Chapter 14, Problem 14.28CRP

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…

3) 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?

Please write the solution and answer. Thank you!

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 A

Use 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 Y

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 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.

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 804 247 696

The 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.

1. 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.