1. A dataset contains information about 325 books for sale at amazon.com. The dataset includes two prices for each book: List Price (the price set by the publisher in dollars) and the Amazon Price (in dollars). The linear model that uses a book's list price to predict its Amazon price is given below: Predicted Amazon Price = -2.41 + 0.83 x List Price with SE Residual = 3.88 a) Interpret the slope by filling in the blanks: As the (Amazon/list) price increases by 1 dollar, the Amazon/list) price is predicted to increase by (). 83 _dollar (s ) . b) Does the y-intercept have a meaningful interpretation in this context? A. Yes. The y-intercept describes how SSError for the linear model compares to SSError for the single-mean model B. Yes. The y-intercept describes how Amazon prices compare to list prices, on average. C. No. $0 is not a reasonable value for the Amazon price, so the intercept does not have a meaningful interpretation D. No. $0 is not a reasonable price for the list price, so the intercept does not have a meaningful interpretation c) Calculate the residual for a book that has a list price of $12.95 and an Amazon price of $5.18 5.18 - 8.34 = - 3.16 2. You are considering two models to predict a quantitative response: a separate-means model and a linear model. You find that SSError is smaller for the separate-means model, yet the SE of the residuals is smaller for the linear model. Does this indicate that you have made a calculation mistake? A. Yes. When the data are quantitative, SSError for the linear model should always be smaller than SSError for the separate-means model B. Yes. SSError and the SE of the residuals both measure the amount of prediction error. As SSError increases, SE of the residuals should always increase. C. No. The separate-means model and the linear model have different degrees of freedom for error, so SSError and the SE of residuals don't always "agree." D. No. Despite their similar names, SSError and the SE of the residuals measure completely different things. It is not surprising that these two values do not "agree." 3. The owner of a cafe suspects that coffee sales are related to the weather. For a period of 60 days in the spring, she records the number of coffees sold at her cafe as well as the minimum daily temperature (in degrees Fahrenheit). a) Using the partially filled out ANOVA table below, calculate the MSModel, MSError, and F-statistic Source DF SS MS Model 8010 Error 58 13575 Total 59 21585