EBK STATISTICS
4th Edition
ISBN: 9780134436814
Author: KLINGENBERG
Publisher: PEARSON CUSTOM PUB.(CONSIGNMENT)
expand_more
expand_more
format_list_bulleted
Concept explainers
Textbook Question
Chapter 13.1, Problem 3PB
Predicting college GPA For all students at Walden University, the prediction equation for y = college GPA (
- a. Find the predicted college GPA for students having (i) high school GPA = 4.0 and college board score = 800 and (ii) x1 = 2.0 and x2 = 200.
- b. For those students with x2 = 500, show that ŷ = 1.20 + 0.50x1.
- c. For those students with x2 = 600, show that ŷ = 1.40 + 0.50x1. Thus, compared to part b, the slope for x1 is still 0.50, and increasing x2 by 100 (from 500 to 600) shifts the intercept upward by 100 × (slope for x2) = 100(0.002) = 0.20 units.
Expert Solution & Answer
Want to see the full answer?
Check out a sample textbook solutionStudents have asked these similar questions
A researcher is conducting a study to examine the relationship between age and agility. She recruited a sample of 50 participants, ranging in age from 20 – 65 years old, and asked them to perform a series of agility tests. Afterward, participants were given an average agility score, which was then used in a correlation analysis against participant age. The results of the study are as follows [r(50) = -0.97, p < 0.001]. Identify the correct interpretation below.
A. There is a non-significant, weak, negative correlation between age and agility, suggest that as age increases, agility decreases
B. There is a statistically significant, strong, negative correlation between age and agility, suggesting that as age increases, agility decreases
C. There is a non-significant, moderate, positive correlation between age and agility, suggesting that there is no relationship between these two variables
D. There is a statistically significant, strong positive correlation between age and…
Regression and Predictions. Exercises 13–28 use the same data sets as Exercises 13–28 in Section 10-1. In each case, find the regression equation, letting the first variable be the predictor (x) variable. Find the indicated predicted value by following the prediction procedure summarized in Figure 10-5 on page 493.
Crickets and Temperature Find the best predicted temperature at a time when a cricket chirps 3000 times in 1 minute. What is wrong with this predicted temperature?
Regression and Predictions. Exercises 13–28 use the same data sets as Exercises 13–28 in Section 10-1. In each case, find the regression equation, letting the first variable be the predictor (x) variable. Find the indicated predicted value by following the prediction procedure summarized in Figure 10-5 on page 493.
Internet and Nobel Laureates Find the best predicted Nobel Laureate rate for Japan, which has 79.1 Internet users per 100 people. How does it compare to Japan’s Nobel Laureate rate of 1.5 per 10 million people?
Chapter 13 Solutions
EBK STATISTICS
Ch. 13.1 - Predicting weight For a study of female college...Ch. 13.1 - Prob. 2PBCh. 13.1 - Predicting college GPA For all students at Walden...Ch. 13.1 - Prob. 4PBCh. 13.1 - Does more education cause more crime? The FL Crime...Ch. 13.1 - Crime rate and income Refer to the previous...Ch. 13.1 - The economics of golf The earnings of a PGA Tour...Ch. 13.1 - Prob. 8PBCh. 13.1 - Controlling can have no effect Suppose that the...Ch. 13.1 - House selling prices Using software with the House...
Ch. 13.1 - Used cars The following data (also available from...Ch. 13.2 - Predicting sports attendance Keeneland Racetrack...Ch. 13.2 - Predicting weight Lets use multiple regression to...Ch. 13.2 - Prob. 14PBCh. 13.2 - Price of used cars For the 19 used cars listed in...Ch. 13.2 - Prob. 16PBCh. 13.2 - Softball data For the Softball data set on the...Ch. 13.2 - Slopes, correlations, and units In Example 2 on y...Ch. 13.2 - Predicting college GPA Using software with the...Ch. 13.3 - Predicting GPA For the 59 observations in the...Ch. 13.3 - Study time help GPA? Refer to the previous...Ch. 13.3 - Variability in college GPA Refer to the previous...Ch. 13.3 - Does leg press help predict body strength? Chapter...Ch. 13.3 - Prob. 24PBCh. 13.3 - Interpret strength variability Refer to the...Ch. 13.3 - Any predictive power? Refer to the previous three...Ch. 13.3 - Predicting pizza revenue Aunt Ermas Pizza...Ch. 13.3 - Prob. 28PBCh. 13.3 - Mental health again Refer to the previous...Ch. 13.3 - Prob. 30PBCh. 13.3 - House prices Use software to do further analyses...Ch. 13.4 - Body weight residuals Examples 47 used multiple...Ch. 13.4 - Strength residuals In Chapter 12, we analyzed...Ch. 13.4 - Prob. 34PBCh. 13.4 - Nonlinear effects of age Suppose you fit a...Ch. 13.4 - Prob. 36PBCh. 13.4 - Why inspect residuals? When we use multiple...Ch. 13.4 - College athletes The College Athletes data set