b. What is the sample regression equation? (Negative values should be indicated by a minus sign. Round your answers to 2 decimalplaces.)ý =Education +Experience +Agec. Predict the hourly wage rate for a 29-year-old worker with 3 years of higher education and 4 years of experience. (Do not roundintermediate calculations. Round your answer to 2 decimal places.) Using data from 50 workers, a researcher estimates Wage = 6g + 61Education + 62Experience + 63Age + ɛ, where Wage is the hourlywage rate and Education, Experience, and Age are the years of higher education, the years of experience, and the age of the worker,respectively. The regression results are shown in the following table.StandardCoefficientsErrort Statp-Value4.121.48InterceptEducation6.110.14491.140.303.800.0004ExperienceAge0.440.182.440.0184-0.020.07-0.290.7764a-1. Interpret the point estimate for 61.O As Education increases by 1 year, Wage is predicted to increase by 1.14/hour.O As Education increases by 1 year, Wage is predicted to increase by 0.44/hour.O As Education increases by 1 year, Wage is predicted to increase by 1.14/hour, holding Age and Experience constant.As Education increases by 1 year, Wage is predicted to increase by 0.44/hour, holding Age and Experience constant.a-2. Interpret the point estimate for 62.O As Experience increases by 1 year, Wage is predicted to increase by 1.14/hour.As Experience increases by 1 year, Wage is predicted to increase by 0.44/hour.O As Experience increases by 1 year, Wage is predicted to increase by 1.14/hour, holding Age and Education constant.O As Experience increases by 1 year, Wage is predicted to increase by 0.44/hour, holding Age and Education constant.

The regression equation from the results shown in the table is, Wage=δ0+δ1 Education+δ2…

Using data from 50 workers, a researcher estimates Wage = 60 + 61Education + 62Experience + 63Age + ɛ, where Wage is the hourly wage rate and Education, Experience, and Age are the years of higher education, the years of experience, and the age of the worker, respectively. The regression results are shown in the following table. Standard Coefficients Error t Stat p-Value 4.12 Intercept Education 6.11 1.48 0.1449 1.14 0.30 3.80 0.0004 Experience Age 0.0184 0.7764 0.44 0.18 2.44 -0.02 0.07 -0.29 a-1. Interpret the point estimate for 61.

Using data from 50 workers, a researcher estimates Wage = 60 + 61Education + 62Experience + 63Age + ɛ, where Wage is the hourly wage rate and Education, Experience, and Age are the years of higher education, the years of experience, and the age of the worker, respectively. The regression results are shown in the following table. Standard Coefficients Error t Stat p-Value 4.12 Intercept Education 6.11 1.48 0.1449 1.14 0.30 3.80 0.0004 Experience Age 0.0184 0.7764 0.44 0.18 2.44 -0.02 0.07 -0.29 a-1. Interpret the point estimate for 61.

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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Concept explainers

Correlation

Correlation defines a relationship between two independent variables. It tells the degree to which variables move in relation to each other. When two sets of data are related to each other, there is a correlation between them.

Linear Correlation

A correlation is used to determine the relationships between numerical and categorical variables. In other words, it is an indicator of how things are connected to one another. The correlation analysis is the study of how variables are related.

Regression Analysis

Regression analysis is a statistical method in which it estimates the relationship between a dependent variable and one or more independent variable. In simple terms dependent variable is called as outcome variable and independent variable is called as predictors. Regression analysis is one of the methods to find the trends in data. The independent variable used in Regression analysis is named Predictor variable. It offers data of an associated dependent variable regarding a particular outcome.

Question

100%

b. What is the sample regression equation? (Negative values should be indicated by a minus sign. Round your answers to 2 decimal
places.)
ý =
Education +
Experience +
Age
c. Predict the hourly wage rate for a 29-year-old worker with 3 years of higher education and 4 years of experience. (Do not round
intermediate calculations. Round your answer to 2 decimal places.)

Using data from 50 workers, a researcher estimates Wage = 6g + 61Education + 62Experience + 63Age + ɛ, where Wage is the hourly
wage rate and Education, Experience, and Age are the years of higher education, the years of experience, and the age of the worker,
respectively. The regression results are shown in the following table.
Standard
Coefficients
Error
t Stat
p-Value
4.12
1.48
Intercept
Education
6.11
0.1449
1.14
0.30
3.80
0.0004
Experience
Age
0.44
0.18
2.44
0.0184
-0.02
0.07
-0.29
0.7764
a-1. Interpret the point estimate for 61.
O As Education increases by 1 year, Wage is predicted to increase by 1.14/hour.
O As Education increases by 1 year, Wage is predicted to increase by 0.44/hour.
O As Education increases by 1 year, Wage is predicted to increase by 1.14/hour, holding Age and Experience constant.
As Education increases by 1 year, Wage is predicted to increase by 0.44/hour, holding Age and Experience constant.
a-2. Interpret the point estimate for 62.
O As Experience increases by 1 year, Wage is predicted to increase by 1.14/hour.
As Experience increases by 1 year, Wage is predicted to increase by 0.44/hour.
O As Experience increases by 1 year, Wage is predicted to increase by 1.14/hour, holding Age and Education constant.
O As Experience increases by 1 year, Wage is predicted to increase by 0.44/hour, holding Age and Education constant.

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