4. The following regression is fitted using variables identified that could be related to tuitioncharges ($) of a university.TUITION = a+ B ACCEPT + y MSAT + 1 VSATWhere ACCEPT = the percentage of applicants that was accepted by the university, MSAT =Median Math SAT score for the freshman class and VSAT = Median English SAT score for thefreshman class.The data was processed using MNITAB and the following is an extract of the output obtained:PredictorCoefStDevTPConstant-267806115-4.380.000ACCEPT116.0037.17*0.003MSAT-4.2114.12VSAT70.8515.77-0.304.490.767**S = 2685R-Sq = 69.6%Analysis of VarianceR-Sq (adj) = 67.7%SourceDFSSMSFPRegression3808139371Residual Error49353193051269379790720802137.370.000Total521161332421a) Write out the regression equation.b) State the dependent and independent variable(s)c) Fill in the blanks identified by ** and ****.d) Is B significant, at the 10% level of significance?e) State one limitation of using the regression equation for predicting the value of the GPAScore.TQ Search45&4+*78hp1446no-114440OP

The given regression equation is: TUITION=α+βACCEPT+γMSAT+λVSAT.The incomplete summary output of the…

Answered: 4. The following regression is fitted…

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 number of pounds of steam used per month by a chemical plant is thought to be related to the average ambient temperature (in °F) for that month. The past year's usage and temperatures are in the following table: (a) Find a regression relating steam usage to temperature. (b) Test for significance of regression using a=0.05. (c) Find a 99% confidence interval for B1 Usage/ 1000 Usage/ 1000 Month Temp. Month Temp. Jan. 21 185.79 July. 68 621.55 Feb. 24 214.47 Aug. 74 675.06 Mar. 32 288.03 Sept. 62 562.03 Apr.o 50 452.93 47 424.84 Oct. May 50 454.58 Nov. 41 369.95 June 59 539.03 Dec. 30 a 273.98
Suppose researchers are interested in exploring the factors which affect depression for Australian adults. The researchers recruited a sample of 99 Australian adults and collected data on several variables which may influence depression. Note that here depression is represented by a score, with higher values representing higher levels of depression. The variables for this study are listed below: Age Gender (0 = female, 1 = male) Stress level Anxiety level Depression Prior to running their analysis (multiple regression), the researchers would like to check the regression assumptions for the data. Which of the assumptions below is not an assumption for multiple regression? A) Sphericity B) Linearity and Additivity C) Equal variances D) Normality E) Metric scales
The local utility company surveys 12 randomly selected customers. For each survey participant, the company collects the following: annual electric bill (in dollars) and home size (in square feet). Output from a regression analysis appears below: Bill 13.45 +4.39*Size Coefficients Estimate Std. Error (Intercept) 13.45 Size 4.39 0.54 0.2 We are 90% confident that the mean annual electric bill increases by between 4.028✔ dollars and 4.753 x dollars for every additional square foot in home size. Round your answers to three decimal places and enter in increasing order.
The fish in my pond have mean length 10 inches with a standard deviation of 2 inches and mean weight 4 pounds with a standard deviation of .6 pounds. The correlation coefficient of length and weight is .9. If the length of a particular randomly selected fish is reported to be 15 inches, then what should we predict for the weight of that fish using simple linear regression?
What kind of plot is useful for deciding whether it is reasonable to find a regression plane for a set of data points involving several predictor variables?
The fish in my pond have mean lenth 14 inches with a standard deviation of 2 inches and mean weight 4 pounds with a standard deviation of .8 pounds. The correlation coefficient of length and weight is .4. If the length of a particular randomly selected fish is reported to be 15 inches, then what should we predict for the weight of that fish using simple linear regression?
The Cadet is a popular model of sport utility vehicle, known for its relatively high resale value. The bivariate data given below were taken from a sample of fifteen Cadets, each bought "new" two years ago and each sold "used" within the past month. For each Cadet in the sample, we have listed both the mileage, x (in thousands), that the Cadet had on its odometer at the time it was sold used, and the price, y (in thousands of dollars), at which the Cadet was sold used. These data are shown graphically in the scatter plot in Figure 1. Mileage, x(in thousands) Used selling price, y(in thousands of dollars) 21.1 31.2 34.2 25.8 23.3 34.1 37.5 22.8 29.1 27.1 24.2 29.7 23.7 28.0 27.7 25.1 24.1 27.6 23.2 30.9 26.7 31.7 28.1 30.0 20.9 31.6 15.2 34.6 25.7 26.6 The least-squares regression line for these data has a slope of approximately −0.5. Answer the following. Carry your intermediate computations to at least four decimal places, and round your answers as…
The following regression is fitted using variables identified that could be related to student requested loan amount, LOAN-AMT(S) for returning students of a certain University. LOAN - AMT = a + B ACCEPT + y PREV + OUTS Where ACCEPT = the percentage of applicants that was accepted by the university, PREV= previous loan amount and OUTS-outstanding loan amount The data was processed using MNITAB and the following is an extract of the output obtained: Predictor Constant ACCEPT PREV OUTS S = 2685 Coef -26780 116.00 -4.21 70.85 Analysis of Variance R-Sq 69.6% Source DE SS Regression 3 Residual Error 49 Total 52 EXHIBIT 2 StDev 6115 37.17 14.12 15.77 808139371 353193051 1161332421 T * 3.14 -0.30 4.49 MS 269379790 7208021 a) What is dependent and independent variables? b) Fully write out the regression equation c) What is the sample size used in this investigation? d) Fill in the blanks identified by *' and ****. e) Is B significant, at the 5% level of significance? P 0.000 0.003 ** 0.000…
If the regression line is not a "good fit" what would be better to use to approximate a response variable?
In the above regression of air quality on other variables in question 2, R-squared is 0.3829. How do you interpret it?

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