opyClear"DeleteClipboard/Data|N=54Interceptpindexenzymebloodclot#Copy DownMove ScreenVariables CasesCatcher➜Go ToCopy Right▾Find/ReplaceFillStandardizeRegression Summary for Dependent Variable: logsurvival (Sheet1 in data_survivR=.90619016 R²= .82118061 Adjusted R²= .80658311F(4.49)-56.255 p<.00000 Sid.Error of estimate: .11670bºb1(49)p-valueStd.Err.of bºSid. Err.of b0.1302260.6631955.0926540.0000060.1396840.0918620.0346280.0227731.5205900.1347900.560623 0.068441 0.0088010.001074 8.191352 0.0000000.585219 0.077613 0.006989 0.0009277.5401690.0000000.364904 0.085766 0.060401 0.014197 4.254645 0.000094Summary of Stepwise Regression; DV: logsurvival (Sheet1 in data_survival)Multiple R. VariableRegressiverSumn pindexStep MultipleMultiple R-squareF-10p-value Variables+in/-out R R-square change entr/remincluded11 0.6841910.4681180.46811845.76597 0.0000002 0.783378 0.613681 0.145564 19.21663 0.0000583 0.868976 0.755120 0.141438 28.87909 0.00000240.906190 0.821181 0.066061 18.10200 0.000094enzymeRegre bloodclotSumnSummary Statistics, DV logsunval (Sheet1 in data sunwal) Regression Summary for Dependent Variable logsunival (Sheet.Multiple Regression ReReadySheet1 in data sunwal28°CPartly sunnyOL CACCUK-Statistica-(data survival-Analysis of Variance; DV: logsurvival (Sheet1 in data survivalWorkbookHome EditView Format Statistics Data Mining Graphs Tools DataRandom ValuesDDE+++aCopy DownOLEX Cut Select AllCopy ClearFormat DeleteClipboard/DataMove ScreenFindReplaceRepeat➜Go ToFind/ReplaceVariables CasesCatcherCopy RightVariables Cases OLEObject ObjectInsertFillStandardizeAnalysis of Variance; DV: logsurvival (Sheet1 in data_survival)Sums of df MeanSquaresP-valueSquares56 25488 0.0000003 064336 40.7660840.667286 49 0.0136183.731622rmat".Ressimngre liverata survivalSheet!ata survivalaFindMultiple R. EffectRegress Regress.Sumn ResidualTotalRegreReplace FR RandoRepeatSummary of Stepwise Regression, DV logsunwalCI,VIeFeatureEditLinks Dictionary What is the first variable to enter in the stepwise regression?[Select]How many variables entered in the entire process?[Select]What do you notice about R2 when you increase the number of variables?[Select]

The first variable in the stepwise regression is liver. There are total 5 variables entered in the…

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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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Interpret the regression results.
Real estate again The regression of Price on Size ofhomes in Albuquerque had R2 = 71.4,, as describedin Exercise 25. Write a sentence (in context, of course)summarizing what the R2 says about this regression.
The arm span and foot length were both measured (in centimeters) for each of 20 students in a biology class. The computer output displays the regression analysis. Which of the following is the best interpretation of the coefficient of determination r2? About 37% of the variation in arm span is accounted for by the linear relationship formed with the foot length. About 65% of the variation in foot length is accounted for by the linear relationship formed with the arm span. About 63% of the variation in arm span is accounted for by the linear relationship formed with the foot length. About 63% of the variation in foot length is accounted for by the linear relationship formed with the arm span.
We run the following regression using a sample of 148 women living in the western states. The variable wage is the hourly wage ( in USD) and educ is the number of years of education completed. wage = -3.07 + 1.52 educ Which statement best described the estimated slope coefficient? Group of answer choices A 1.52 extra years of education is predicted to increase wage by 1 USD An extra year of education is predicted to increase hourly wage by 1.52 USD A 1 percent increase in education is predicted to increase wage by 1.52 percent A 1.52 percent increase in education is predicted to increase wage by 1
Fill in the blanks. a. Multicollinearity is considered to be severe if the VIF for one or more predictor variables is ______. or greater. b. If the coefficient of multiple determination for the regression of the predictor variable x11 on all the other predictor variables in a regression equation is 0.6, then the VIF for x1 is ______. c. The effect of multicollinearity in a polynomial regression analysis can be reduced by ______. the predictor variable.
2. Participants were kept awake for a certain number of hours before given a visuospatial task. Researchers measured how many correct responses each participant had. Results are shown below. Use alpha = .01. Number of Correct Hours Kept Awake (X) Responses (Y) 21 X = 10 SS, = 422 Y = 10 SS, = 690 2 4 19 %3D 6. 13 SPxy =-525 ху 5 20 S, =5.70 S, = 7.29 9. 11 10 9. 14 5 15 5 17 2 18 1 17 1 13 4 18 8 11 A. Graph the data. B. State the hypotheses. C. Make a decision about the null. a. Calculate Pearson's r i. Decision about null hypothesis? b. Calculate effect size i. Interpret effect size. D. State your conclusion. E. Relate your conclusion to the research. F. Calculate the regression formula. G. If someone was kept awake for 9 hours, what is the predicted number of correct responses?
2. How do you identify which variable in a study is the factor? How do you identify the levels of a factor? How do you identify the dependent variable?
Write down the properties of R2 . If R2=35.7%, interpret your data. Distinguish between the correlation and regression with example.
What important guideline should you keep in mind when choosing independent variables for multiple regression?

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What is the first variable to enter in the stepwise regression? [Select] How many variables entered in the entire process? [Select] What do you notice about R2 when you increase the number of variables? [Select]