Answer Happy

Suppose that you want to estimate the relationship between people's weight (W) and the number of times they eat out in a month (EO): W₁ = Bo + B₁ EO; + U₁, where Bo is the intercept of the population regression line; B₁ is the slope of the population regression line; u; is the error term; and the subscript i runs over observations, i = 1, n. ; For this, you collect data from a random sample of 300 people. After analyzing the data, you determine that the covariance between people's weight and the number of times they eat out in a month is 4.62 and the variance of the number of times people eat out in a month is 4.04. You also find that the mean weight of people in the sample is 63.82 kg and the mean number of times people eat out in a month is 2.75. The OLS estimator of the slope B₁ is (Round your answer to two decimal places.) The OLS estimator of the intercept Bo is (Round your answer to two decimal places.) Which of the following statements are true in describing the estimates of the coefficients of the regression line? (Check all that apply.) A. The intercept shows the predicted weight when an individual does not go out to eat. The value of the intercept has real-world meaning in this case. B. The slope, B₁, is the percentage change in weight due to a unit change in the number of times a person eats out in a month. C. The slope, B₁, is the change in weight due to a unit change in the number of times a person eats out in a month. D. The intercept shows the predicted weight when an individual does not go out to eat. The value of the intercept therefore has no real-world meaning in this case.