Introduction to Probability and Statistics
14th Edition
ISBN: 9781133103752
Author: Mendenhall, William
Publisher: Cengage Learning
expand_more
expand_more
format_list_bulleted
Concept explainers
Videos
Question
Chapter 12.5, Problem 12.23E
a)
To determine
To find the least square regression line relating the number of chirps to temperature.
b)
To determine
To test if data provide sufficient evidence to indicate that there is a linear relationship between number of chirps and temperature.
c)
To determine
To calculate r2 and interpret it.
Expert Solution & Answer
Want to see the full answer?
Check out a sample textbook solution
Students have asked these similar questions
A box office analyst seeks to predict opening weekend box office gross for movies. Toward this goal, the analyst plans to use online trailer views as a predictor. For each of the 66 movies, the number of online trailer views from the release of the trailer through the Saturday before a movie opens and the opening weekend box office gross (in millions of dollars) are collected and stored in the accompanying table. The least-squares regression equation for these data is
Yi=−1.606+1.428Xi and the standard error of the estimate is SYX=19.348.
Assume that the straight-line model is appropriate and there are no serious violations the assumptions of the least-squares regression model. Complete parts (a) and (b) below.
a. At the 0.01 level of significance, is there evidence of a linear relationship between online trailer views and opening weekend box office gross?
Determine the hypotheses for the test.
b. Construct a 95%confidence interval estimate of the population…
The operations manager of a musical instrument distributor feels that demand for a particular type of guitar may be related to the number of YouTube views for a music video by the popular rock group Marble Pumpkins during the preceding month. The manager has collected the data shown in the following table:
YouTube views (1,000s)
Guitar Sales
30
8
40
11
70
12
60
10
80
15
50
13
a. Using the equations presented in this chapter, compute the SST, SSE, and SSR. Find the least-squares regression line for these data (use manual computations)
b. Using the regression equation, predict guitar sales if there were 40,000 views last month (use manual computations)
A box office analyst seeks to predict opening weekend box office gross for movies. Toward this goal, the analyst plans to use online trailer views as a predictor. For each of the 66 movies, the number of online trailer views from the release of the trailer through the Saturday before a movie opens and the opening weekend box office gross (in millions of dollars) are collected and stored in the accompanying table. The least-squares regression equation for these data is Yi=−1.068+1.394Xi and the standard error of the estimate is SYX=19.412. Assume that the straight-line model is appropriate and there are no serious violations the assumptions of the least-squares regression model. Significance level at 0.05 . Complete parts (a) and (b) below.
Chapter 12 Solutions
Introduction to Probability and Statistics
Ch. 12.4 - Prob. 12.1ECh. 12.4 - Prob. 12.2ECh. 12.4 - Prob. 12.3ECh. 12.4 - Prob. 12.4ECh. 12.4 - Prob. 12.5ECh. 12.4 - You are given five points with these coordinates:...Ch. 12.4 - Prob. 12.7ECh. 12.4 - Prob. 12.8ECh. 12.4 - Prob. 12.9ECh. 12.4 - Prob. 12.10E
Ch. 12.4 - Prob. 12.11ECh. 12.4 - Prob. 12.12ECh. 12.4 - Prob. 12.13ECh. 12.4 - Prob. 12.14ECh. 12.4 - Prob. 12.15ECh. 12.4 - Prob. 12.16ECh. 12.4 - Prob. 12.17ECh. 12.5 - Prob. 12.19ECh. 12.5 - Prob. 12.20ECh. 12.5 - Prob. 12.21ECh. 12.5 - Prob. 12.22ECh. 12.5 - Prob. 12.23ECh. 12.5 - Prob. 12.24ECh. 12.5 - Professor Asimov, continued Refer to thedata in...Ch. 12.5 - Prob. 12.26ECh. 12.5 - Prob. 12.27ECh. 12.5 - Prob. 12.28ECh. 12.5 - Prob. 12.29ECh. 12.5 - Prob. 12.30ECh. 12.6 - Prob. 12.34ECh. 12.6 - Prob. 12.35ECh. 12.6 - Prob. 12.36ECh. 12.6 - Prob. 12.37ECh. 12.6 - Prob. 12.38ECh. 12.7 - Refer to Exercise 12.7. Portions of the MINITAB...Ch. 12.7 - Prob. 12.41ECh. 12.7 - Prob. 12.42ECh. 12.7 - Prob. 12.43ECh. 12.7 - Prob. 12.44ECh. 12.7 - Prob. 12.45ECh. 12.7 - Prob. 12.46ECh. 12.8 - Prob. 12.50ECh. 12.8 - Prob. 12.51ECh. 12.8 - Prob. 12.52ECh. 12.8 - Prob. 12.53ECh. 12.8 - Prob. 12.55ECh. 12.8 - Prob. 12.56ECh. 12.8 - Prob. 12.58ECh. 12.8 - Baseball Stats Does a team’s batting average...Ch. 12 - Prob. 12.62SECh. 12 - Prob. 12.63SECh. 12 - Prob. 12.65SECh. 12 - Prob. 12.66SECh. 12 - Prob. 12.67SECh. 12 - Tennis, Anyone? If you play tennis, you know that...Ch. 12 - Prob. 12.69SECh. 12 - Prob. 12.70SECh. 12 - Prob. 12.71SECh. 12 - Movie Reviews How many weeks cana movie run and...Ch. 12 - In addition to increasingly large bounds onerror,...Ch. 12 - Prob. 12.74SECh. 12 - Prob. 12.76SECh. 12 - Prob. 1CSCh. 12 - Prob. 2CSCh. 12 - Prob. 3CS
