PART FOUR:ICE TESTS400PRACTICE TEST 25. A sports researcher is studying statistics for aset of professional baseball teams over a singleseason. The researcher performs a regressionanalysis on the total payroll (in millions ofdollars) versus the number of wins. An outputof the results is shown below.7. Theis 3.thiscalcpophassamRegression Analysis: Wins versus Payroll(A)PredictorCoef.SE(B)Constant 66.4212.465.330.000(C)PayrollS= 16.9082 R-Sq 11.3% R-Sq(adj) = 3.9%0.20700.16731.240.240(D)(E)Which of the following is the bestinterpretation for the slope of the regressionline?8. Wh-rela(A) An addition of about $5 million inpayroll corresponds to 1 extra win.expe(A)(B) An addition of about $17 million inpayroll corresponds to 1 extra win.(C) An addition of about $66 million inpayroll corresponds to 1 extra win.(D) Five extra wins correspond to an addi-tion of about $1 million in payroll.(B)(C)(D)(E) One extra win corresponds to an addi-tion of about $17 million in payroll.

Given : Constant = 66.42 Slope / Coefficient of Payroll = 0.2070

Which of the following is the best interpretation for the slope of the regression line? (A) An addition of about $5 million in payroll corresponds to 1 extra win. (B) An addition of about $17 million in payroll corresponds to 1 extra win. (C) An addition of about $66 million in payroll corresponds to 1 extra win. (D) Five extra wins correspond to an addi- tion of about $1 million in payroll. (E) One extra win corresponds to an addi- tion of about $17 million in payroll.

Which of the following is the best interpretation for the slope of the regression line? (A) An addition of about $5 million in payroll corresponds to 1 extra win. (B) An addition of about $17 million in payroll corresponds to 1 extra win. (C) An addition of about $66 million in payroll corresponds to 1 extra win. (D) Five extra wins correspond to an addi- tion of about $1 million in payroll. (E) One extra win corresponds to an addi- tion of about $17 million in payroll.

College Algebra

7th Edition

ISBN:9781305115545

Author:James Stewart, Lothar Redlin, Saleem Watson

Publisher:James Stewart, Lothar Redlin, Saleem Watson

Chapter1: Equations And Graphs

Section: Chapter Questions

Problem 10T: Olympic Pole Vault The graph in Figure 7 indicates that in recent years the winning Olympic men’s...

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Olympic 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?
1. Suppose you wanted to predict Winnings ($) using only the number of poles won (Poles), the number of wins (Wins), the number of top five finishes (Top 5), or the number of top ten finishes (Top 10). Which of these four variables provides the best single predictor of winnings?2. Develop an estimated regression equation that can be used to predict Winnings ($) given the number of poles won (Poles), the number of wins (Wins), the number of top five finishes (Top 5), and the number of top ten (Top 10) finishes. Test for individual significance and discuss your findings and conclusions.3. Create two new independent variables: Top 2–5 and Top 6–10. Top 2–5 represents the number of times the driver finished between second and fifth place and Top 6–10 represents the number of times the driver finished between sixth and tenth place. Develop an estimated regression equation that can be used to predict Winnings ($) using Poles, Wins, Top 2–5, and Top 6–10. Test for individual significance and…
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Cinema HD an online movie streaming service that offers a wide variety of award-winning TV shows, movies, animes, and documentaries, would like to determine the mathematical trend of memberships in order to project future needs. Year 2013 2014 2015 2016 2017 2018 2019 2020 2021 Membership 17 16 16 21 20 20 23 25 24 Use the following time series data, to develop a regression equation relating memberships to time. Forecast 2023 membership Assuming the COVID-19 pandemic comes to an end in 2021, in your opinion, how will this affect membership? Why? How will this affect your prediction? What are the issues associated with qualitative forecasting, and how are these overcome? Provide an example of qualitative forecasting and explain the shortcomings.
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Multiple regression is sometimes used in litigation. In the case of Cargill, Inc. v. Hardin (1971), the prosecution charged that the cash price of wheat was manipulated in violation of the Commodity Exchange Act. In a statistical study conducted for this case, a multiple regression model was constructed to predict the cash price of wheat using three supply-and-demand explanatory variables: economic growth, population growth, and meat consumption. Data for 24 years were used to construct the regression equation, and a prediction for the suspect period was computed from this equation Based on a significance level of 5%, which of the following independent variables significantly predict the cash price of wheat? a. Economic Growth b. Population Growth c. Meat Consumption d. All the independent variables significantly predict the cash price of wheat.
Multiple regression is sometimes used in litigation. In the case of Cargill, Inc. v. Hardin (1971), the prosecution charged that the cash price of wheat was manipulated in violation of the Commodity Exchange Act. In a statistical study conducted for this case, a multiple regression model was constructed to predict the cash price of wheat using three supply-and-demand explanatory variables: economic growth, population growth, and meat consumption. Data for 24 years were used to construct the regression equation, and a prediction for the suspect period was computed from this equation. The actual cash price of wheat under investigation in 1963 was $2.13. Based on the comparison of the correct predicted cash price calculated in the previous question and the actual cash price, what does the evidence suggest about Cargill, Inc.? a. Because the predicted price is relatively close to the actual price (within one cent), Cargill, Inc. probably did not artificially manipulate the price of wheat.…
Multiple regression is sometimes used in litigation. In the case of Cargill, Inc. v. Hardin (1971), the prosecution charged that the cash price of wheat was manipulated in violation of the Commodity Exchange Act. In a statistical study conducted for this case, a multiple regression model was constructed to predict the cash price of wheat using three supply-and-demand explanatory variables: economic growth, population growth, and meat consumption. Data for 24 years were used to construct the regression equation, and a prediction for the suspect period was computed from this equation. In 1963, during the period in question, economic growth was 3.8; population growth was 1.40; and meat consumption was 152.95. Based on these values, what would be the predicted cash price of wheat at this time in 1963?
Multiple regression is sometimes used in litigation. In the case of Cargill, Inc. v. Hardin (1971), the prosecution charged that the cash price of wheat was manipulated in violation of the Commodity Exchange Act. In a statistical study conducted for this case, a multiple regression model was constructed to predict the cash price of wheat using three supply-and-demand explanatory variables: economic growth, population growth, and meat consumption. Data for 24 years were used to construct the regression equation, and a prediction for the suspect period was computed from this equation. The following output represents the regression analysis. . Before the judge and jury consider the results of the regression model, they must ensure that the model is valid. What is the proper hypothesis test for this model, and what is the proper conclusion?