Introductory Statistics (10th Edition)
10th Edition
ISBN: 9780321989178
Author: Neil A. Weiss
Publisher: PEARSON
expand_more
expand_more
format_list_bulleted
Concept explainers
Textbook Question
Chapter 15, Problem 1FDA
Recall from Chapter 1 (see page 34) that the Focus database and Focus sample contain information on the undergraduate students at the University of Wisconsin - Eau Claire (UWEC). Now would be a good time for you to review the discussion about these data sets.
Open the Focus sample worksheet (FocusSample) in the technology of your choice and do the following.
- a. Perform a residual analysis to decide whether considering the assumptions for regression inferences met by the variables high school percentile and cumulative GPA appears reasonable.
- b. With high school percentile as the predictor variable and cumulative GPA as the response variable, determine and interpret the standard error of the estimate.
- c. At the 5% significance level, do the data provide sufficient evidence to conclude that high school percentile is useful for predicting cumulative GPA of UWEC undergraduates?
- d. Determine a point estimate for the mean cumulative GPA of all UWEC undergraduates who had high school percentiles of 74.
- e. Find a 95% confidence interval for the mean cumulative GPA of all UWEC undergraduates who had high school percentiles of 74.
- f. Determine the predicted cumulative GPA of a UWEC undergraduate who had a high school percentile of 74.
- g. Find a 95% prediction interval for the cumulative GPA of a UWEC undergraduate who had a high school percentile of 74.
- h. At the 5% significance level, do the data provide sufficient evidence to conclude that high school percentile and cumulative GPA are
positively linearly correlated ?
Expert Solution & Answer
Want to see the full answer?
Check out a sample textbook solutionStudents have asked these similar questions
Describe a hypothetical study for which multiple regression with more than two predictor variables would be an appropriate analysis.
Your description should include one dependent variable and three or more predictors.
D& T LTD marketing team needed more information about the effectiveness of their 3 main mode of advertising. To determine which type is the most effective, the manager collected one week’s data from 25 randomly selected stores. For each store, the following variables were recorded:
Weekly gross sales
Weekly expenditure on direct mailing (Direct)
Weekly expenditure on newspaper advertising (Newspaper)
Weekly expenditure on television commercials (Television)
Following is the regression output based on the above-mentioned data.
SUMMARY OUTPUT
Regression Statistics
Multiple R
0.442…
Please ASAP in part a , b and c
Chapter 15 Solutions
Introductory Statistics (10th Edition)
Ch. 15.1 - Suppose that x and y are predictor and response...Ch. 15.1 - Prob. 2ECh. 15.1 - Prob. 3ECh. 15.1 - Prob. 4ECh. 15.1 - Prob. 5ECh. 15.1 - In Exercises 15.315.6, assume that the variables...Ch. 15.1 - The difference between an observed value and a...Ch. 15.1 - Identify two graphs used in a residual analysis to...Ch. 15.1 - Which graph used in a residual analysis provides...Ch. 15.1 - Figure 15.8 shows three residual plots and a...
Ch. 15.1 - Figure 15.9 on the next page shows three residual...Ch. 15.1 - In Exercises 15.1215.21, we repeat the data and...Ch. 15.1 - In Exercises 15.1215.21, we repeat the data and...Ch. 15.1 - Prob. 14ECh. 15.1 - Prob. 15ECh. 15.1 - Prob. 16ECh. 15.1 - Prob. 17ECh. 15.1 - Prob. 18ECh. 15.1 - Prob. 19ECh. 15.1 - Prob. 20ECh. 15.1 - Prob. 21ECh. 15.1 - Prob. 22ECh. 15.1 - Prob. 23ECh. 15.1 - Prob. 24ECh. 15.1 - Prob. 25ECh. 15.1 - In Exercises 15.2215.27, we repeat the information...Ch. 15.1 - Prob. 27ECh. 15.1 - Prob. 28ECh. 15.1 - In Exercises 15.2815.33, a. compute the standard...Ch. 15.1 - Prob. 30ECh. 15.1 - In Exercises 15.2815.33, a. compute the standard...Ch. 15.1 - In Exercises 15.2815.33, a. compute the standard...Ch. 15.1 - In Exercises 15.2815.33, a. compute the standard...Ch. 15.1 - In Exercises 15.3415.43, use the technology of...Ch. 15.1 - In Exercises 15.3415.43, use the technology of...Ch. 15.1 - In Exercises 15.3415.43, use the technology of...Ch. 15.1 - In Exercises 15.3415.43, use the technology of...Ch. 15.1 - Prob. 38ECh. 15.1 - Prob. 39ECh. 15.1 - Prob. 40ECh. 15.1 - Prob. 41ECh. 15.1 - Prob. 42ECh. 15.1 - Prob. 43ECh. 15.2 - Explain why the predictor variable is useless as a...Ch. 15.2 - Prob. 45ECh. 15.2 - Prob. 46ECh. 15.2 - In this section, we used the statistic b1 as a...Ch. 15.2 - In Exercises 15.4815.57, we repeat the information...Ch. 15.2 - Prob. 49ECh. 15.2 - In Exercises 15.4815.57, we repeat the information...Ch. 15.2 - In Exercises 15.4815.57, we repeat the information...Ch. 15.2 - Prob. 52ECh. 15.2 - Prob. 53ECh. 15.2 - Prob. 54ECh. 15.2 - In Exercises 15.4815.57, we repeat the information...Ch. 15.2 - Prob. 56ECh. 15.2 - Prob. 57ECh. 15.2 - Prob. 58ECh. 15.2 - In Exercises 15.5815.63, we repeat the information...Ch. 15.2 - Prob. 60ECh. 15.2 - In Exercises 15.5815.63, we repeat the information...Ch. 15.2 - Prob. 62ECh. 15.2 - In Exercises 15.5815.63, we repeat the information...Ch. 15.2 - Prob. 64ECh. 15.2 - In each of Exercises 15.6415.69, apply Procedure...Ch. 15.2 - In each of Exercises 15.6415.69, apply Procedure...Ch. 15.2 - Prob. 67ECh. 15.2 - Prob. 68ECh. 15.2 - Prob. 69ECh. 15.2 - Prob. 70ECh. 15.2 - In Exercises 15.7015.80, use the technology of...Ch. 15.2 - In Exercises 15.7015.80, use the technology of...Ch. 15.2 - Prob. 73ECh. 15.2 - Prob. 74ECh. 15.2 - Prob. 75ECh. 15.2 - In Exercises 15.7015.80, use the technology of...Ch. 15.2 - Prob. 77ECh. 15.2 - Prob. 78ECh. 15.2 - In Exercises 15.7015.80, use the technology of...Ch. 15.2 - Prob. 80ECh. 15.3 - Without doing any calculations, fill in the blank....Ch. 15.3 - Prob. 82ECh. 15.3 - Prob. 83ECh. 15.3 - Prob. 84ECh. 15.3 - In Exercises 15.8215.91, we repeat the data from...Ch. 15.3 - Prob. 86ECh. 15.3 - Prob. 87ECh. 15.3 - In Exercises 15.8215.91, we repeat the data from...Ch. 15.3 - Prob. 89ECh. 15.3 - Prob. 90ECh. 15.3 - Prob. 91ECh. 15.3 - Prob. 92ECh. 15.3 - In Exercises 15.9215.97, presume that the...Ch. 15.3 - In Exercises 15.9215.97, presume that the...Ch. 15.3 - In Exercises 15.9215.9, presume that the...Ch. 15.3 - Prob. 96ECh. 15.3 - In Exercises 15.9215.97, presume that the...Ch. 15.3 - Prob. 98ECh. 15.3 - In Exercises 15.9815.108, use the technology of...Ch. 15.3 - In Exercises 15.9815.108, use the technology of...Ch. 15.3 - In Exercises 15.9815.108, use the technology of...Ch. 15.3 - In Exercises 15.9815.108, use the technology of...Ch. 15.3 - Prob. 103ECh. 15.3 - Prob. 104ECh. 15.3 - Prob. 105ECh. 15.3 - Prob. 106ECh. 15.3 - In Exercises 15.9815.108, use the technology of...Ch. 15.3 - Prob. 108ECh. 15.3 - Margin of Error in Regression. In Exercises 15.109...Ch. 15.3 - Refer to the confidence interval and prediction...Ch. 15.4 - Identify the statistic used to estimate the...Ch. 15.4 - Prob. 112ECh. 15.4 - Suppose that, for a sample of pairs of...Ch. 15.4 - Prob. 114ECh. 15.4 - Prob. 115ECh. 15.4 - Prob. 116ECh. 15.4 - Prob. 117ECh. 15.4 - Prob. 118ECh. 15.4 - Prob. 119ECh. 15.4 - Prob. 120ECh. 15.4 - Prob. 121ECh. 15.4 - Prob. 122ECh. 15.4 - Prob. 123ECh. 15.4 - Prob. 124ECh. 15.4 - Prob. 125ECh. 15.4 - Prob. 126ECh. 15.4 - Prob. 127ECh. 15.4 - Prob. 128ECh. 15.4 - Prob. 129ECh. 15.4 - Prob. 130ECh. 15.4 - Prob. 131ECh. 15.4 - Prob. 132ECh. 15.4 - Prob. 133ECh. 15.4 - In each of Exercises 15.13415.144, use the...Ch. 15.4 - In each of Exercises 15.13415.144, use the...Ch. 15.4 - Prob. 136ECh. 15.4 - Prob. 137ECh. 15.4 - Prob. 138ECh. 15.4 - Prob. 139ECh. 15.4 - Prob. 140ECh. 15.4 - In each of Exercises 15.13415.144, use the...Ch. 15.4 - Prob. 142ECh. 15.4 - Prob. 143ECh. 15.4 - Prob. 144ECh. 15 - Prob. 1RPCh. 15 - Suppose that x and y are two variables of a...Ch. 15 - What two plots did we use in this chapter to...Ch. 15 - Regarding analysis of residuals, decide in each...Ch. 15 - Suppose that you perform a hypothesis test for the...Ch. 15 - Prob. 6RPCh. 15 - Prob. 7RPCh. 15 - Prob. 8RPCh. 15 - Prob. 9RPCh. 15 - Identify the relationship between two variables...Ch. 15 - Graduation Rates. Graduation ratethe percentage of...Ch. 15 - Prob. 12RPCh. 15 - Prob. 13RPCh. 15 - For Problems 1417, presume that the variables...Ch. 15 - For Problems 1417, presume that the variables...Ch. 15 - For Problems 1417, presume that the variables...Ch. 15 - Prob. 17RPCh. 15 - In Problems 1820, use the technology of your...Ch. 15 - In Problems 1820, use the technology of your...Ch. 15 - In Problems 1820, use the technology of your...Ch. 15 - Recall from Chapter 1 (see page 34) that the Focus...Ch. 15 - At the beginning of this chapter, we presented...