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
- 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?arrow_forwardThe following fictitious table shows kryptonite price, in dollar per gram, t years after 2006. t= Years since 2006 0 1 2 3 4 5 6 7 8 9 10 K= Price 56 51 50 55 58 52 45 43 44 48 51 Make a quartic model of these data. Round the regression parameters to two decimal places.arrow_forwardThe basal metabolic rate (kcal/day) of large anteaters is believed to be subject to a power law relationship with its weight (kg). A study was performed measuring several anteaters and reported the following data: Weight (kg) 6.0 28.5 16.0 19.0 23.5 11.0 9.0 25.5 22.0 BMR (kcal/day) 80.1 247.0 162.3 172.4 215.1 111.9 104.6 224.6 208.3 Transform the data correctly to perform a linear least-squares regression, then report your model as y=cxpy=cxp. Estimate the BMR for a 27 kg anteater.arrow_forward
- The U.S. Postal Service is attempting to reduce the number of complaints made by the public against its workers. To facilitate this task, a staff analyst for the service regresses the number of complaints lodged against an employee last year on the hourly wage of the employee for the year. The analyst ran a simple linear regression in SPSS. The results are shown below. Table 7: Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .854a .730 .695 6.6235 a. Predictors: (Constant), Hourly Wage Table 8: ANOVA ANOVAb Model Sum of Squares df Mean Square F Sig. 1 Regression 1918.458 1 1918.458 129.783 .000a Residual 709.567 48 14.782 Total 2628.025 49 a. Predictors: (Constant), Hourly Wage b. Dependent Variable: Number of Complaints Table 9: Coefficients Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t…arrow_forwardThe U.S. Postal Service is attempting to reduce the number of complaints made by the public against its workers. To facilitate this task, a staff analyst for the service regresses the number of complaints lodged against an employee last year on the hourly wage of the employee for the year. The analyst ran a simple linear regression in SPSS. The results are shown below. Table 7: Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .854a .730 .695 6.6235 a. Predictors: (Constant), Hourly Wage Table 8: ANOVA ANOVAb Model Sum of Squares df Mean Square F Sig. 1 Regression 1918.458 1 1918.458 129.783 .000a Residual 709.567 48 14.782 Total 2628.025 49 a. Predictors: (Constant), Hourly Wage b. Dependent Variable: Number of Complaints Table 9: Coefficients Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t…arrow_forwardThe average number of people in a family attending college for various years is given in the table below: Year 1969 1973 1975 1979 1983 1988 1991 # Attending College 4.0 3.6 3.2 3.0 3.0 3.0 2.9 Calculate the least-squares regression line. Give the slope accurate to 4 decimal places.arrow_forward
- A box office analyst seeks to predict opening weekend box office gross for movies. Toward this goal, the analyst plans to use online trailer views as a predictor. For each of the 66 movies, the number of online trailer views from the release of the trailer through the Saturday before a movie opens and the opening weekend box office gross (in millions of dollars) are collected and stored in the accompanying table. The least-squares regression equation for these data isYi=−0.914+1.408Xi and the standard error of the estimate is SYX=19.887. Assume that the straight-line model is appropriate and there are no serious violations the assumptions of the least-squares regression model. Compute the test statistic.arrow_forwardA mail-order business selling personal computer supplies, software and hardware maintains a centralized warehouse. Management is currently examining the process of distribution from the warehouse and wants to study the factors that affect the warehouse distribution costs. Data collected over 24 random months contain the warehouse’s distribution cost (in thousands of Rands), the sales (in thousands of Rands) and