The regression equation is Health Index= y + a Age + ß Blood sugar + 8 Blood PressureSE20986339.28AgeBlood sugar209.2Blood pressure 207.2S = 962.233 R-Sq = 86.6% R-Sq (adj) = 76.5%CoefConstantAnalysis of VarianceSourceDFRegression3Residual Error. 4Total7SSCoef291271.95179.3225.423863180370357027566750T7.214.72*0.92MS7954393925892F***P0.0020.0090.308**P0.032a) What is dependent and independent variables?b) Fully write out the regression equation.c) Fill in the missing values **, ****, and *****.d) Hence test whether & is significant. Give reasons for your answer.e) Perform the F Test making sure to state the null and alternative hypothesis.

Independent variables are the explanatory variables that predict the dependent or the response…

Answered: The regression equation is Health…

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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• In a political science class, the teacher gives a midterm exam and a final. The association between midterm and final scores is linear. The summary statistics are shown below. Midterm Mean=75, Midterm Standard Deviation=10 Final Mean=75, Final Standard Deviation=10 r=0.8 According to the regression equation, for a student who gets 85 on the midterm (one standard deviation above average) what is the predicted final exam grade? 83 85 70 75
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Predicting Percent Body FatThis problem uses the dataset BodyFat, which gives the percent of weight made up of body fat for 100 men as well as other variables such as Age, Weight (in pounds), Height (in inches), and circumference (in cm) measurements for the Neck, Chest, Abdomen, Ankle, Biceps, and Wrist.1Using Neck Circumference to Predict Body FatThe regression line for predicting body fat percent using neck circumference is BodyFat^=-47.9+1.75(Neck). Click here for the dataset associated with this question. (a) What body fat percent does the line predict for a person with a neck circumference of 35 cm?Round your answer to two decimal places.BodyFat^= %What body fat percent does the line predict for a person with a neck circumference of 40 cm?Round your answer to one decimal place.BodyFat^= % (b) One of the men in the study had a neck circumference of 38.7 cm and a body fat percent of 11.3. Find the residual for this man.Round your answer to three decimal places.residual= %…
49
In a multiple regression analysis involving an intercept, 15 independent variables and 200 observations, Total SS = 800 and Residual SS = 240. Adjusted R-squared is
Could you please help interpret this SPSS output, i already conducted the multiple regression analysis i just need 1 and 2...THIS IS THE COMPLETE QUESTION Conduct a multiple regression analysis, using ‘monthly sales value’ as the Dependent Variable and entering all other variables simultaneously as the predictors. Determine the regression equation. Which are the best predictors and which predictors contribute very little to the estimate of mean monthly sales value? Interpret the regression results in terms of (1) the variance explained, (2) the overall significance of the regression equation, and (3) the significance, sign and value of each regression coefficient.
Please help me with question d, e, f, g, h, and I. Thank you.
Simple linear regression results: Dependent Variable: Heart. DiseaseIndependent Variable: BikingHeart. Disease = 18.115809 - 0.20845321 BikingSample size: 68R (correlation coefficient) = -0.94616452R-sq = 0.8952273Estimate of error standard deviation: 1.5273175 Correlation between Biking and Heart. Disease is: -0.94616452. Correlation between Smoking and Heart. Disease is: 0.32421. 1) State r 2 (i.e., the coefficient of determination) for “Biking” and “Heart.Disease” and explain what this value means in context of the data set.
mean of x = 2.882, standard deviation = 1.634 Mean of y = 2.588, standard deviation = 0.246 The correlation coefficient is 0.159 slope coefficient for regression line = .0239 Find the intercept for the regression line.
Define Residuals or errors in Alternative Regression Models?
Which of the following tools is not appropriate for studying the relationship between two numerica variables? Correlation coefficient Scatter plot Historam Regression
6

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