CanvasA study of the top 75 MBA programs attempted to predict the average starting salary (in $1000's) of graduates of the program based on1 Hour, 47 Minutes, 54the 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 SalaryPredictorVariables Coefficient Std Error T PConstant 18.1849 10.3336 1.76 0.0826Size 1.47494 0.14017 10.52 0.0000R-Squared 0.6027 Resid. Mean Square (MSE) 532.986Adjusted R-Squared 0.5972 Standard Deviation 23.0865The model was then used to create 95% confidence and prediction intervals for y and for E(Y) when the tuition charged by the MBAprogram was $75,000. The results are shown here:95% confidence interval for E(Y): ($123,390, $134,220)95% prediction interval for Y: ($82,476, $175,130)Which of the following interpretations is correct if you want to use the model to estimate E(Y) for all MBA programs?O We are 95% confident that the average starting salary for graduates of a single MBA program that charges $75,000 in tuition will fall between $123,390and $134,220.O We are 95% confident that the average of all starting salaries for graduates of all MBA programs that charge $75,000 in tuition will fall between $82,476and $175,130.O We are 95% confident that the average of all starting salaries for graduates of all MBA programs that charge $75,000 in tuition will fall between$123,390 and $134,220.O We are 95% confident that the average starting salary for graduates of a single MBA program that charges $75,000 in tuition will fall between $82,476and $175,130.

Given: Dependent variable : starting salary Independent variable : tuition fee charged The 95%…

Answered: A study of the top 75 MBA programs…

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 table shows a part of an output of a linear regression model predicting the average fare on different flight routes. Data Table Regression Table Coefficient Constant 95.80976147 COUPON −9.61654124 DISTANCE 0.080733811 PAX −0.000167343 What is the difference in prediction of the following two routes? Route A that is 3,000 miles, with COUPON=1.5 and PAX=6,000 Route B that is 3,000 miles, with COUPON=1.2 and PAX=6,000.
dead popgtden beechfac 0.1425 0.499 0.153846 0.37829 1 0.2457 0.28364 1 0.08462 0.38465 0.1927 0 0.28479 1 0.03628 0.38918 1 0.20943 Ol 0.43262 Can anyone give me some suggestions for this data? Which method is best to use for this data? (Regression, Multiple Regression, Logistic Regression, Clustering, ANN, or Classification Trees) dead (fraction of dead flycatchers) popgtden (mean great tit density) beechfac (beech crop factor (See supplement, low or high))
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) Using a 0.05 level of significance, does this regression equation appear to have any value for predicting revenue based on Ads? a. Yes because there is a significant linear relationship between the two quantities. b. No because there is not a significant linear relationship between the two quantities. c. Yes because there is not a significant linear relationship between the two quantities. d. No because there is a significant linear relationship between the two quantities.
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Use the pizza cost and the subway fare in the table below to find the regression equation, letting pizza cost be the predictor (x) variable. (Pizza cost is in dollars per slice, subway fare and CPI are in dollars.) What is the best predicted subway fare when pizza costs $3.98 per slice? Year Pizza Cost Subway Fare CPI 1960 1973 1986 1995 2002 2003 2009 2013 2015 2019 0.152 0.353 1.000 1.251 1.753 2.003 2.254 2.300 2.747 2.997 0.151 0.346 1.000 1.347 1.497 2.004 2.253 2.551 2.747 2.750 29.6 44.4 109.6 152.4 180.0 184.0 214.5 233.0 237.0 252.2 The regression equation is y=+x. (Round the y-intercept to four decimal places as needed. Round the slope to three decimal places as needed.) The best predicted subway fare when pizza costs $3.98 per slice is $ (Round to the nearest cent as needed.)
Twenty members of an athletic club are studying the relationship between the time it takes an individual athlete to reach a given level of fatigue during exercise (time to fatigue, measured in minutes) and athletic performance. For each member, time to fatigue and a performance score were recorded. The computer output of the regression analysis is shown in the table. Term CoefCoef SE CoefSE Coef TT Constant 39.88 4.24 9.41 Time to fatigue 3.92 0.71 5.52 Which of the following is a 90 percent confidence interval for the slope of the regression line relating performance score and time to fatigue? Assume that the conditions for inference are met.
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