EBK BASIC BUSINESS STATISTICS
14th Edition
ISBN: 9780134685168
Author: STEPHAN
Publisher: YUZU
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Chapter 15, Problem 41PS
In problem 15.32-15.36 you developed multiple regression models to predict the fair market value of houses in Glen Cove, Roslyn, and Freeport. Now write a report based on the models you developed. Append all appropriate charts and statistical information to your report.
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Consider the following passage:
I ran a regression, with many variables to predict the result of another variable which was the murder rate. One can see that lots of things, can cause the murder rate to increase or decrease. I tried to account for all the important factors, and those factors are the SAT scores, unemployment rate, and international migration per 1,000. The SAT score is, average combined total score participants did on the SAT exam. The unemployment rate is, "a measure of the prevalence of unemployment and it is calculated as a percentage by dividing the number of unemployed individuals by all individuals currently in the labor force." (Wikipedia) International migration per 1,000 is, the number of people who come into a state from other countries per 1,000 people who live in the state. After I run the regression I will look at the t scores and p values and I should hopefully conclude that international migration does not cause crime.
Which writing mistakes, if any, did…
Consider the following passage:
I ran a regression, with many variables to predict the result of another variable which was the murder rate. One can see that lots of things, can cause the murder rate to increase or decrease. I tried to account for all the important factors, and those factors are the SAT scores, unemployment rate, and international migration per 1,000. The SAT score is, average combined total score participants did on the SAT exam. The unemployment rate is, "a measure of the prevalence of unemployment and it is calculated as a percentage by dividing the number of unemployed individuals by all individuals currently in the labor force." (Wikipedia) International migration per 1,000 is, the number of people who come into a state from other countries per 1,000 people who live in the state. After I run the regression I will look at the t scores and p values and I should hopefully conclude that international migration does not cause crime.
Which writing mistakes, if any, did…
13. Examine the following regression equation and answer the questions that follow:
Salary = 261,128 +91,569Goals + 16,346Assists - 585,560.Defenseman
(2.789) (9.641)
(3.301) (5.001)
R² = 0.65
Salary = NHL Player's Salary in $
Goals Number of goals scored by that player
Assists = Number of assists made by that player
Defenseman = Takes on a value of 1 if player is a defenseman. Otherwise, the value of this
variable is zero.
The numbers in brackets are t-statistics for the variables above them.
(a)
What salary will an offensive player make that scores no goals and has no assists?
Interpret the meaning of the R² in words.
(b)
(c)
What is the increase in salary from scoring one more goal (holding the number of assists
constant)?
Chapter 15 Solutions
EBK BASIC BUSINESS STATISTICS
Ch. 15 - The following is the quadratic regression equation...Ch. 15 - Business actively recruit business student with...Ch. 15 - A study was conducted on automobile engines to...Ch. 15 - Prob. 4PSCh. 15 - In the production of printed circuit boards,...Ch. 15 - An automotive sales manager wishes to examine the...Ch. 15 - Researchers wanted to investigate the relationship...Ch. 15 - Prob. 8PSCh. 15 - Prob. 9PSCh. 15 - Prob. 10PS
Ch. 15 - Using the data of Problem 15.4 on page 600, stored...Ch. 15 - Using the data of Problem 15.6 on page 601, stored...Ch. 15 - Using the data of Problem 15.6 on page 601 stored...Ch. 15 - If the coefficient of determination between two...Ch. 15 - If the coefficient of determination between two...Ch. 15 - Prob. 16PSCh. 15 - Refer to Problem 14.5 on page 542. Perform a...Ch. 15 - Refer to Problem 14.6 on page 542. Perform a...Ch. 15 - Refer to Problem 14.7 on page 542. Perform a...Ch. 15 - Refer to Problem 14.8 on page 542. Perform a...Ch. 15 - Prob. 21PSCh. 15 - Prob. 22PSCh. 15 - Prob. 23PSCh. 15 - You need to develop a model to predict the asking...Ch. 15 - Accounting Today identified top public accounting...Ch. 15 - How can you evaluate whether collinearity exists...Ch. 15 - Prob. 27PSCh. 15 - Prob. 28PSCh. 15 - A Specialist in baseball analytics has expanded...Ch. 15 - In the production of printed circuit boards,...Ch. 15 - Hemlock Farms is a community located in the Pocono...Ch. 15 - Prob. 32PSCh. 15 - Prob. 33PSCh. 15 - Prob. 34PSCh. 15 - You are a real estate broker who wants to compare...Ch. 15 - You are a real estate broker who wants to compare...Ch. 15 - Financial analysts engage in business valuation to...Ch. 15 - Prob. 38PSCh. 15 - A molding machine that contains different cavities...Ch. 15 - The file Cites contains a sample of 25 cities in...Ch. 15 - In problem 15.32-15.36 you developed multiple...
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- 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_forwardNeed help with entirety of the problem.arrow_forward
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- 1. Consider the following regression model: Fram Risk Score; = Bo + B1 × Health Insurance; + u¿ The Framingham Risk Score predicts 10-year risk of cardiovascular disease based on age, cholesterol levels, blood pressure, blood sugar, use of medication for high blood pressure, and smoking. A higher score means worse overall cardiovascular health. A researcher who collects data and regresses the Fram Risk Score against Health Insurance (= 1 if have insurance) finds that B, 2. Consider the following regression model: Class Average; = Bo + B1 x Office Hours; + u; Class Average is the students' average grade in the class at the end of the term and Office Hours is the number of office hours held by the instructor over the entire term. A researcher who collects data and regresses Class Average against Office Hours finds that, surprisingly, B V A V Aarrow_forwardConsider the following combinations of average test score and the average student-teacher ratio in some fictional school districts. Student-teacher ratio (X): 15. 17, 19, 20, 22. 23.5. 25 680, 640, 670, 660. 630, 660, 635 Test-score (Y): Estimate the linear regression line. Oy=-713.57+2.9x O y=-2.97+713.57X Oy=2.97+713.57x O y=713.57-2.97x QUESTION 2 (Continued) Test the hypothesis HB₁=0 vs. H_ :B₁ #0 1 O T=-1.51, P-value=0.38 O T=-1.51, P-value=0.19 O T=1.51, P-value=0.19 O T=1.51, P-value=0.38 QUESTION 3 (Continued) Compute R² and interpret it. 0.31: About 31% of variation in test score is explained by variation in student-teacher ratio through the model. 0.31: About 31% of variation in student-teacher ratio is explained by variation in test score through the model. O 0.69: About 69% of variation in test score is explained by variation in student-teacher ratio through the model. O 0.69: About 89% of variation in student-teacher ratio is explained by variation in test score through…arrow_forwardSuppose that you have several hypotheses to test, as follow:H1: Costumer focus has a significant positive relationship with organization performance.H2: Continuous improvement has a significant negative relationship with organization performance.H3: Employee involvement has a significant negative relationship with organization performance.H4: Top management support has a significant positive relationship with organization performance.H5: Supplier management has a significant positive relationship with organization performance.After you analyze the data using linear regression analysis, the result showed as follow: MODEL SUMMARY Model R R Square Adjusted R Square Std. Error of the Estimate 1 .674a 0.579 0.467 0.833 ANNOVA Model Sum of Squares df Mean Square F Sig. Regression 85.400 6 13.673 23.547 .000b Residual 140.500 195 .695 Total 218.500 198 a. Dependent Variable: Organization Performance b. Predictors: (Constant), Customer focus,…arrow_forward
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