Model SummaryModelAdjusted RSquareRR SquareStd. Error ofthe Estimate.890.7771.175801.793ANOVAModelSum ofdfMeanFSig.Squares206.153SquareRegression368.718 49.705 .000aResidual53.918391.383Total260.07142CoefficientsUnstandardized StandardizedCoefficientsB Std. ErrorModelSig.tCoefficientsBeta11.071 .000.750 8.671 .000.352 4.321 .000386 .701(Constant)6.725.607persons per sq mi% pop below pov line.001.000214.050minority pop (%).006015.037A multi-variable regression model is being used to investigate the relationshipbetween various factors and the infant mortality rate in the US. Data is collected forall 50 states plus the Capital District. The model currently being explored is:Infant mortality rate = f(population density, poverty, minority)ý = bo + bqX1 + bz%2 + byx3x, is the population density, in persons per square mile;x2 is the percentage of the population that is below the poverty linex3 is the percentage of the population believed to belong to a visible minority groupy-hat is the rate of deaths per 1000 infants.The outputs of this SPSS multiple regression model are given above.What is the fitted equation of this multiple regression model.

Solution: Given information: k= 3 independent variable. n= 43 observation.

Answered: Model R Square Adjusted R Square R.…

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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Model Summary Adjusted R Std. Error of the Model R R Square Square Estimate 1 .919 887 64259 ANOVA Model Sum of Squares df Mean Square F Sig 1 Regression 11.718 002 Residual Total 25.500 7 Coefficients Standardized Unstandardized Coefficients Coefficients 4 Model B Std. Error Beta t Sig. 1 (Constant) 83.230 1.574 52.882 000 TVAdv .304 1.153 7.532 001 NewsAdv .321 .621 4.057 010 What is SSE? O.605 1.065 O-2.506 O 2.605
Using General Linear Test
Interpret the slope in this model.
Explain these tables to me in detail
A regression analysis was performed and the summary output is shown below. Regression Statistics Multiple R 0.7802268560.780226856 R Square 0.6087539470.608753947 Adjusted R Square 0.5870180550.587018055 Standard Error 6.7217061336.721706133 Observations 2020 ANOVA dfdf SSSS MSMS F� Significance F� Regression 11 1265.3871265.387 1265.3871265.387 28.006928.0069 4.9549E-054.9549E-05 Residual 1818 813.264813.264 45.18145.181 Total 1919 2078.6512078.651 Step 1 of 2: How many independent variables are included in the regression model
21. Least-squares OK? Following is a residual plot produced by MINITAB. Was it appropriate to compute the least-squares regression line? Explain. Residuals Versus x -2 -3 5.0 5.5 6.0 6.5 7.0 7.5 8.0 Residual
If a regression model of the form y= B,+B,x,+... + B,x, is fit to 132 observations on each variable and yields an R´value of 0.87, fill in the blanks in the following ANOVA table. Do all calculations to at least three decimal places. Source of Degrees of freedom Sums of Mean F statistic variation squares squares Regression 69 Error Total
What is the least-squares regression line with the point (9,13) included in the data set? Data Set x y 3 6 4 5 5 7 7 6 8 9 8 8 10 8 11 9 11 7 12 10 13 12 13 10 14 11 This is a reading assessment question. ..... y hat = ______x + ______ Type integers or decimals rounded to 4 decimal places as needed
Model Summary Adjusted R Square Std. Error of Model R R Square the Estimate 1 .772 .596 .516 215.1509 a. Predictors: (Constant), Porosity ANOVA Sum of Model Squares df Mean Square F Sig. Regression 341943.282 1 341943.282 7.387 .042b Residual 231449.575 46289.915 Total 573392.857 a. Dependent Variable: Runoff b. Predictors: (Constant), Porosity Coefficients Standardized Unstandardized Coefficients Coefficients Model B Std. Error Beta t Sig. (Constant) 1863.761 237.546 7.846 .001 Porosity -80.729 29.703 -.772 -2.718 .042 a. Dependent Variable: Runoff A geomorphologist is building a model to understand variation in the amount of runoff observed in various streams within a large drainage basin. Linear regression is used to determine whether runoff is inversely related to the porosity of the soil. The variables here are thus runoff and porosity. The SPSS output from the analysis is shown above. One of the cases has a runoff value of 900 and a porosity value of 8. Calculate the value of the…
explain on the strength and the variation of the model.
2. CREATININE LEVEL PROBLEM Some statistics Analyzer A: mean = 105.75; median = 99.5 Analyzer B: mean = 97.5; median = 89.5 Let difference = Analyzer A - Analyzer B d=8.25 Md = 7 data: Analyzer A W = 0.88028 p-value = 0.1895 Wilk-Shapiro Test for Normality data: Analyzer B W = 0.91541 p-value = 0.3937 Ha: not equal to 0 t = 2.9652 p-value = 0.02095 data: difference W = 0.95788 p-value 0.7897 Student's t-test on Paired Samples: Analyzer A vs Analyzer B Ha: less than 0 t = 2.9652 p-value = 0.9895 Ha: greater than 0 t = 2.9652 p-value = 0.01048 Wilcoxon Matched-Pairs Signed Ranks Test: Analyzer A vs Analyzer B Ha: not equal to 0 Ha: less than 0 Ha: greater than 0 V = 27 V = 27 V = 27 p-value = 0.03461 p-value = 0.9888 p-value 0.01731 F-test for Equality of Variances F = 1.1311 p-value = 0.856 Student's t-test on Two Independent Pop'n Means: Analyzer A vs Analyzer B Ha: not equal to 0 Ha: less than 0. Ha: greater than 0 t = 9.2109 t = 9.2109 t = 9.2109 p-value 0.00003668 p-value=1 p-value…
ANOVA ▾ ANOVA Heart Rate Y Cases Gender Group Gender Group Sum of Squares 45030.005 168432.080 1794.005 192729.830 Descriptives Descriptives - Heart Rate Gender Group Female Male df 1 1 1 796 Mean Square Mean SD Control 148.000 16.271 Runners 115.985 15.972 Control 130.000 17.100 Runners 103.975 12.499 45030.005 168432.080 1794.005 242.123 N 200 200 200 200 F Assumption Checks ▾ Test for Equality of Variances (Levene's) F df2 VS-MPR* df1 P 5.562 3.000 796.000 <.001 59.104 *Vovk-Sellike Maximum p-Ratio: Based on the p-value, the maximum possible odds in favor of H, over He equals 1/(-e p log(pl) for p s.37 (Sellke, Bayarri, & Berger, 2001). 185.980 695.647 7.409 Residuals Note. Type III Sum of Squares Vovk-Sellike Maximum p-Ratio: Based on the p-value, the maximum possible odds in favor of H, over H, equals 1/(-e p log(p)) for p s.37 (Sellke, Bayarri, & Berger, 2001). Post Hoc Tests D < .001 <.001 0.007 Female Control VS-MPR* Standard Post Hoc Comparisons - Gender Group Male Control…

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