the number of orders received. A multiple linear regression model was fitted to the data by using Stat1.2. Use the output to answer the questions that follow by typing only the letter of the correct option in the answer boxes. Variablesy: Warehouse Distribution Costx1: Salesx2: Number of Orders Model Fitting StatisticsR2 = 0.8504Adj R2: ? Regression Coefficients Beta Parameter Standard b Parameter Standard Estimates…arrow_forwardA company trains its employees with instructional videos and claims that the amount of time, in hours, spent training is linearly related to an increase in productivity. The company selected a random sample of five employees to test its claim. The data were used to create the computer output for a least-squares linear regression, shown in the table. Variable DF Estimate SE Intercept 1 3.6 1.1489 Hours 1 0.8 0.3464 Which of the following is the correct test statistic and number of degrees of freedom? t=2.31 with 4 degrees of freedom A t=2.31 with 3 degrees of freedom B t=2.31 with 5 degrees of freedom C t=3.13 with 1 degree of freedom D t=3.13 with 3 degrees of freedom Earrow_forward
- An auto manufacturing company wanted to investigate how the price of one of its car models depreciates with age. The research department at the company took a sample of eight cars of this model and collected the following information on the ages (in years) and prices (in hundreds of dollars) of these cars. Age 8 8 5 2 6 5 2 2 Price 38 19 53 70 40 51 80 80 1.) Find the least squares regression line equation in the form ^ = a+bx. y Use "Age" as the independent variable and "Price" as the dependent variable. 2.) Predict the price of a 5 year old car of this model. ypred=arrow_forwardWe wish to predict the salary for baseball players (y) using the variables RBI (x1x1) and HR (x2x2), then we use a regression equation of the form ˆy=b0+b1x1+b2x2. HR - Home runs - hits on which the batter successfully touched all four bases, without the contribution of a fielding error. RBI - Run batted in - number of runners who scored due to a batters's action, except when batter grounded into double play or reached on an error Salary is in millions of dollars. The following is a chart of baseball players' salaries and statistics from 2016. Player Name RBI's HR's Salary (in millions) Miquel Cabrera 108 38 28.050 Yoenis Cespedes 86 31 27.500 Ryan Howard 59 25 25.000 Albert Pujols 119 31 25.000 Robinson Cano 103 39 24.050 Mark Teixeira 44 15 23.125 Joe Mauer 49 11 23.000 Hanley Ramirez 111 30 22.750 Justin Upton 87 31 22.125 Adrian Gonzalez 90 18 21.857 Jason Heyward 49 7 21.667 Jayson Werth 70 21 21.571 Matt Kemp 108 35 21.500 Jacoby Ellsbury 56 9…arrow_forwardThe systolic blood pressure of individuals is thought to be related to both age and weight. For a random sample of 11 men, the following data were obtained. Systolic Blood pressuex1 Age (years)x2 Weight (pounds)x3 132 52 173 143 59 184 153 67 194 162 73 211 154 64 196 168 74 220 137 54 188 149 61 188 159 65 207 128 46 167 166 72 217 a) Look at the coefficients of the regression equation. Write out the regression equation. (Use 3 decimal places.) x1 = _____ + _____ x2 + ____x3 b) If age were held fixed, but a person put on 10 pounds, what would you expect for the corresponding change in systolic blood pressure? (Use 2 decimal places.)c) If a person kept the same weight but got 10 years older, what would you expect for the corresponding change in systolic blood pressure? (Use 2 decimal places.)arrow_forward
arrow_back_ios
SEE MORE QUESTIONS
arrow_forward_ios
Recommended textbooks for you
College AlgebraAlgebraISBN:9781305115545Author:James Stewart, Lothar Redlin, Saleem WatsonPublisher:Cengage Learning
Elementary Linear Algebra (MindTap Course List)AlgebraISBN:9781305658004Author:Ron LarsonPublisher:Cengage Learning
Functions and Change: A Modeling Approach to Coll...AlgebraISBN:9781337111348Author:Bruce Crauder, Benny Evans, Alan NoellPublisher:Cengage Learning
College Algebra
Algebra
ISBN:9781305115545
Author:James Stewart, Lothar Redlin, Saleem Watson
Publisher:Cengage Learning
Elementary Linear Algebra (MindTap Course List)
Algebra
ISBN:9781305658004
Author:Ron Larson
Publisher:Cengage Learning
Functions and Change: A Modeling Approach to Coll...
Algebra
ISBN:9781337111348
Author:Bruce Crauder, Benny Evans, Alan Noell
Publisher:Cengage Learning
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