Pearson eText Business Statistics: First Course -- Instant Access (Pearson+)
8th Edition
ISBN: 9780136880974
Author: David Levine, David Stephan
Publisher: PEARSON+
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The following three problems refer to the data sets displayed below:
++++
a
b
8. Which data set has the greatest slope of the least squares regression line?
PROBLEM :The amount spent on medical expenses per day is correlated with other health factors for 5 adult males. A study came up with the following table:
Medical Amount spentcost for alcohol Weight Age2100 200 185 502378 250 200 421657 100 175 372584 200 225 542658 250 220 32
Which of the following factors has a significant effect on medical expenses? Create the regression equation for the problem.
13. Examine the following regression equation and answer the questions that follow:
Salary = 261,128 +91,569Goals + 16,346Assists - 585,560.Defenseman
(2.789) (9.641)
(3.301) (5.001)
R² = 0.65
Salary = NHL Player's Salary in $
Goals Number of goals scored by that player
Assists = Number of assists made by that player
Defenseman = Takes on a value of 1 if player is a defenseman. Otherwise, the value of this
variable is zero.
The numbers in brackets are t-statistics for the variables above them.
(a)
What salary will an offensive player make that scores no goals and has no assists?
Interpret the meaning of the R² in words.
(b)
(c)
What is the increase in salary from scoring one more goal (holding the number of assists
constant)?
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- Problem 3: North America Airlines maintains an extensive inventory of wing flaps. The number of wing flaps needed to replace damaged wing flaps each month depends partially on the number of flying hours for the fleet that month. Flying 162 149 185 171 138 154 Hours (1000s) Number 12 13 14 10 11 of Wing Flaps 1. Determine the number of wing flaps needed for 150,000 flying hours. 2. Is the regression model a good model? Why?arrow_forwardMark Price, the new productions manager for Speakers and Company, needs to find out which variable most affects the demand for their line of company speakers. He is uncertain whether the unit price of the product or the effects of increased marketing are the main drivers in sales and wants to use regression analysis to figure out which factor drives more demand for its particular market. Pertinent information was collected by an extensive marketing project that lasted over the past 12 years and was reduced to the data that follow: YEAR 1 2 3 4 5 6 7 B 9 10 11 12 UNIT SALES (THOUSANDS) 398 698 898 y bar 1,304 1,163 1,195 898 1,200 982 1,235 875 811 PRICE $ PER UNIT 283 209 213 220 209 200 220 209 230 213 216 243 ADVERTISING ($000) 621 821 1,200 1,405 1,210 1,304 875 1,200 691 875 691 691 a. Perform a regression analysis based on these data using Excel. Note: Negative values should be indicated by a minus sign. Round your answers to 4 decimal places. Price advertisingarrow_forwardMark Price, the new productions manager for Speakers and Company, needs to find out which variable most affects the demand for their line of stereo speakers. He is uncertain whether the unit price of the product or the effects of increased marketing are the main drivers in sales and wants to use regression analysis to figure out which factor drives more demand for its particular market. Pertinent information was collected by an extensive marketing project that lasted over the past 12 years and was reduced to the data that follow: YEAR 123456789 10 11 12 UNIT SALES (THOUSANDS) 396 696 896 1,285 1,155 1,186 896 1,118 985 1,334 926 820 PRICE $/UNIT 282 219 206 210 219 209 218 206 220 206 226 236 ADVERTISING ($000) 600 829 1,118 1,413 1,214 1,285 907 1,118 708 907 708 682 a. Perform a regression analysis based on these data using Excel. (Negative values should be indicated by a minus sign. Round your answers to 4 decimal places.)arrow_forward