on...Ch. 13.4 - House prices Use software with the House Selling...Ch. 13.4 - Prob. 40PBCh. 13.5 - U.S. and foreign used cars Refer to the used car...Ch. 13.5 - Prob. 42PBCh. 13.5 - Predict using house size and condition For the...Ch. 13.5 - Quality and productivity The table shows data from...Ch. 13.5 - Predicting hamburger sales A chain restaurant that...Ch. 13.5 - Prob. 46PBCh. 13.5 - House size and garage interact? Refer to the...Ch. 13.5 - Prob. 48PBCh. 13.5 - Comparing sales You own a gift shop that has a...Ch. 13.6 - Prob. 50PBCh. 13.6 - Prob. 51PBCh. 13.6 - Prob. 52PBCh. 13.6 - Prob. 53PBCh. 13.6 - Prob. 54PBCh. 13.6 - Prob. 55PBCh. 13.6 - Prob. 56PBCh. 13.6 - Prob. 57PBCh. 13.6 - Prob. 58PBCh. 13.6 - Prob. 59PBCh. 13 - House prices This chapter has considered many...Ch. 13 - Prob. 61CPCh. 13 - Prob. 62CPCh. 13 - Prob. 63CPCh. 13 - Prob. 64CPCh. 13 - Prob. 65CPCh. 13 - Prob. 66CPCh. 13 - Prob. 67CPCh. 13 - Prob. 68CPCh. 13 - Prob. 69CPCh. 13 - AIDS and AZT In a study (reported in the New York...Ch. 13 - Factors affecting first home purchase The table...Ch. 13 - Unemployment and GDP Refer to Exercise 13.67. When...Ch. 13 - Prob. 75CPCh. 13 - Prob. 76CPCh. 13 - Prob. 77CPCh. 13 - Prob. 78CPCh. 13 - Prob. 79CPCh. 13 - True or false: Slopes For data on y = college GPA,...Ch. 13 - Prob. 81CPCh. 13 - Lurking variable Give an example of three...Ch. 13 - Prob. 83CPCh. 13 - Prob. 84CPCh. 13 - Prob. 85CPCh. 13 - Logistic versus linear For binary response...Ch. 13 - Prob. 87CPCh. 13 - Prob. 88CPCh. 13 - Prob. 89CPCh. 13 - Prob. 90CPCh. 13 - Prob. 91CPCh. 13 - Prob. 92CPCh. 13 - Prob. 93CP
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
- Reading is fundamental to a teenager's ability to perform well in school. Assume a researcher is interested in the ability of the number of books read over the summer to predict ACT Reading scores. Using a random sample of 10 random high school students the researchers recorded the number of books read over the summer and the students' ACT Reading scores. Books Read (X) ACT Score (Y) (X- Xmean) (Y - Ymean) (X-Xmean)(Y-Ymean) (X-Xmean)? (Y - Ymean)? 2 16 -5.4 -14.9 81 29 221 2 19 -5.4 -11.9 64 29 141 3 17 -4.4 -13.9 61 20 192 4 25 -3.4 -5.9 20 12 34 21 -3.4 -9.9 34 12 97 24 -1.4 -6.9 10 2 47 6 21 -1.4 -9.9 14 2 97 8 24 0.6 -6.9 -4 47 7 27 -0.4 -3.9 2 15 10 22 2.6 -8.9 -23 7 78 Total 52 216 259.1 113.3 969.3 МEAN 7.4 30.9 a. Identify the regression line using the number of books read to predict ACT Reading score. Use a = .05 to evaluate the quality of the prediction of the regression line. b. What is the predicted ACT Reading score when 5 books are read?arrow_forward(a) For United States, provide data for the variables below over the years 1993 –2007:(i) Net migration rate (per 1,000 population)(ii) Total fertility rate (live births per woman)(iii)Unemployment, general level (Thousands)(iv) Wages(v) Life expectancy at birth for both sexes combined (years)Data can be obtained from the UN database http://data.un.org/Explorer.aspxUsing R-Studio, estimate a regression equation to determine the effect of unemployment,general level, wages and life expectancy at birth for both sexes on the net migration rate.(All codes and regression output should be provided).(i) Write down the regression equation. (ii) Interpret the coefficients and determine which of the individual coefficients in theregression model are statistically significant. In responding, construct and test anyappropriate hypothesis. (iii) Interpret the coefficient of determination.arrow_forwardRegression and Predictions. Exercises 13–28 use the same data sets as Exercises 13–28 in Section 10-1. In each case, find the regression equation, letting the first variable be the predictor (x) variable. Find the indicated predicted value by following the prediction procedure summarized in Figure 10-5 on page 493. CPI and the Subway Use the CPI/subway fare data from the preceding exercise and find the best predicted subway fare for a time when the CPI reaches 500. What is wrong with this prediction?arrow_forward
- (a) For United States, provide data for the variables below over the years 1993 – 2007: (i) Net migration rate (per 1,000 population) (ii) Total fertility rate (live births per woman) (iii)Unemployment, general level (Thousands) (iv) Wages (v) Life expectancy at birth for both sexes combined (years) Data can be obtained from the UN database http://data.un.org/Explorer.aspx Using R-Studio, estimate a regression equation to determine the effect of unemployment, general level, wages and life expectancy at birth for both sexes on the net migration rate. (All codes and regression output should be provided). (iv) Using the 10% level of significance, determine and discuss whether the overall regression equation is statistically significant. In responding, construct and test any appropriate hypothesis. (v) Determine and interpret the confidence interval for the independent variable(s).arrow_forward(a) For United States, provide data for the variables below over the years 1993 – 2007: (i) Net migration rate (per 1,000 population) (ii) Total fertility rate (live births per woman) (iii)Unemployment, general level (Thousands) (iv) Wages (v) Life expectancy at birth for both sexes combined (years) Data can be obtained from the UN database http://data.un.org/Explorer.aspx Using R-Studio, estimate a regression equation to determine the effect of unemployment, general level, wages and life expectancy at birth for both sexes on the net migration rate. (All codes and regression output should be provided).