BASIC BUSINESS STATISTICS-STUD.SOLN.MAN
14th Edition
ISBN: 9780134685045
Author: BERENSON
Publisher: PEARSON
expand_more
expand_more
format_list_bulleted
Concept explainers
Videos
Question
Chapter 15, Problem 10PS
a.
To determine
Find regression equation for the provided scenario.
b.
To determine
Test whether there is significant relationship between calories, percentage of alcohol, and number of carbohydrates.
c.
To determine
Interpret the meaning of coefficient of multiple determination.
d.
To determine
Compute the adjusted
e.
To determine
Compare the above results with those in the Problem 15.4.
Expert Solution & Answer
Want to see the full answer?
Check out a sample textbook solution
Students have asked these similar questions
Ex.1. A fuel company wants to state the relationship between the amount of gas it sells for heating and the daily temperature. The following table on sales and temperature is given to show X daily average temperature, Y gas sales amount. Find the regression line of Y on X.
An engineer performed an experiment to determine the effect of CO2 pres-
sure, CO, temperature, peanut moisture, CO2 flow rate, and peanut particle
size on the total yield of oil per batch of peanuts. Table B.7 summarizes the
experimental results.
e. Find a 95% CI for the regression coefficient for temperature for both
models in part d. Discuss any differences.
Given below are results from the regression analysis where the dependent variable is the number of weeks a worker is unemployed due to a layoff (Unemploy) and the independent variables are the age of the worker (Age), the number of years of education received (Edu), the number of years at the previous job (Job Yr), a dummy variable for marital status (Married:
1=married,
0=otherwise),
a dummy variable for head of household (Head:
1=yes,
0=no)
and a dummy variable for management position (Manager:
1=yes,
0=no).
We shall call this Model 1. The coefficient of partial determination
(R2Yj.(All variables except j))
of each of the six predictors are, respectively, 0.2807, 0.0386, 0.0317, 0.0141, 0.0958, and 0.1201. Model 2 is the regression analysis where the dependent variable is Unemploy and the independent variables are Age and Manager. The results of the regression analysis are given. Refer to model 1. Which of the following is the correct null hypothesis to test…
Chapter 15 Solutions
BASIC BUSINESS STATISTICS-STUD.SOLN.MAN
Ch. 15 - The following is the quadratic regression equation...Ch. 15 - Business actively recruit business student with...Ch. 15 - A study was conducted on automobile engines to...Ch. 15 - Prob. 4PSCh. 15 - In the production of printed circuit boards,...Ch. 15 - An automotive sales manager wishes to examine the...Ch. 15 - Researchers wanted to investigate the relationship...Ch. 15 - Prob. 8PSCh. 15 - Prob. 9PSCh. 15 - Prob. 10PS
Ch. 15 - Using the data of Problem 15.4 on page 600, stored...Ch. 15 - Using the data of Problem 15.6 on page 601, stored...Ch. 15 - Using the data of Problem 15.6 on page 601 stored...Ch. 15 - If the coefficient of determination between two...Ch. 15 - If the coefficient of determination between two...Ch. 15 - Prob. 16PSCh. 15 - Refer to Problem 14.5 on page 542. Perform a...Ch. 15 - Refer to Problem 14.6 on page 542. Perform a...Ch. 15 - Refer to Problem 14.7 on page 542. Perform a...Ch. 15 - Refer to Problem 14.8 on page 542. Perform a...Ch. 15 - Prob. 21PSCh. 15 - Prob. 22PSCh. 15 - Prob. 23PSCh. 15 - You need to develop a model to predict the asking...Ch. 15 - Accounting Today identified top public accounting...Ch. 15 - How can you evaluate whether collinearity exists...Ch. 15 - Prob. 27PSCh. 15 - Prob. 28PSCh. 15 - A Specialist in baseball analytics has expanded...Ch. 15 - In the production of printed circuit boards,...Ch. 15 - Hemlock Farms is a community located in the Pocono...Ch. 15 - Prob. 32PSCh. 15 - Prob. 33PSCh. 15 - Prob. 34PSCh. 15 - You are a real estate broker who wants to compare...Ch. 15 - You are a real estate broker who wants to compare...Ch. 15 - Financial analysts engage in business valuation to...Ch. 15 - Prob. 38PSCh. 15 - A molding machine that contains different cavities...Ch. 15 - The file Cites contains a sample of 25 cities in...Ch. 15 - In problem 15.32-15.36 you developed multiple...
