Business Statistics: A First Course (8th Edition)
8th Edition
ISBN: 9780135177785
Author: David M. Levine, Kathryn A. Szabat, David F. Stephan
Publisher: PEARSON
expand_more
expand_more
format_list_bulleted
Concept explainers
Question
error_outline
This textbook solution is under construction.
Students have asked these similar questions
a. Round off in 4 decimal places. With complete solution and box the final answer.
The following equation is the result of performing a multiple regression analysis:
Job performance = 10 + (5*job knowledge) + (0.7* conscientiousness), where job
knowledge is measured on a scale of 0-5 and conscientiousness is measured on a
scale of 0 to 100. Which of the following conclusions is correct?
!!
O If a person scored 5 on job knowledge and 100 on conscientiousness he or she
would have the maximum predictive score possible
If a person scored 0 on both job knowledge and conscientiousness, his or her
predictive score is 0
ONeither job knowledge nor conscientiousness predicts performance
O Conscientiousness is less important than job knowledge.
Question 3!
For a measuring tool to be usefulitmus be
10. Terry's best friend, Heather, is the owner of Heather's Hondas. Like Terry, Heather sells
used cars, and she too would like to try to predict the price of a used car based on the
number of miles the car was driven by previous owners. When Heather creates a
scatterplot, she notices a linear relationship between these two variables, and she obtains
the following regression equation.
Predicted price = 18855.05 – 0.101(miles)
Heather also finds that r-squared is equal to 46.9%. This means the correlation (or r)
between price and miles for Heather's sample of cars must be equal to what value?
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_forwardWhat does the y -intercept on the graph of a logistic equation correspond to for a population modeled by that equation?arrow_forwardFor the following exercises, use Table 4 which shows the percent of unemployed persons 25 years or older who are college graduates in a particular city, by year. Based on the set of data given in Table 5, calculate the regression line using a calculator or other technology tool, and determine the correlation coefficient. Round to three decimal places of accuracyarrow_forward
- QUESTION 2 XXX Electric Illuminating Company is doing a survey on the relationship between electricity used in kilowatt-hours (thousand) and the number of rooms in a private single-family residence. A random sample of 10 homes was selected and the electricity consumption recorded as below. ii. Find a suitable linear regression equation ? = ? + ??. iii. Determine the number of kilowatt-hours (thousand) for an eleven-room residence.arrow_forwardConsider the following hypothetical regression: FRIES = 22.5 + 0.08*TRAFFIC + 9.1*COUPON? + -1.1*TEMP where FRIES is the number of pounds of fries a restaurant sells in a week, TRAFFIC is the number of people who walked by the restaurant that week (foot traffic), COUPON? is a dummy variable of if the restaurant offered a coupon or not that week (1=coupon, 0=no coupon); and TEMP is the average high that week, measured in Fahrenheit. All variables are statistically significant. If the average high is expected to be 4 degrees warmer next week, how should FRIES change? 1 Increase by 18.1 pounds 2 Decrease by 4.4 pounds 3 It is impossible to tell without knowing the values of TRAFFIC and COUPON?. 4 Increase by 4.4 pounds 5 Increase by 26.9 poundsarrow_forward1. An agent for a residential real estate company in a large city would like to be able to predict the monthly rental cost for apartments, based on the size of an apartment, as defined by square footage. The agent selects a sample of 25 apartments in a particular residential neighborhood and gathers the following data a. determine the coefficient of determination, and interpret its meaning. b. determine the standard error of the estimate. c. How useful do you think this regression model is for predicting the monthly rent?arrow_forward
- a. What is a residual? b. In what sense is the regression line the straight line that "best" fits the points in a scatterplot? a. What is a residual? OA. Aresidual is a point that has a strong effect on the regression equation. OB. A residual is the amount that one variable changes when the other variable changes by exactly one unit. OC. Aresidual is a value that is determined exactly, without any error. OD. Aresidual is a value of y - y, which is the difference betvween an observed value of y and a predicted value of y. b. In what sense is the regression line the straight line that "best" fits the points in a scatterplot? The regression line has the property that the of the residuals is the possible sum.arrow_forwardSuppose the estimaited OLS regression is: Happiness = a + b*dailychocolates Now use chocolate consumprion per week instead of days. What is the relationship between the old and new units? How would this affect b (i.e. what is bnew in terms of b)?arrow_forwarda. What is a residual? b. In what sense is the regression line the straight line that "best" fits the points in a scatterplot? a. What is a residual? A. A residual is a value of y−y, which is the difference between an observed value of y and a predicted value of y. B. A residual is a value that is determined exactly, without any error. C. A residual is the amount that one variable changes when the other variable changes by exactly one unit. D. A residual is a point that has a strong effect on the regression equation. b. In what sense is the regression line the straight line that "best" fits the points in a scatterplot? The regression line has the property that the ▼ sum of squares sum of the residuals is the ▼ lowest highest possible sum.arrow_forward
- 32)arrow_forwardPlease show all work/stepsarrow_forwardSuppose you are examining a multi-variable linear regression model that was designed to predict the weight of a person, measured in kg, using 3 predictor variables. One of the variables used in this analysis is "height", with the coefficient of this variable being equal to 3.96, with a standard error of the coefficient equal to 1.168. There are 300 datapoints in the dataset. Using this information, what would be the test statistic (t-ratio) for the test to see if the variable "height" is significant? Only round final answer. Round to two decimal places.arrow_forward
arrow_back_ios
SEE MORE QUESTIONS
arrow_forward_ios
Recommended textbooks for you
- Glencoe Algebra 1, Student Edition, 9780079039897...AlgebraISBN:9780079039897Author:CarterPublisher:McGraw HillFunctions and Change: A Modeling Approach to Coll...AlgebraISBN:9781337111348Author:Bruce Crauder, Benny Evans, Alan NoellPublisher:Cengage Learning
- Linear Algebra: A Modern IntroductionAlgebraISBN:9781285463247Author:David PoolePublisher:Cengage LearningCollege AlgebraAlgebraISBN:9781305115545Author:James Stewart, Lothar Redlin, Saleem WatsonPublisher:Cengage LearningBig Ideas Math A Bridge To Success Algebra 1: Stu...AlgebraISBN:9781680331141Author:HOUGHTON MIFFLIN HARCOURTPublisher:Houghton Mifflin Harcourt
Glencoe Algebra 1, Student Edition, 9780079039897...
Algebra
ISBN:9780079039897
Author:Carter
Publisher:McGraw Hill
Functions and Change: A Modeling Approach to Coll...
Algebra
ISBN:9781337111348
Author:Bruce Crauder, Benny Evans, Alan Noell
Publisher:Cengage Learning
Linear Algebra: A Modern Introduction
Algebra
ISBN:9781285463247
Author:David Poole
Publisher:Cengage Learning
College Algebra
Algebra
ISBN:9781305115545
Author:James Stewart, Lothar Redlin, Saleem Watson
Publisher:Cengage Learning
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