- Consider the following combinations of average test score and the average student-teacher ratio in some fictional school districts. Student-teacher ratio (X): 15. 17, 19, 20, 22. 23.5. 25 680, 640, 670, 660. 630, 660, 635 Test-score (Y): Estimate the linear regression line. Oy=-713.57+2.9x O y=-2.97+713.57X Oy=2.97+713.57x O y=713.57-2.97x QUESTION 2 (Continued) Test the hypothesis HB₁=0 vs. H_ :B₁ #0 1 O T=-1.51, P-value=0.38 O T=-1.51, P-value=0.19 O T=1.51, P-value=0.19 O T=1.51, P-value=0.38 QUESTION 3 (Continued) Compute R² and interpret it. 0.31: About 31% of variation in test score is explained by variation in student-teacher ratio through the model. 0.31: About 31% of variation in student-teacher ratio is explained by variation in test score through the model. O 0.69: About 69% of variation in test score is explained by variation in student-teacher ratio through the model. O 0.69: About 89% of variation in student-teacher ratio is explained by variation in test score through…arrow_forward3) What is the use of regression analysis (Please support your answer with a suitable example)? Driving Experience (years) Monthly Auto Insurance Premium 5 $64 2 87 12 50 9 71 15 44 6 56 25 42 16 60 Does the insurance premium depend on the driving experience or does the driving experience depend on the insurance premium? Do you expect a positive or a negative relationship between these two variables?arrow_forwardPlease write the solution and answer. Thank you!arrow_forward
- 1. Consider the following regression model: Fram Risk Score; = Bo + B1 × Health Insurance; + u¿ The Framingham Risk Score predicts 10-year risk of cardiovascular disease based on age, cholesterol levels, blood pressure, blood sugar, use of medication for high blood pressure, and smoking. A higher score means worse overall cardiovascular health. A researcher who collects data and regresses the Fram Risk Score against Health Insurance (= 1 if have insurance) finds that B, 2. Consider the following regression model: Class Average; = Bo + B1 x Office Hours; + u; Class Average is the students' average grade in the class at the end of the term and Office Hours is the number of office hours held by the instructor over the entire term. A researcher who collects data and regresses Class Average against Office Hours finds that, surprisingly, B V A V Aarrow_forwardUse the following data to answer Questions a, b, and c. a) Determine the correlation coefficient between the percentage of people who get greater than 7 hours of sleep and the percentage who score in the 95th percentile on cognitive tests. b) Find the equation of the regression line for the percentage who get greater than 7 hours of sleep and the percentage who score in the 95th percentile on cognitive tests. c) What percentage of people can we anticipate score in the 95th percentile on cognitive tests in a generation where 27% get greater than 7 hours of sleep? Generation Millennials Gen X Boomers Gen Y Percentage who get greater than 7 hours of sleep (x) 41 Millennials 53 Gen X 64 Boomers 68 Gen y Percentage who score high (95th percentile) on cognitive tests (y) 39 Millennials 47 Gen X 56 Boomers 66 Gen Yarrow_forwardPROBLEM : The amount spent on medical expenses per day is correlated with other health factors for 5 adult males. A study came up with the following table: Medical amount spent Age 50 cost for alcohol weight 2100 200 185 2378 250 200 42 1657 100 175 37 2584 200 225 54 2658 250 220 32 Which of the following factors has a significant effect on medical expenses? Create the regression equation for the problem.arrow_forward
- Mark Price, the new productions manager for Speakers and Company, needs to find out which variable most affects the demand for their line of stereo speakers. He is uncertain whether the unit price of the product or the effects of increased marketing are the main drivers in sales and wants to use regression analysis to figure out which factor drives more demand for their particular market. Pertinent information was collected by an extensive marketing project that lasted over the past 10 years and was reduced to the data that follow: YEAR SALES/UNIT (THOUSANDS) PRICE $/UNIT ADVERTISING ($000) 1998 395 278 605 1999 695 219 831 2000 895 216 1,104 2001 1,286 207 1,404 2002 1,147 219 1,203 2003 1,180 193 1,286 2004 895 222 884 2005 1,104 202 1,104 2006 974 220 696 2007 1,238 216 884 2008 884 220 696 2009 804 247 696arrow_forwardThe residuals for data set A and data set B were calculated and plotted on separate residual plots. If the residuals for data set A do not form a pattern and the residuals for data set B do not form a pattern, what can be concluded? OA. A linear model is a good fit for data set A but not data set B. O B. A linear model is a good fit for data set B but not data set A. AC. A linear model is a good fit for both data sets. D. A linear model is not a good fit for either data set.arrow_forward1. The scatterplot below contains high school and university grades for 105 computerscience majors at Big Ten Universities. We now consider how we could predict astudent’s university GPA if we knew his or her high school GPA.c. The r2 for this regression line is 0.61. Interpret this value given the context of the data.arrow_forward
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