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
- The 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_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_forwardWhat does the y -intercept on the graph of a logistic equation correspond to for a population modeled by that equation?arrow_forward
- Respiratory Rate Researchers have found that the 95 th percentile the value at which 95% of the data are at or below for respiratory rates in breath per minute during the first 3 years of infancy are given by y=101.82411-0.0125995x+0.00013401x2 for awake infants and y=101.72858-0.0139928x+0.00017646x2 for sleeping infants, where x is the age in months. Source: Pediatrics. a. What is the domain for each function? b. For each respiratory rate, is the rate decreasing or increasing over the first 3 years of life? Hint: Is the graph of the quadratic in the exponent opening upward or downward? Where is the vertex? c. Verify your answer to part b using a graphing calculator. d. For a 1- year-old infant in the 95 th percentile, how much higher is the walking respiratory rate then the sleeping respiratory rate? e. f.arrow_forwardTable 6 shows the population, in thousands, of harbor seals in the Wadden Sea over the years 1997 to 2012. a. Let x represent time in years starting with x=0 for the year 1997. Let y represent the number of seals in thousands. Use logistic regression to fit a model to these data. b. Use the model to predict the seal population for the year 2020. c. To the nearest whole number, what is the limiting value of this model?arrow_forwardFor the following exercises, consider the data in Table 5, which shows the percent of unemployed ina city of people 25 years or older who are college graduates is given below, by year. 40. Based on the set of data given in Table 6, calculate the regression line using a calculator or other technology tool, and determine the correlation coefficient to three decimal places.arrow_forward
- In a multiple regression setting which of the following statements is NOT correct? Select one: a. The estimated slope coefficient for the explanatory variable x1 represents how much the outcome variable changes when x1 increases by one unit and all other variables remain the same. b. Suppose you are interested in the relationship between food consumption and level of household income. Even though your primary interest is in the consumption-income relationship it is good practice to add in other explanatory variables such as household size in order to avoid problems of confoundment. c. As you add more explanatory variables to a multiple regression model you expect the standard error of the estimate to increase. d. With a large enough sample you can treat the estimated regression coefficients as if they are normally distributed irrespective of the underlying distribution of the error term.arrow_forwardTo assess the relationship between monthly sales of a product and monthly advertising expenditure(both in thousands of dollars); a linear regression model was fitted using data for 20 months and results were as follows: s.e(8) Variable B Intercept 6.3812 advert 1.1762 0.0786 (a) From the results, we can say that an increase of 1000 dollars in monthly expenditure is associated with an increase of 1.1762 dollars in average sales. an increase of 1.1762 dollars in monthly expenditure is associated with an increase of one dollar in average selling price. an increase of 1000 dollars in monthly expenditure is associated with an increase of 1176.2 dollars in average sales. an increase of one dollar in monthly expenditure is associated with an increase of 1.1762 dollars in average sales. <arrow_forwardA sales manager collected the following data on x = years of experience and y = annual sales ($1,000s). The estimated regression equation for these data is ý = 81 + 4x. Years of Annual Sales Salesperson Experience ($1,000s) 80 3 97 3 102 4 4 102 103 6. 8 101 10 119 8 10 123 9. 11 127 10 13 136 (a) Compute SST, SSR, and SSE. SST = SSR = SSE = (b) Compute the coefficient of determination r2. (Round your answer to three decimal places.) r2 = Comment on the goodness of fit. (For purposes of this exercise, consider a proportion large if it is at least 0.55.) O The least squares line did not provide a good fit as a small proportion of the variability in y has been explained by the least squares line. The least squares line provided a good fit as a large proportion of the variability in y has been explained by the least squares line. The least squares line provided a good fit as a small proportion of the variability in y has been explained by the least squares line. The least squares line did…arrow_forward
- Suppose Connie used regression to predict the height of a woman’s current boyfriend by using her own height as the explanatory variable. Height was measured in feet from a sample of 100 women undergraduates, and their boyfriends, at De La Salle Lipa. Now, suppose that the height of both the women and the men are converted to centimeters. The impact of this conversion on the slope is: the magnitude of the slope will change neither the sign nor magnitude of the slope will change the sign of the slope will change both the sign and magnitude of the slope will changearrow_forwardA sales manager collected the following data on x = years of experience and y = annual sales ($1,000s). The estimated regression equation for these data is ŷ = 80 + 4x. Years of Experience Annual Sales ($1,000s) Salesperson 1 2 3 4 5 6 7 8 9 10 (a) Compute SST, SSR, and SSE. SST = SSR = SSE = 1 3 4 4 6 8 10 10 11 13 80 97 97 102 103 101 119 118 127 136 (b) Compute the coefficient of determination. (Round your answer to three decimal places.) 2= Comment on the goodness of fit. (For purposes of this exercise, consider a proportion large if it is at least 0.55.) The least squares line provided a good fit as a large proportion of the variability in y has been explained by the least squares line. The least squares line did not provide a good fit as a large proportion of the variability in y has been explained by the least squares line. The least squares line provided a good fit as a small proportion of the variability in y has been explained by the least squares line. The least squares line…arrow_forward
arrow_back_ios
arrow_forward_ios
Recommended textbooks for you
Calculus For The Life SciencesCalculusISBN:9780321964038Author:GREENWELL, Raymond N., RITCHEY, Nathan P., Lial, Margaret L.Publisher:Pearson Addison Wesley,
College AlgebraAlgebraISBN:9781305115545Author:James Stewart, Lothar Redlin, Saleem WatsonPublisher:Cengage Learning
Algebra & Trigonometry with Analytic GeometryAlgebraISBN:9781133382119Author:SwokowskiPublisher:Cengage
Algebra and Trigonometry (MindTap Course List)AlgebraISBN:9781305071742Author:James Stewart, Lothar Redlin, Saleem WatsonPublisher:Cengage Learning
Functions and Change: A Modeling Approach to Coll...AlgebraISBN:9781337111348Author:Bruce Crauder, Benny Evans, Alan NoellPublisher:Cengage Learning
Calculus For The Life Sciences
Calculus
ISBN:9780321964038
Author:GREENWELL, Raymond N., RITCHEY, Nathan P., Lial, Margaret L.
Publisher:Pearson Addison Wesley,
College Algebra
Algebra
ISBN:9781305115545
Author:James Stewart, Lothar Redlin, Saleem Watson
Publisher:Cengage Learning
Algebra & Trigonometry with Analytic Geometry
Algebra
ISBN:9781133382119
Author:Swokowski
Publisher:Cengage
Algebra and Trigonometry (MindTap Course List)
Algebra
ISBN:9781305071742
Author:James Stewart, Lothar Redlin, Saleem Watson
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