(b) Using R-Studio redo the regression analysis with the total fertility rate as an additionalindependent variable. (All codes and regression output should be provided).(i) Write down the regression equation. (ii) Use the 5% level of significance, determine and discuss whether the total fertilityrate has a significant impact on the net migration rate in your assigned country.…arrow_forwardThe least-squares regression equation is y=647.8x+17,858 where y is the median income and x is the percentage of 25 years and older with at least a bachelor's degree in the region. The scatter diagram indicates a linear relation between the two variables with a correlation coefficient of 0.7507. predict the median income of a region in which 20% of adults 25 years and older have at least a bachelor's degree. Round to the nearest dollar as needed.arrow_forward
- The least-squares regression equation is y=761.7x+13,208 where y is the median income and x is the percentage of 25 years and older with at least a bachelor's degree in the region. The scatter diagram indicates a linear relation between the two variables with a correlation coefficient of 0.7483. Predict the median income of a region in which 20% of adults 25 years and older have at least a bachelor's degree.arrow_forwardA study of the mail survey response rate patterns of people aged 60-90 found a prediction equation of = 90.2-0.6xrelating x = age (in years) and y = percentage of people who responded to the survey. Using the linear prediction equation, find the predicted response rate for a (i) 60-year-old and (ii) 90-year-old. The predicted response rate for 60-year-old: The predicted response rate for 90-year-old:arrow_forwardDo all the predictors help to explain Y , or is only a subsetof the predictors useful?arrow_forward
- A prospective cohort study is run to estimate the incidence of stroke in persons 55 years of age and older. All participants are free of stroke at study start. Each participant is followed for a maximum of 5 years. The data are summarized in Table 3–14. Number of Strokes Number of Stroke-Free Person-Years Men (n = 125) 9 478 Women (n = 200) 21 97 What is the annual incidence rate of stroke in men? What is the annual incidence rate of stroke in women? What is the annual incidence rate of stroke (men and women combined)?arrow_forwardIn 1999, the average percentage of women who received prenatal care per country is 80.1%. Table #7.3.9 contains the percentage of woman receiving prenatal care in 2009 for a sample of countries ("Pregnant woman receiving," 2013). Do the data show that the average percentage of women receiving prenatal care in 2009 is higher than in 1999? Test at the 5% level. Table #7.3.9: Percentage of Woman Receiving Prenatal Care 100.0 72.73 74.52 75.79 76.28 76.28 100.0 76.65 80.34 80.60 81.90 86.30 87.70 100.0 87.76 88.40 90.70 91.50 91.80 92.10 100.0 92.20 92.41 92.47 93.00 93.20 93.40 100.0 93.63 93.68 93.80 94.30 94.51 95.00 95.80 95.80 96.23 96.24 97.30 97.90 97.95 98.20 99.00 99.00 99.10 99.10arrow_forward10) A regression was run to determine if there is a relationship between hours of TV watched per day (x) and number of situps a person can do (y).The results of the regression were:y=ax+b a=-0.767 b=31.009 r2=0.609961 r=-0.781 Use this to predict the number of situps a person who watches 7.5 hours of TV can do (to one decimal place)arrow_forward
arrow_back_ios
SEE MORE QUESTIONS
arrow_forward_ios
Recommended textbooks for you
- Algebra & Trigonometry with Analytic GeometryAlgebraISBN:9781133382119Author:SwokowskiPublisher:Cengage
Algebra & Trigonometry with Analytic Geometry
Algebra
ISBN:9781133382119
Author:Swokowski
Publisher:Cengage
The Shape of Data: Distributions: Crash Course Statistics #7; Author: CrashCourse;https://www.youtube.com/watch?v=bPFNxD3Yg6U;License: Standard YouTube License, CC-BY
Shape, Center, and Spread - Module 20.2 (Part 1); Author: Mrmathblog;https://www.youtube.com/watch?v=COaid7O_Gag;License: Standard YouTube License, CC-BY
Shape, Center and Spread; Author: Emily Murdock;https://www.youtube.com/watch?v=_YyW0DSCzpM;License: Standard Youtube License