The following data is on number of hours studied (x) (per week) and the final grade (y) (out of100) for 10 students in the course Stat-240X111216691424320y31586573374460912184givingEx = 121, Ex = 1855, Ey = 564, Ey =36562, Exy = 8157.(A) Find the equation of the least squares line that approximates the regression of the test scoreson the number of hours studied.(B) Predict the average test score of a person who studied 16 hours for the test. Compute theestimated variance of your prediction.(C) Compute the correlation coefficient and then comment.

Given data as ( x ) number of hours studied per week and the final grade ( y ). and also given that…

Answered: The following data is on number of…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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Data is collected on the distance of several hikes (in miles), along with the amount of time (in minutes) the hike is expected to take. All hikes in the data set are between 0.5 miles and 10 miles long, and the relationship between distance and time is linear and strong. The regression equation to predict time based on distance is as follows: Predicted time = –266 + 31.48 (distance). Suppose we want to use the regression equation to predict the time it takes to complete a particular hike. For which one of the following distances would using the regression equation result in extrapolation? 1 mile 4.5 miles 6.8 miles 9 miles None of the above distances would result in extrapolation.
A recent study showed that the hours a person exercised in a week affected the individual'sresting heart rate. It was computed that r = -.68 and the least squares regression line was?̂ = 83-1.4x, where x is the hours exercised and y is the resting heart rate. d. What percentage of variability in resting heart rate can be explained by variability inhours exercised?
Q5/ Use Linear Regression to fit the following data: 1 2 4 5 6. Y 4 9 10 10 8
A trucking company considered a multiple regression model for relating the dependent variable y = total daily travel time for one of its drivers (hours) to the predictors x₁ = distance traveled (miles) and x₂ = the number of deliveries made. Suppose that the model equation is Y = -0.800+ 0.060x₁ +0.900x₂ + e (a) What is the mean value of travel time when distance traveled is 50 miles and four deliveries are made? hr (b) How would you interpret ₁ = 0.060, the coefficient of the predictor x₁? O When the number of deliveries is constant, the average change in travel time associated with a ten-mile (i.e. one unit) increase in distance traveled is 0.060 hours. O The total daily travel time increases by 0.060 hours when the distance traveled increases by 1. O When the number of deliveries is held fixed, the average change in travel time associated with a one-mile (i.e. one unit) increase in distance traveled is 0.060 hours. O The average change in travel time associated with a one-mile (i.e.…
The owner of a movie theater company used multiple regression analysis to predict gross revenue (y) as a function of television advertising (x,) and newspaper advertising (x,). The estimated regression equation was ý = 82.3 + 2.29x, + 1.90x2. The computer solution, based on a sample of eight weeks, provided SST = 25.1 and SSR = 23.415. (a) Compute and interpret R? and R 2. (Round your answers to three decimal places.) The proportion of the variability in the dependent variable that can be explained by the estimated multiple regression equation is 653 x . Adjusting for the number of independent variables in the model, the proportion of the variability in the dependent variable that can be explained by the estimated multiple regression equation is (b) When television advertising was the only independent variable, R2 = 0.653 and R,2 = 0.595. Do you prefer the multiple regression results? Explain. Multiple regression analysis (is preferred since both R2 and R.2 show an increased v v…
A least squares regression line was calculated to relate the length (cm) of newborn boys to their weight in kg. The line is weight = -5.25 +0.1696 length. A newborn was 48 cm long and weighed 3 kg. According to the regression model, what was his residual? What does that say about him?
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The distance between the Y value in the data and the predicted Y value from the regression equation is known as the residual. What is the value for the sum of the squared residuals? a. SSresidual = r2(SSX) b. SSresidual = r2(SSY) c. SSresidual = (1 – r2)(SSX) d. SSresidual = (1 – r2)(SSY)
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