042.) Check all that apply. O X₁ O X₂ What proportion of the total variation in sales is explained by the regression equation? O 0.122 O 0.070 O 0.640 O 0.052 The given F-value shows that you reject the null hypothesis that neither of the independent variables…