Concept explainers
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.
17. CSI Statistics Use the shoe print lengths and heights to find the best predicted height of a male who has a shoe print length of 31.3 cm. Would the result be helpful to police crime scene investigators in trying to describe the male?
Want to see the full answer?
Check out a sample textbook solutionChapter 10 Solutions
MyLab Statistics with Pearson eText -- Standalone Access Card -- for Essentials of Statistics
Additional Math Textbook Solutions
Probability and Statistics for Engineering and the Sciences
Introductory Statistics
Fundamentals of Statistics (5th Edition)
Elementary Statistics
Elementary Statistics Using Excel (6th Edition)
STATISTICS F/BUSINESS+ECONOMICS-TEXT
- Sam Jones has 2 years of historical sales data for his company. He is applyingfor a business loan and must supply his projections of sales by month for thenext 2 years to the bank. a. Using the data from Table 6–12, provide a regression forecast for timeperiods 25 through 48.b. Does Sam’s sales data show a seasonal pattern?arrow_forwardMaking Predictions. In Exercises 5–8, let the predictor variable x be the first variable given. Use the given data to find the regression equation and the best predicted value of the response variable. Be sure to follow the prediction procedure summarized in Figure 10-5 on page 493. Use a 0.05 significance level. Bear Measurements Head widths (in.) and weights (lb) were measured for 20 randomly selected bears (from Data Set 9 “Bear Measurements” in Appendix B). The 20 pairs of measurements yield x = 6.9 in., ȳ = 214.3 lb, r = 0.879, P -value = 0.000, and ŷ = −212 + 61.9x. Find the best predicted value of ŷ (weight) given a bear with a head width of 6.5 in.arrow_forwardCity Fuel Consumption: Finding the Best Multiple Regression Equation. In Exercises 9–12, refer to the accompanying table, which was obtained using the data from 21 cars listed in Data Set 20 “Car Measurements” in Appendix B. The response (y) variable is CITY (fuel consumption in mi/gal). The predictor (x) variables are WT (weight in pounds), DISP (engine displacement in liters), and HWY (highway fuel consumption in mi /gal). A Honda Civic weighs 2740 lb, it has an engine displacement of 1.8 L, and its highway fuel consumption is 36 mi/gal. What is the best predicted value of the city fuel consumption? Is that predicted value likely to be a good estimate? Is that predicted value likely to be very accurate?arrow_forward
- City Fuel Consumption: Finding the Best Multiple Regression Equation. In Exercises 9–12, refer to the accompanying table, which was obtained using the data from 21 cars listed in Data Set 20 “Car Measurements” in Appendix B. The response (y) variable is CITY (fuel consumption in mi/gal). The predictor (x) variables are WT (weight in pounds), DISP (engine displacement in liters), and HWY (highway fuel consumption in mi /gal). Which regression equation is best for predicting city fuel consumption? Why?arrow_forwardCity Fuel Consumption: Finding the Best Multiple Regression Equation. In Exercises 9–12, refer to the accompanying table, which was obtained using the data from 21 cars listed in Data Set 20 “Car Measurements” in Appendix B. The response (y) variable is CITY (fuel consumption in mi/gal). The predictor (x) variables are WT (weight in pounds), DISP (engine displacement in liters), and HWY (highway fuel consumption in mi /gal). If exactly two predictor (x) variables are to be used to predict the city fuel consumption, which two variables should be chosen? Why?arrow_forwardAppendix B Data Sets. In Exercises 13–16, refer to the indicated data set in Appendix B and use technology to obtain results. Predicting IQ Score Refer to Data Set 8 “IQ and Brain Size” in Appendix B and find the best regression equation with IQ score as the response (y) variable. Use predictor variables of brain volume and/or body weight. Why is this equation best? Based on these results, can we predict someone’s IQ score if we know their brain volume and body weight? Based on these results, does it appear that people with larger brains have higher IQ scores?arrow_forward
