Given a large sample of employees in a given industry, you run a regression for annual salary (in $1,000s) versus thee X variables: X1, the number of years employed in the industry; X2, a dummy for college degree (1 if employee has at least one college degree, 0 otherwise); and X3, a dummy for gender (1 for male, 0 for female). The estimated regression equation is Y = 47.9 + 2.7*X1 + 4.9*X2 + 1.8*X3. Consider two employees, not part of the sample, with the following characteristics: Jim, who has 5 years of experience in the industry and no college degree; and Mary, who has 10 years of experience in the company and a college degree. Which of the following is the prediction of the difference between their annual salaries (Jim's salary minus Mary's salary, in $1,000s)? a. 16.6 b. -20.2 c. -6.8 d. -16.6
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.
Given a large sample of employees in a given industry, you run a regression for annual salary (in $1,000s) versus thee X variables: X1, the number of years employed in the industry; X2, a dummy for college degree (1 if employee has at least one college degree, 0 otherwise); and X3, a dummy for gender (1 for male, 0 for female). The estimated regression equation is Y = 47.9 + 2.7*X1 + 4.9*X2 + 1.8*X3. Consider two employees, not part of the sample, with the following characteristics: Jim, who has 5 years of experience in the industry and no college degree; and Mary, who has 10 years of experience in the company and a college degree. Which of the following is the prediction of the difference between their annual salaries (Jim's salary minus Mary's salary, in $1,000s)?
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