Knowledge Booster
Learn more about
Need a deep-dive on the concept behind this application? Look no further. Learn more about this topic, statistics and related others by exploring similar questions and additional content below.Similar questions
- 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_forwardYou plan to fit a regression model that will be used to predict first-year college GPA (FYGPA) from high-school GPA (HSGPA), ACT score (ACT), first-generation status (Yes or No), socioeconomic class (lower class, lower to middle class, middle to upper class, and upper class), and school type (public or private). What is the total number of estimated regression coefficients? If the sample size is n = 250 students, what are the degrees of freedom for the following sources of variation: Regression Error Totalarrow_forward
- Regarding a scatterplot, a. identify one of its uses. b. what property should it have to obtain a regression line for the data?arrow_forwardThe St. Lucian Government is interested in predicting the number of weekly riders on the public buses using the following variables: • • • • Price of bus trips per weekThe population in the cityThe monthly income of ridersAverage rate to park your personal vehicle Determine the multiple regression equation for the data. What is the predicted value of the number of weekly riders if: price of bus trips per week = $24; population = $2,000,000; the monthly income of riders = $13,500; and average rate to park your personal vehicle = $150. Interpret the coefficient of determination.arrow_forwardRestaurant Digest, a famous restaurant magazine writes that the amount of tips servers get in Florida is affected by several factors including the amount of bill, number of adult diners as well as kids and the income of the diners. Use the data below to answer the following questions: Find the multiple regression model. At a level of significance of 0.03, is there a significance relationship between the amount of tip and at least one of the independent variables? What is the likely amount of tip if a group of diners spend $40.33 and had an annual income of $81,000. The diners included 4 adults and 3 kids? What percent of the variation in amount of tip is accounted for by amount of bill, annual income, number of adults and kids? Customer Amount of Tip Amount of Bill Number of adult Diners Number of kids Annual Income 1 $ 7.00 $ 48.97 5 3 100000 2 $ 4.50 $ 28.23 4 3 80000 3…arrow_forward
- Bill wants to explore factors affecting work stress. He would like to examine the relationship between age, number of years at the workplace, perceived social support, and work stress. He collects data on the variables from 100 employees (males and females) working in banks. The research question is How accurately can work stress be predicted from linear combination of the predictors (age, social support, number of years at the workplace)? Conduct a multiple regression analysis to answer the following questions: What is the regression equation for all the predictors? Write a results section based on your analysis that answers the research question.arrow_forwardBill wants to explore factors affecting work stress. He would like to examine the relationship between age, number of years at the workplace, perceived social support, and work stress. He collects data on the variables from 100 employees (males and females) working in banks. The research question is How accurately can work stress be predicted from linear combination of the predictors (age, social support, number of years at the workplace)? Conduct a multiple regression analysis to answer the following questions: What is the relationship of age, number of years, and social support with work stress? Is the regression significant? If yes, what does it indicate?arrow_forwardExplain whether each scenario below is a regression, classification, or unsupervised learn- ing problem. If it is a supervised learning scenario, indicate whether we are more interested in inference or prediction. Finally, provide in each case the number of observations, n, and the number of predictors, p. (1) An online