8. For the following data:a. Find the regression equation for predicting Y from X.b. Calculate the Pearson correlation for these data.Use r and SS, to compute SSstandard error of estimate for the equation.and theresidualY336.9.8437109.

Answered: Image /qna-images/answer/df43b489-b7b3-46c8-9ecc-eb10a97d4faa.jpg

Answered: 8. For the following data: a. Find the…

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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The following data are the monthly salaries y and the grade point averages x for students who obtained a bachelor's degree in business administration. GPA 2.6 3.4 3.6 3.2 3.5 2.9 Monthly Salary ($) 3,500 3,900 4,200 3,800 4,200 3,900 The estimated regression equation for these data is ý = 1,970.72 + 608.11x and MSE = 18,671.17. (a) Develop a point estimate of the starting salary (in dollars) for a student with a GPA of 3.1. (Round your answer to the nearest cent). (b) Develop a 95% confidence interval for the mean starting salary (in dollars) for all students with a 3.1 GPA. (Round your answers to the nearest cent). to $ (c) Develop a 95% prediction interval (in dollars) for Ryan Dailey, a student with a GPA of 3.1. (Round your answers to the nearest cent). to $
Consider the following data set, where y is the final grade in a math class and is the average number of hours the student spent working on math each week. hours/week X 6.5 10 11 12.5 13 14.5 15 17.5 19.5 20 Grade y 66.1 63 71.4 72.5 79.2 94.3 95 91.5 100 98 The regression equation is y = 3x + 41.3. a) Explain what the value of the slope means in this situation, where y is the final grade in a math class and is the average number of hours the student spent working on math each week. O For each additional hour per week that a student studies on average, their final grade will be about 3 points. O For each additional hour per week that a student studies on average, their final grade will be about 41.3 points higher. For each additional hour per week that a student studies on average, their final grade will be about 3 points higher. O For each additional hour per week that a student studies on average, their final grade will be about 41.3 points. b) Explain what the value of the…
Calculate the regression coefficient and obtain the lines of regression for the following data: USE MICROSOFT EXCEL
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The following table contains ACT scores and the GPA for eight college students. Estimate the relationship between GPA (yi) and ACT (x;) using OLS regression by hand. Report the intercept and slope estimates: Student 1 2 3 4 5 6 7 8 GPA 2.8 3.4 3.0 3.5 3.6 3.0 2.7 3.7 ŷ₁ =B₁ + B₁x₁ ACT 21 24 26 27 29 25 25 30 y X ŷ₁
The following table gives the data for the average temperature and the snow accumulation in several small towns for a single month. Determine the equation of the regression line, ŷ = bo + b₁x. Round the slope and y-intercept to the nearest thousandth. Then determine if the regression equation is appropriate for making predictions at the 0.05 level of significance. Critical Values of the Pearson Correlation Coefficient Average Temperature (°F) Average Temperatures and Snow Accumulations 42 31 24 45 38 18 33 21 25 9 12 27 7 15 22 30 13 20 37 Snow Accumulation (in.) 8
The following table shows students’ number of absences, x, and the student’s final grade,, y.# of absences x 6 2 15 9 12 5 8 Final grade y 82 86 43 74 58 90 78a) Calculate r, the correlation coefficient ________________b) Find the equation of the regression line __________________________________c) If a student is absent 4 times,, what grade does your regression line predict?__________________
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A sales manager collected the following data on x = years of experience and y = annual sales ($1,000s). The estimated regression equation for these data is ý = 81 + 4x. Years of Annual Sales Salesperson Experience ($1,000s) 80 3 97 3 102 4 4 102 103 6. 8 101 10 119 8 10 123 9. 11 127 10 13 136 (a) Compute SST, SSR, and SSE. SST = SSR = SSE = (b) Compute the coefficient of determination r2. (Round your answer to three decimal places.) r2 = Comment on the goodness of fit. (For purposes of this exercise, consider a proportion large if it is at least 0.55.) O The least squares line did not provide a good fit as a small proportion of the variability in y has been explained by the least squares line. The least squares line provided a good fit as a large proportion of the variability in y has been explained by the least squares line. The least squares line provided a good fit as a small proportion of the variability in y has been explained by the least squares line. The least squares line did…

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8. For the following data: a. Find the regression equation for predicting Y from X. b. Calculate the Pearson correlation for these data. Use r and SS, to compute SS standard error of estimate for the equation. and the residual Y 3 3 6. 9. 5 8 4 3 7 10 9.