- 10 – 11. Margaret, an archeologist, is conducting a test to determine if there is a positive linear relationship between the total height of a dinosaur and its leg length. Her random sample of 15 dinosaur total heights (in feet) and leg lengths (in feet) produced the results shown in the following TI calculator screen. Use the TI calculations in the screen shot to help you answer questions: 10 & 11. LinReg y=a+bx a=28.67845743 b=5.639892354 r=559696513 r=.7481286741 10. What would you predict for a dinosaur's total height (to 2 decimal places) in feet, if the leg length is 5.8 feet? a) 61.39 feet b) 28.68 feet c) 114.99 feet d) 61.33 feet e) 74.81 feet 11. What percent of variation in the dinosaur's total height can be accounted for by the variation in the dinosaur's leg length? a) 28.68% b) 5.64%% c) 55.97% d) 74.81% e) none of thesearrow_forwardWhat is the relationship between the amount of time statistics students study per week and their final exam scores? The results of the survey are shown below. Time Score 3 4 73 16 2 15 10 3 95 61 67 67 88 90 75 a. Find the correlation coefficient: r = b. The null and alternative hypotheses for correlation are: Hg: ?v = 0 H: ?v + 0 Round to 2 decimal places. The p-value is: (Round to four decimal places) c. Use a level of significance of a = 0.05 to state the conclusion of the hypothesis test in the context of the study. O There is statistically insignificant evidence to conclude that a student who spends more time studying will score higher on the final exam than a student who spends less time studying. O There is statistically significant evidence to conclude that there is a correlation between the time spent studying and the score on the final exam. Thus, the regression line is useful. O There is statistically insignificant evidence to conclude that there is a correlation between the…arrow_forwardSection 10.2 Question #9 The data show the bug chirps per minute at different temperatures. Find the regression equation, letting the first variable be the independent (x) variable. Find the best predicted temperature for a time when a bug is chirping at the rate of 3000 chirps per minute. Use a significance level of 0.05. What is wrong with this predicted value? Chirps in 1 min 981 1023 1074 1101 1203 874 Temperature (°F) 83 79.4 80.9 82.8 92.3 72.8 What is the regression equation? y= ___________+ ___________x (Round the x-coefficient to four decimal places as needed. Round the constant to two decimal places as needed.) What is the best predicted temperature for a time when a bug is chirping at the rate of 3000 chirps per minute? The best predicted temperature when a bug is chirping at 3000 chirps per minute is _________°F. (Round to one decimal place as needed.)arrow_forward
- SECTION 13.3arrow_forwardThe 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…arrow_forwardThe quarterly sales data (number of copies sold) for a college textbook over the past three years follow. a) Construct a time series plot. What type of pattern exists in the data? b) Use a regression model with dummy variables as follows to develop an equation to account for seasonal effects in the data. Qtr1=1 if Quarter 1, 0 otherwise; Qtr2=1 if Quarter 2, 0 otherwise; Qtr3=1 if Quarter 3, 0 otherwise. c) Compute the quarterly forecasts for next year. d) Let t=1 to refer to the observation in quarter 1 of year 1; t=2 to refer to the observation in quarter 2 of year 1; ...; and t=12 to refer to the observation in quarter 4 of year 3. Using the dummy variables defined in part (b) and also using t, develop an equation to account for seasonal effects and any linear trend in the time series. Based upon the seasonal effects in the data and linear trend, compute the quarterly forecasts for next year.arrow_forward
- MATLAB: An Introduction with ApplicationsStatisticsISBN:9781119256830Author:Amos GilatPublisher:John Wiley & Sons IncProbability and Statistics for Engineering and th...StatisticsISBN:9781305251809Author:Jay L. DevorePublisher:Cengage LearningStatistics for The Behavioral Sciences (MindTap C...StatisticsISBN:9781305504912Author:Frederick J Gravetter, Larry B. WallnauPublisher:Cengage Learning
- Elementary Statistics: Picturing the World (7th E...StatisticsISBN:9780134683416Author:Ron Larson, Betsy FarberPublisher:PEARSONThe Basic Practice of StatisticsStatisticsISBN:9781319042578Author:David S. Moore, William I. Notz, Michael A. FlignerPublisher:W. H. FreemanIntroduction to the Practice of StatisticsStatisticsISBN:9781319013387Author:David S. Moore, George P. McCabe, Bruce A. CraigPublisher:W. H. Freeman