retailer must decide whether to display advertisement A or advertisement B to each customer on the basis of collected customer demographics (age, income, zip code, and gender). A set of 150 of its customers have already expressed a preference for one advertisement or the other. (2) A policy analyst is interested in discovering factors that are associated with the crime rate across different U.S. cities. For each of 500 cities, the policy analyst gathers the following data: the crime rate, unemployment rate, population, median income, median home price, and state. (3) The the channel owner to see where the subscribers are located, their age and gender, the times and days…arrow_forward
- The Transactional Records Access Clearinghouse at Syracuse University reported data showing the odds of an Internal Revenue Service audit. The following table shows the average adjusted gross income reported and the percent of the returns that were audited for 20 selected IRS districts. Develop the estimated regression equation that could be used to predict the percent audited given the average adjusted gross income reported. At the .05 level of significance, determine whether the adjusted gross income and the percent audited are related. Did the estimated regression equation provide a good fit? District Adjusted Gross Income ($) Percent Audited Los Angeles 36,664 1.3 Sacramento 38,845 1.1 Atlanta 34,886 1.1 Boise 32,512 1.1 Dallas 34,531 1.0 Providence 35,995 1.0 San Jose 37,799 0.9 Cheyenne 33,876 0.9 Fargo 30,513 0.9 New Orleans 30,174 0.9 Oklahoma City 30,060 0.8 Houston 37,153…arrow_forward4. Housing Prices in New YorkWe have looked at predicting the price (in s) of New York homes based on the size (in thousands of square feet), using the data in HomesForSaleNY. Two other variables in the dataset are the number of bedrooms and the number of bathrooms. Use technology to create a multiple regression model to predict price based on all three variables: size, number of bedrooms, and number of bathrooms. Price Size Beds Baths 145 1.3 3 1.5 875 2.9 7 3.75 300 1.5 3 2.5 370 1.1 2 1 268 1.5 2 2 1399 4.8 6 5 1125 3.1 3 2.5 299 1.4 3 2 110 1.2 3 1 2999 6 7 8 170 1 2 1 269 1.5 3 1.5 150 1 2 1.5 288 1.8 3 2.1 350 1.3 3 2 120 0.9 1 1 309 2.4 4 2.5 1500 1.5 2 1.5 635 2.5 4 2.5 350 0.9 2 1 459 1.8 4 2.5 275 2.9 4 1.5 275 1.8 3 2 2500 3.7 3 3 187 1.4 3 1.5 238 1.7 3 1.5 155 0.7 1 1 175 1.6 3 1.5 569 3.2 4 2 105 1.2 2 2.5 a) Which of the variables which are significant at the 5% level? b) Which variable is the most…arrow_forwardPlease see attached image.arrow_forward
arrow_back_ios
arrow_forward_ios
Recommended textbooks for you
- Algebra and Trigonometry (MindTap Course List)AlgebraISBN:9781305071742Author:James Stewart, Lothar Redlin, Saleem WatsonPublisher:Cengage LearningCollege AlgebraAlgebraISBN:9781305115545Author:James Stewart, Lothar Redlin, Saleem WatsonPublisher:Cengage LearningLinear Algebra: A Modern IntroductionAlgebraISBN:9781285463247Author:David PoolePublisher:Cengage Learning
- Glencoe Algebra 1, Student Edition, 9780079039897...AlgebraISBN:9780079039897Author:CarterPublisher:McGraw HillBig Ideas Math A Bridge To Success Algebra 1: Stu...AlgebraISBN:9781680331141Author:HOUGHTON MIFFLIN HARCOURTPublisher:Houghton Mifflin Harcourt
Algebra and Trigonometry (MindTap Course List)
Algebra
ISBN:9781305071742
Author:James Stewart, Lothar Redlin, Saleem Watson
Publisher:Cengage Learning
College Algebra
Algebra
ISBN:9781305115545
Author:James Stewart, Lothar Redlin, Saleem Watson
Publisher:Cengage Learning
Linear Algebra: A Modern Introduction
Algebra
ISBN:9781285463247
Author:David Poole
Publisher:Cengage Learning
Glencoe Algebra 1, Student Edition, 9780079039897...
Algebra
ISBN:9780079039897
Author:Carter
Publisher:McGraw Hill
Big Ideas Math A Bridge To Success Algebra 1: Stu...
Algebra
ISBN:9781680331141
Author:HOUGHTON MIFFLIN HARCOURT
Publisher:Houghton Mifflin Harcourt
Correlation Vs Regression: Difference Between them with definition & Comparison Chart; Author: Key Differences;https://www.youtube.com/watch?v=Ou2QGSJVd0U;License: Standard YouTube License, CC-BY
Correlation and Regression: Concepts with Illustrative examples; Author: LEARN & APPLY : Lean and Six Sigma;https://www.youtube.com/watch?v=xTpHD5WLuoA;License: Standard YouTube License, CC-BY