EP BUSINESS STATISTICS:FIRST COURSE-ACC
8th Edition
ISBN: 9780135179802
Author: Levine
Publisher: PEARSON CO
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The November 24, 2001, issue of The Economist published economic data for 15
industrialized nations. Included were the percent changes in gross domestic product (GDP),
industrial production (IP), consumer prices (CP), and producer prices (PP) from Fall 2000
to Fall 2001, and the unemployment rate in Fall 2001 (UNEMP). An economist wants to
construct a model to predict GDP from the other variables. A fit of the model
GDP = , + P,IP + 0,UNEMP + f,CP + P,PP + €
yields the following output:
The regression equation is
GDP = 1.19 + 0.17 IP + 0.18 UNEMP + 0.18 CP – 0.18 PP
Predictor
Coef SE Coef
тР
Constant
1.18957 0.42180 2.82 0.018
IP
0.17326 0.041962 4.13 0.002
UNEMP
0.17918 0.045895 3.90 0.003
CP
0.17591 0.11365 1.55 0.153
PP
-0.18393 0.068808 -2.67 0.023
Predict the percent change in GDP for a country with IP = 0.5, UNEMP = 5.7, CP =
3.0, and PP = 4.1.
a.
b.
If two countries differ in unemployment rate by 1%, by how much would you predict
their percent changes in GDP to differ, other…
Suppose you a manager for a local car dealership, and you want to use a linear
regression model to predict the price of a used car. You decide to use four
predictor variables - "Age' (how long the car has been in use since it was
produced), "Dents" (the number of visible dents on the outside of the car),
"Accidents" (the number of accidents the car has been in), and "mpg" (the fuel
efficiency of the car, measured in miles per gallon). Your dataset contains this
information for the past 120 cars sold at your dealership. Using this model, your
analysis finds an R² of 37%. What is the F statistic of your analysis?
Note:
1- Only round your final answer. Round your final answer to two decimal
places.
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- What does the y -intercept on the graph of a logistic equation correspond to for a population modeled by that equation?arrow_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_forwardDevelop an estimated multiple linear regression model that could be used to predict the alumni giving rate using Graduation Rate, % of Classes Under 20, and Student/Faculty Ratio as independent variables. Discuss your findings.arrow_forward
- 1. A change of a dependent variable is all due to a manipulation of intended independent variables . Explain, with an example, why this statement can be inaccurate . Explain why cause of a regression model can also fail to discover a causal relationshiparrow_forwardWhat is regression models?arrow_forward1arrow_forward
- Suppose you wanted to test whether or not the payoff to an additional year of education was the same for men and women in the STEM majors. How would you set up your regression analysis in this casearrow_forwardWhat are some examples of ways in which linear regression to create a beneficial statistical outcome, in a business setting?arrow_forwardUse the Financial database from “Excel Databases.xls” on Blackboard. Use Total Revenues, Total Assets, Return on Equity, Earnings Per Share, Average Yield, and Dividends Per Share to predict the average P/E ratio for a company. Use Excel to develop the multiple linear regression model. Assume a 5% level of significance. Which independent variable is the strongest predictor of the average P/E ratio of a company? A. Total Revenues B. Average Yield C. Earnings Per Share D.Return on Equity E. Total Assets F.Dividends Per Share Company Type Total Revenues Total Assets Return on Equity Earnings per Share Average Yield Dividends per Share Average P/E Ratio AFLAC 6 7251 29454 17.1 2.08 0.9 0.22 11.5 Albertson's 4 14690 5219 21.4 2.08 1.6 0.63 19 Allstate 6 20106 80918 20.1 3.56 1 0.36 10.6 Amerada Hess 7 8340 7935 0.2 0.08 1.1 0.6 698.3 American General 6 3362 80620 7.1 2.19 3 1.4 21.2 American Stores 4 19139 8536 12.2 1.01 1.4 0.34 23.5 Amoco 7 36287…arrow_forward
- 28. When would a survival analysis model be used instead of a logistic regression model?arrow_forward16. A financial analyst wants to investigate the relationship between the annual starting salary of new employees at a large firm versus years of education the employee has (beyond high school). The analyst selected a random sample of 18 new employees and recorded their starting salary and how many years of education they have beyond high school. The computer output of an analysis of salary versus years of education is shown in the table. Regression Analysis: Salary (in $1,000) versus Education Predictor Coef SE Coef Constant 25.840 3.803 *** *** Education 13.4862 0.5310 *** *** Assuming that all conditions for inference are met, which of the following is the appropriate test statistic for testing the null hypothesis that the slope of the population regression line equals o? (A) 0 (B) 0.163 (C) 1 (D) 1.54 (E) 25.40arrow_forward1. Sue is conducting a regression analysis to predict the amount of salaries expense using the number of employees and the average rate of pay. This type of regression analysis is called a(n) independent regression. linear regression. multiple regression. dependent regression.arrow_forward
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