A study of the top 75 MBA programs attempted to predict the average starting salary (in $1000's) of 6 graduates of the program based on the amount of tuition (in $1000's) charged by the program. The results of a simple linear regression analysis are shown below: Least Squares Linear Regression of Salary Predictor Variables Coefficient Std Error T P Constant 18.1849 10.3336 1.76 0.0826 Tuition 1.47494 0.14017 10.52 0.0000 R-Squared Adj R-Squared 0.5972 0.6027 Resid. Mean Square (MSE) 532.986 Standard Deviation 23.0865 In addition, we are told that the coefficient of correlation was calculated to be r = 0.7763. Interpret this result. A) There is a fairly strong positive linear relationship between the amount of tuition charged and the average starting salary variables. B) There is almost no linear relationship between the amount of tuition charged and the average starting salary variables. C) There is a very weak positive linear relationship between the amount of tuition charged and the average starting salary variables. D) There is a fairly strong negative linear relationship between the amount of tuition charged and the average starting salary variables.

A study of the top 75 MBA programs attempted to predict the average starting salary (in $1000's) of 6 graduates of the program based on the amount of tuition (in $1000's) charged by the program. The results of a simple linear regression analysis are shown below: Least Squares Linear Regression of Salary Predictor Variables Coefficient Std Error T P Constant 18.1849 10.3336 1.76 0.0826 Tuition 1.47494 0.14017 10.52 0.0000 R-Squared Adj R-Squared 0.5972 0.6027 Resid. Mean Square (MSE) 532.986 Standard Deviation 23.0865 In addition, we are told that the coefficient of correlation was calculated to be r = 0.7763. Interpret this result. A) There is a fairly strong positive linear relationship between the amount of tuition charged and the average starting salary variables. B) There is almost no linear relationship between the amount of tuition charged and the average starting salary variables. C) There is a very weak positive linear relationship between the amount of tuition charged and the average starting salary variables. D) There is a fairly strong negative linear relationship between the amount of tuition charged and the average starting salary variables.

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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Consider the following estimated regression model relating annual salary to years of education and work experience. Estimated Salary = 11,433.70 + 3312.68(Education) + 819.54 (Experience ) Suppose an employee with 7 years of education has been with the company for 5 years (note that education years are the number of years after 8th grade). According to this model, what is his estimated annual salary? 囲 Tables E Keypad Answer Keyboard Shortcuts
A vocational counselor uses the number of days without employment to predict her clients' feelings of self efficacy, measured on a scale of 1 to 5, with higher numbers meaning that clients feel more secure in their job related abilities. The slope of the regression line is –1.02. Which statement is the best interpretation for this finding? a. For every 1-point increase in self-efficacy, there is an associated decrease in the number of days of unemployment. b. For every additional day of unemployment, there is an associated decrease in self-efficacy of 1.02 points. c. The least number of days a person can be unemployed and still feel self-efficacious is 3.98 points d. The decrease in self-efficacy of 1.02 points is caused by each additional day of unemployment
A cafe company wants to determine how the money they spend on Google ads impacts their monthly revenue. Over 6 consecutive months, they vary the amount they spend on their Ads (in $) and record the associated revenue (in $) for each month. The data is shown below: l Revenue 50 427 75 472 100 467 125 529 150 518 175 543 A) Develop a regression equation for predicting monthly revenue based on the amount spent with Ads. What is the y-intercept? B) What is the sample correlation between these two variables? C) What is the slope of your regression equation? Give your answer to two decimal places. D) Using a 0.05 level of significance, does this regression equation appear to have any value for predicting revenue based on Ads?
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Stoaches are fictional creatures that nest in truffula forests. A researcher wants to know whether there is a relationship between a stoach’s wingspan (?W, the predictor) and its nest height (?H, the response). A sample of 85 stoaches is observed, and for each, the wing-span (in cm) and the nest height (in m) are recorded. The observed data meet the assumptions for a linear regression, so the researcher fits the regression model and obtains a regression equation ℎ̂=−4.273+0.834?,h^=−4.273+0.834w, with standard error for the coefficient of ?w equal to 0.242. Determine the ?p-value from a test for a statistically significant linear dependence of nest height on wing-span. (Give your answer to 4 decimal places.
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