how would I write a conclusion for this stepwise mutlitple linear regression.CoefficientsaModelUnstandardized CoefficientsStandardized CoefficientstSig.CorrelationsCollinearity StatisticsBStd. ErrorBetaZero-orderPartialPartToleranceVIF1(Constant)4.987.228 21.919<.001 energy level.572.044.44413.045<.001.444.444.4441.0001.0002(Constant)4.519.228 19.841<.001 energy level.439.046.3419.594<.001.444.343.315.8501.177exercise.453.061.2667.472<.001.398.273.245.8501.1773(Constant)4.762.234 20.381<.001 energy level.436.045.3399.620<.001.444.344.312.8491.177exercise.427.060.2507.070<.001.398.260.229.8391.191Smoking History-.324.082-.129-3.945<.001-.183-.149-.128.9841.017a. Dependent Variable: overall state of health

Stepwise regression is the step-by-step iterative construction of a regression model that involves…

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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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The summary output obtained from fitting the multiple regression are given below. Model Unstandardized Coefficients Standardized Sig. Coefficients B Std. Error Beta -3.512 (Constant) Education (years) -3019.226 859.789 .000 658.518 45.852 .581 14.362 .000 Gender -1615.440 253.239 -.249 -6.379 .000 Age (years) 45.008 10.469 .163 4.299 .000 Dependent Variable: Beginning Salary, Male=0 & Female=1. (a) Write down the estimated multiple regression model of the beginning salary on education, gender and age of employees of a company. (b) Interpret estimated regression coefficient values. (c) Find the predicted beginning salary for an employee who is 24 years old male and has 17 years of education.
Hi I know that you can’t answer all questions. Can you just answer question 15 please.
1. What is R2 and the standard error for the regression? 1a. What is your point estimate enrollment for Spring 2022?
Sam had 3 caffeinated drinks on april 24 Use linear regression to predict the number non-caffeinated drink he had on April 24 and calculate the error of estimate for your prediction.
The data are the ages of criminals and their victims. The regression output is shown in a separate tab from the data. Do the data support that a prediction of victim age can be obtained given the data provided in the file? Cite the elements of the output you used to draw your conclusion.
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
Sarah has some data and wants to run a linear regression model on it. Before she runs the test, she knows she needs to check to make sure all conditions are met. Based only on the plots below, what condition is not met? Data Scatterplot Normal Probability Plot Residual Scatterplot 25 100 Regression Standardoed Predcted Vale Observed Cum Prob Linearity O Normality Equal Variances O Independence O The plots do not show a problem with any of the listed conditions.
Hi I know that you can’t answer all questions. Can you just answer question 16 please.
Question. Using the following regression result, make your answers to the questions. Call: Im(formula - wage - educ, data - data) Residuals: 10 Median -5.1579 -2.5066 0.3816 2.4539 4.4737 Min 30 Max Coefficients: Estimate Std. Error t value Pr(>1tl) (Intercept) 9.0526 educ 5.1420 1.761 0.11635 2.1842 0.5994 3.644 0.00655 ** --- Signif. codes: 0 ***** 0.001 *** 0.01 ** 0.05 . 0.1' 1 Residual standard error: 3.305 on 8 degrees of freedom Multiple R-squared: 0.6241, F-statistic: 13.28 on 1 and 8 DF, p-value: 0.006549 Adjusted R-squared: 0.5771 1) Using the estimate and standard error of educ variable, perform the following hypothesis test. Ho:Beduc = 0 H1: Beduc # 0 2) Interpret the above hypothesis test result.
From the results shown above, write the regression equation.
(iii) determine the values of *, **, ***, **** (iv) conduct a hypothesis test at the 5% significance level to determine whether ø is significant
What does the slope and the intercept mean in this setting?

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Coefficients^a
Model	Unstandardized Coefficients	Standardized Coefficients	t	Sig.	Correlations	Collinearity Statistics
B	Std. Error	Beta	Zero-order	Partial	Part	Tolerance	VIF
1	(Constant)	4.987	.228		21.919	<.001
energy level	.572	.044	.444	13.045	<.001	.444	.444	.444	1.000	1.000
2	(Constant)	4.519	.228		19.841	<.001
energy level	.439	.046	.341	9.594	<.001	.444	.343	.315	.850	1.177
exercise	.453	.061	.266	7.472	<.001	.398	.273	.245	.850	1.177
3	(Constant)	4.762	.234		20.381	<.001
energy level	.436	.045	.339	9.620	<.001	.444	.344	.312	.849	1.177
exercise	.427	.060	.250	7.070	<.001	.398	.260	.229	.839	1.191
Smoking History	-.324	.082	-.129	-3.945	<.001	-.183	-.149	-.128	.984	1.017
a. Dependent Variable: overall state of health