Concept explainers
Referring to Problem 14.77, Suppose that an addition to using ERA to predict the number of wins, analytics specialist wants to include the league
a. State the multiple regression equation.
b. Interpret the slopes in (a).
c. Predict the mean number of wins for a team with an ERA of 4.50 in American League.
d. Perform a residual analysis on the model and determine whether the regression assumptions are valid.
e. Is there a significant relationship between wins and the two independent variables (ERA and league) at the 0.05 level of significance?
f. At the 0.05 level of significance, determine whether each independent variable makes a contribution to the regression model. Indicate the most appropriate regression model for this
set of data.
g. Construct a
h. Construct a
i. Compute and interpret the adjusted
j. Compute and interpret the coefficients of partial determination.
k. What assumption do you have to make about the slope of wins with ERA?
l. Add an interaction term to the model and, at the 0.05 level of significance, determine whether it makes a significant contribution to the model.
m. On the basis of the result of (f) and (l), which model appropriate? Explain.
Want to see the full answer?
Check out a sample textbook solutionChapter 14 Solutions
Basic Business Statistics, Student Value Edition
- Find the equation of the regression line for the following data set. x 1 2 3 y 0 3 4arrow_forwardOlympic Pole Vault The graph in Figure 7 indicates that in recent years the winning Olympic men’s pole vault height has fallen below the value predicted by the regression line in Example 2. This might have occurred because when the pole vault was a new event there was much room for improvement in vaulters’ performances, whereas now even the best training can produce only incremental advances. Let’s see whether concentrating on more recent results gives a better predictor of future records. (a) Use the data in Table 2 (page 176) to complete the table of winning pole vault heights shown in the margin. (Note that we are using x=0 to correspond to the year 1972, where this restricted data set begins.) (b) Find the regression line for the data in part ‚(a). (c) Plot the data and the regression line on the same axes. Does the regression line seem to provide a good model for the data? (d) What does the regression line predict as the winning pole vault height for the 2012 Olympics? Compare this predicted value to the actual 2012 winning height of 5.97 m, as described on page 177. Has this new regression line provided a better prediction than the line in Example 2?arrow_forwardThe following fictitious table shows kryptonite price, in dollar per gram, t years after 2006. t= Years since 2006 0 1 2 3 4 5 6 7 8 9 10 K= Price 56 51 50 55 58 52 45 43 44 48 51 Make a quartic model of these data. Round the regression parameters to two decimal places.arrow_forward
- 1. The following data give the percentage of women working in five companies in the retail and trade industry. The percentage of management jobs held by women in each company is also shown. % Working 69 48 73 54 61 % Management 51 21 61 51 43 Develop the estimated regression equation by computing the values of b0 (y-intercept) and b1 (slope).Predict the percentage of management jobs held by women in a company that has 65% women employees.arrow_forwardGiven the equation of a regression line is y= -1.5x-1.8, what is the best predicted value for y given x= -3.0? Assume that the variables x and y have significant correlation.arrow_forward
- College AlgebraAlgebraISBN:9781305115545Author:James Stewart, Lothar Redlin, Saleem WatsonPublisher:Cengage LearningAlgebra and Trigonometry (MindTap Course List)AlgebraISBN:9781305071742Author:James Stewart, Lothar Redlin, Saleem WatsonPublisher:Cengage LearningFunctions and Change: A Modeling Approach to Coll...AlgebraISBN:9781337111348Author:Bruce Crauder, Benny Evans, Alan NoellPublisher:Cengage Learning