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(2) Suppose a purpose of study is to find a relationship between the "years of education" and "average hourly wage" Let x denotes "years of education" and y denote the "average hourly wage." The following summary statistics is obtained from a sample of 6 wage earners: 2000- 1800 100 1400 0

(2) Suppose a purpose of study is to find a relationship between the "years of education" and "average hourly wage" Let x denotes "years of education" and y denote the "average hourly wage." The following summary statistics is obtained from a sample of 6 wage earners: 2000- 1800 100 1400 0

MATLAB: An Introduction with Applications
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 1 Regression Residual Total a. Dependent Variable: y b. Predictors: (Constant), x Model Summary Model R 997⁰ Expected Carb Sum of Squares 69.143 .357 69.500 R Square 995 1 a. Predictors: (Constant), x b. Dependent Variable: y Normal PP Plot of Regression Standardized Residual Dependent Variable: y Observed On Preb ca ANOVA dif Adjusted R Square 994 Regre Stand 1 4 Mean Square 69.143 .089 Std. Error of the Estimate 29881 Scatterplat d Regrested Predicted F 774.400 Sig. .000⁰ (a) Does there seem to be linear relationship between the years of education and the hourly wages? (b) Determine the correlation between the years of education and the hourly wage. Would this correlation be valid if the relationship between the two variables was not linear? (c) Test the hypothesis to determine if years of education are related to the hourly wages. Use a = 0.05. (d) State the linear relationship between the two variables using least squares method. (e) Based on (d), what would be the estimated average wage for an wage earner who had 11 years of education? Is this estimate reliable?
Transcribed Image Text:+ Model 1 Regression Residual Total a. Dependent Variable: y b. Predictors: (Constant), x Model Summary Model R 997⁰ Expected Carb Sum of Squares 69.143 .357 69.500 R Square 995 1 a. Predictors: (Constant), x b. Dependent Variable: y Normal PP Plot of Regression Standardized Residual Dependent Variable: y Observed On Preb ca ANOVA dif Adjusted R Square 994 Regre Stand 1 4 Mean Square 69.143 .089 Std. Error of the Estimate 29881 Scatterplat d Regrested Predicted F 774.400 Sig. .000⁰ (a) Does there seem to be linear relationship between the years of education and the hourly wages? (b) Determine the correlation between the years of education and the hourly wage. Would this correlation be valid if the relationship between the two variables was not linear? (c) Test the hypothesis to determine if years of education are related to the hourly wages. Use a = 0.05. (d) State the linear relationship between the two variables using least squares method. (e) Based on (d), what would be the estimated average wage for an wage earner who had 11 years of education? Is this estimate reliable?
(2) Suppose a purpose of study is to find a relationship between the "years of education" and "average hourly wage" Let x denotes "years of education" and y denote the "average hourly wage." The following summary statistics is obtained from a sample of 6 wage earners: Model 1 20:00- 14.00- 1400 O X 12:00 (Constant) a. Dependent Variable: y 15.00 B 16:00 Unstandardized Coefficients Std. Error -9.071 1.571 Coefficients .856 .056 Standardized Coefficients Beta .997 t -10.600 27.828 Sig. .000 .000
Transcribed Image Text:(2) Suppose a purpose of study is to find a relationship between the "years of education" and "average hourly wage" Let x denotes "years of education" and y denote the "average hourly wage." The following summary statistics is obtained from a sample of 6 wage earners: Model 1 20:00- 14.00- 1400 O X 12:00 (Constant) a. Dependent Variable: y 15.00 B 16:00 Unstandardized Coefficients Std. Error -9.071 1.571 Coefficients .856 .056 Standardized Coefficients Beta .997 t -10.600 27.828 Sig. .000 .000
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