A professor wants to predict students' final examination scores on the basis of theirmidterm test scores. An equation was determined on the basis of data on the scores ofthree students who took the same course with the same instructor the previoussemester (see the table on the right).a) Find the regression line, y=mx+b.Midterm Score,x826569FinalExam Score, yb)The midterm score of a student was 88%. Use the regression line to predict the student's final exam score.876778a) y = ☐(Simplify your answer. Round to five decimal places as needed.)b) The student's final exam score is(Simplify your answer. Round to two decimal places as needed.).

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Answered: A professor wants to predict students'…

Calculus: Early Transcendentals

8th Edition

ISBN:9781285741550

Author:James Stewart

Publisher:James Stewart

Chapter1: Functions And Models

Section: Chapter Questions

Problem 1RCC: (a) What is a function? What are its domain and range? (b) What is the graph of a function? (c) How...

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The data show the number of felony convictions, in hundreds, and the crime rate, in crimes per 100,000, for seven randomly selected states. For the given data, a. determine the correlation coefficient between the number of felony convictions and the crime rate, (b) find the equation of the regression line, (c) approximate what crime rate can we anticipate in a state that has 12 hundred felony convictions.Felony convictions: 11.4, 8.1, 6.7, 3.4, 2.4, 2.3, 0.4Crime rate/ 100,000: 12, 9.5, 10.4, 9, 4.2, 5.5, 3.3 A. a. r = 0.906 b. y = 3.85x + 0.777 c. 47 B. a. r = 0.906 b. y = 0.777x + 3.85 c. 13.2 C. a. r = -0.906 b. y = 0.777x - 3.85 c. 5.5 D. a. r = -0.906 b. y = -0.777x + 3.85 c. 5.5
a) Determine a linear regression equation for the data. Round the slope and y-intercept to the nearest whole number. b) Find the correlation coefficient, r-value, of the line. Round your answer to the nearest thousandth. c) Predict the distance the driver would have traveled after 11 hours. Round your answer to the nearest whole number.
The following data show the brand, price($), and the overall score for 6 stereo headphones that were tested by Consumer Reports. The overall score is based on sound quality and effectiveness of ambient noise reduction. Scores range from 0 (lowest) to 100 (highest). The estimated regression equation for these data is y=23.043+2.7x , where x=price and y=overall score . Brand Price Score Bose 170 75 Scullcandy 160 71 Koss 95 62 Phillips/O'Neill 70 56 Denon 60 40 JVC 45 26 Round your answers to three decimal places. a. Compute SST,SSR , and SSE. SST (to 3 decimals) SSR (to 3 decimals) SSE (to 3 decimals) c. What is the value of the sample correlation coefficient? rxy=
A real estate company wants to study the relationship between house sales prices and some important predictors of sales prices. Based on data from recently sold homes in the space, the following variables are used in a multiple regression model. y = sales price (in thousands of dollars) x₁ = total floor area (in square feet) x₂ = number of bedrooms x3 distance to nearest high school (in miles) = The estimated model is as follows. =76+0.098x₁ +16x₂ - 8x3 Answer the questions below for the interpretation of the coefficient of X₂ in this model. (a) Holding the other variables fixed, what is the average change in sales price for each additional bedroom in a house? dollars (b) Is this change an increase or a decrease? O increase O decrease X
Please help, this is for financial mathematics, thank you so much!!!!!
A regression analysis was performed to determine if there is a relationship between hours of TV watched per day (2) and number of sit ups a person can do (y). The results of the regression were: y=ax+b a=-0.666 b=21.511 72=0.543169 r=-0.737 Use this to predict the number of sit ups a person who watches 7 hours of TV can do, and please round your answer to a whole number. Hìnt: Help Question Help: Message instructor Submit Question 围 hp Ce %23 & 3. 4. 6. 8. W e y 61
The table shows the average weekly wages (in dollars) for state government employees and federal government employees for 8 years. The equation of the regression line is y=1.410x−17.174. Complete parts (a) and(b) below.
The linear regression equation for the data from the number 2 is y=.27x+5.82. Describe the slope of the linear best-fit equations.
biologist was recording various temperatures (in degrees Celsius) and predicting the amount of glucose produced by a plant (in milligrams). Suppose the linear equation was the following: ?̂ = 12.7 − 0.223? a) Interpret the slope of the regression line.
A real estate company wanted to see the relationship between home prices and square footage of homes for sale. The regression line is ŷ = –802,456 + 144x, where x is the square footage of a home. Complete the statement based on the information. For every -square-foot increase, the predicted home price is predicted to increase by $
The table shows the number of goals allowed and the total polints earned (2 points for a win, and 1 point for an overtime or shootout loss) by 14 ice hockey teams over the course of a season. The equation of the regression line is y= -0.573x+220.523. Use the data to answer the following questions. (a) Find the coefficient of determination, , and interpret the result. (b) Find the standard error of the estimate, s and interpret the result. Goals Allowed, x Polnts, y 209 205 220 226 261 266 275 201 214 204 224 201 263 244 D 111 108 100 97 84 82 44 107 105 95 92 86 67 69 (0) ? =D (Round to three decimal places as needed.) Interpret the coefficient of determination. Select the correct choice below and fill in the answer box to complete your choice. (Round to one decimal place as needed.) O A. About % of the variation in number of goals allowed can be explained by the relationship between number of goals allowed and points eamed. The remaining variation is unexplained. O B. About % of the…
After interviewing salespersons at Harley Davidson dealerships, a researcher has created a linear regression line to explain the relationship between a Harley Davidson motorcycle's age (x) and price (y). The regression has an R² = 87.7%. Write a sentence summarizing what R² says about this regression. The age of the motorcycle explains 12.3% of the variation in price. The age of the motorcycle explains 9.36% of the variation in price. The age of the motorcycle explains 87.7% of the variation in price. The price of the motorcycle explains 12.3% of the variation in age. The price of the motorcycle explains 87.7% of the variation in age.

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