Consider the two variable regression model: Y = Bo+ B,Edu + B2Exp21 + U. where Y denotes the average monthly income, Edu denotes the number of years of education, Exp denotes the number of years of experience, and u, denotes the error term Suppose the researcher wants to test whether the effect of education on average monthly income and the effect of experience on the average monthly income of an individual are the same or not. So, the test the researcher wants to conduct is Ho: B, = B, vs. H, B, #B2. The hypotheses can be tested by modifying the original regression equation to turn the restriction into a restriction on a single regression coefficient. Suppose the regression function is modified in the following way: Y, = Bo +Y,Edu+ B2W, + u, where y, = B,-B2 and W, = Edu, + Exp2 Since y = B, - B2, the test the researcher wants to conduct will now be Ho: y=0 vs. H,: y 0. Let y, and SE,), denote the estimated slope coefficient of Y, and the standard error of 71, respectively. If SEG,) is 1.14 and y, is 1.75, then the 95% confidence interval for the difference between the coefficients, B, - B2, denoted by y1, is ) (Round your answer to two decimal places. Enter a minus sign if your answer is negative.) v the null hypothesis Ho y=0. Based on the calculated confidence interval, we can say that at the 5% significance level, we will reject fail to reject Enter your answer in each of the answer boxes.

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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.