A linear regression analysis is used to analyze the relationship of the sleeptime the night before the exam (minutes) and the midterm score (points).The results are shown below.SUMMARY OUTPUTMultiple RR SquareAdjusted R SquareStandard ErrorObservationsANOVARegression StatisticsRegressionResidualTotal0.9817120.9637580.9592282.204662df10SSMSF ignificance F1 1034.016 1034.016 212.7371 4.79E-078 38.88427 4.86053491072.9Coefficientsandard Err t Stat P-value Lower 95%Upper 95%ower 95.0%pper 95.0%12.10246 4.971651 2.434294 0.040927 0.637811 23.56711 0.637811 23.567110.204902 0.014048 14.58551 4.79E-07 0.172506 0.237297 0.172506 0.237297InterceptSleep TimeWhich statement aligns with the result?A. Linear regression is not suitable for this analysis because R Square is0.963758B. The relationship is not linear since Multiple R is too high at 0.981412C. The student shall sleep at least 204.902 minutes before the examD. The student shall sleep at least 12.10246 minutes before the examE. If the student sleeps 1 more hour, the score shall increase 21 points.F. If the student sleeps 1 more hour, the score shall increase 12.2941points.G. If the student sleeps 1 more hour, the score shall increase 12.6824points.(Enter either A, B, C, D, E, F, or G.)

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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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Researchers are interested in predicting the height of a child based on the heights of their mother and father. Data were collected, which included height of the child (height ), height of the mother ( mothersheight), and height of the father (fathersheight ). The initial analysis used the heights of the parents to predict the height of the child (all units are inches). The results of the analysis, a multiple regression, are presented below. . regress height mothersheight fathersheight Source Model Residual Total height mothersheight fathersheight _cons SS df 208.008457 314.295372 37 2 104.004228 8.49446952 MS 522.303829 39 13.3924059 Coef. Std. Err. .6579529 .1474763 .2003584 .1382237 9.804327 12.39987 t P>|t| 4.46 0.000 C 0.156 0.79 0.434 Number of obs = F( 2, 37) = Prob > F R-squared Adj R-squared Root MSE = .3591375 -.0797093 -15.32021 = 40 12.24 0.0001 0.3983 0.3657 2.9145 [95% Conf. Interval] .9567683 .4804261 34.92886 What is the predicted height for a child born to a mother…
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Explain what a residual is and how this relates to the best-fit regression model.
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Using your dataset, run a regression of Y=GPA and X=# Friends.(do not need your actual data, just the regression results)a) State what this regression is attempting to analyze. “By running this regression, we areattempting to show.....”b) Write out the regression equation and describe what it shows (if Friends increase by 1, then. . . ).c) Find your hypothesized GPA when the # friends equals 17.d) Is the slope of # of Friends significantly different from zero?Include Ho, Ha, decision rule, t statistic from table, tc, decision, and conclusion.e) Is the r-squared of # of Friends significantly different from zero?Include Ho, Ha, decision rule, F statistic from table, Fc, decision, and conclusion.
Is it reasonable to interpret the y-intercept of the regression line? Why or why not? One home that is 1,750 square feet is listed at $590,000. Is this home's price above or below average for a home of this size? Use residuals and show your work to answer. footaget) ## (Intercept) 21.14411 136.31449 0.155 0.87954 ## footage 0.28388 0.08516 3.333 0.00667 ** ## Residual standard error: 71.64 on 11 degrees of freedom ## Multiple R-squared: 0.5025, Adjusted R-squared: 0.4573 ## F-statistic: 11.11 on 1 and 11 DF, p-value: 0.006671
Write down the properties of R2 . If R2=35.7%, interpret your data. Distinguish between the correlation and regression with example.

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A linear regression analysis is used to analyze the relationship of the sleep time the night before the exam (minutes) and the midterm score (points). The results are shown below.