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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Would the regression in Equation be useful for predicting test scoresin a school district in Massachusetts? Why or why not?
An admissions counselor is looking to determine an equation that relates the graduate grade point averages of students who are newly admitted to an academic program with their undergraduate GPA, their GRE scores, and the number of years they have been out of college. What is the correct format for a multiple regression equation? Select the correct answer below: yˆ=b0+b1x1+b2x2+b3x3 yˆ=b1x1+b2x2+b3x3 yˆ=b0+b1x1+b2x2+b3x3+b4x4 yˆ=b0+b1x1+b2x2
There is a linear relationship between the number of chirps made by the stiped ground cricket and the air temperature. It was determined that the linear regression model is: y = 25.2 + 3.3x where x is the number of chirps per minute and y is the estimated temperature in degrees Fahrenheit. What is the predicted number of chirps made when the temperature is 60 degrees Fahrenheit? Round to the nearest integer. Do not include units.
Give the equation of the regression line: -913.2969+0.4808years(y) a) Plug an actual x value into the equation and discuss the difference between the real y and the predicted yb) Discuss the domain of your datac) Include the scatterplot with line of best fit
Mary used a sample of 68 large U.S. cities to estimate the relationship between Crime (annual property crimes per 100,000 persons) and Income (median annual income per capita, in dollars). Her estimated regression equation was Crime = 428 + 0.050 Income. If Income decreases by 500, we would expect that Crime will Group of answer choices remain unchanged. decrease by 25. increase by 500. decrease by 50.
A business analyst found a linear relationship between the predicted profit (in thousands of dollars) with the amount of employees hired (think of the units as per person). The resulting regression equation and coefficient of determination were found to be: ŷ = 10 + 4.5x R² = 72% a) Interpret the slope and y-intercept using the guidelines learned in class. b) Find the predicted profit when the you hire 5 employees. c) Suppose that the true profit when 5 employees are hired is $42 thousand dollars. Find the residual. d) What is the correlation coefficient? Round to three decimal places. e) What is the strength of the correlation coefficient?
A marketing consultant created a linear regression model to predict the number of units sold by a client based on the amount of money spent on marketing by the client. Which of the following is the best graphic to use to evaluate the appropriateness of the model?
The arm span and foot length were both measured (in centimeters) for each of 20 students in a biology class. The computer output displays the regression analysis. Which of the following is the best interpretation of the coefficient of determination r2? About 37% of the variation in arm span is accounted for by the linear relationship formed with the foot length. About 65% of the variation in foot length is accounted for by the linear relationship formed with the arm span. About 63% of the variation in arm span is accounted for by the linear relationship formed with the foot length. About 63% of the variation in foot length is accounted for by the linear relationship formed with the arm span.
Ariel was running analyses over and over in census data and came across a correlation between weight and debt (r=.78). Independent variable is weight and dependent variable is debt. b) Curious Ariel noted some statistics on these weight (M=160lb, SD=15lb) and debt (M=196k, SD=20k). If Ariel wanted to calculate a regression equation, what would her slope and intercept be?
Using the weights (lb) and highway fuel consumption amounts (mi/gal) of the 48 cars listed in the accompanying data set, one gets this regression equation: y = 58.9-0.00749x, where x represents weight. Complete parts (a) through (d). Click the icon to view the car data. The site to your captio V.UVITU. D. The slope is -0.00749 and the y-intercept is 58.9. c. What is the predictor variable? ... OA. The predictor variable is highway fuel consumption, which is represented by x. OB. The predictor variable is highway fuel consumption, which is represented by y. C. The predictor variable is weight, which is represented by x. OD. The predictor variable is weight, which is represented by y. d. Assuming that there is a significant linear correlation between weight and highway fuel consumption, what is the best predicted value for a car that weighs 2994 lb? The best predicted value of highway fuel consumption of a car that weighs 2994 lb is (Round to one decimal place as needed.) mi/gal.
please use this situation: A small theater company has a linear regression model to estimate y = the concession stand sales in dollars, based on knowing x = the number of people in attendance. The regression equation is: = 6.72x + 11.50 and the correlation coefficient was r = 0.781. The data set saw the number of people in attendance ranging from a minimum of 18 people to a maximum of 170 people. 1) How reliable would it be to make a prediction for the concession sales amount if there were 500 people in attendance? Explain.
A pediatrician wants to determine the relation that exists between a child's height (x) and head circumference (y). She randomly selects 11 children from her practice and measures their height and head circumference in inches. She finds that the correlation is 0.694, and the regression equation is y = 0.294x + 2.02. What proportion of the variation in head circumference can be explained by the variation in the values of height? Round your answer to three decimal places. %

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