ng 50 observations, the following regression output is obtained from estimating y = 60 + 61x + 82d1 + 63d2 + E. Standard Coefficients -0.75 t Stat -2.50 P-value 0.0160 Error Intercept 0.30 2.86 1.10 2.60 0.0125 d1 d2 -13.60 16.00 -0.85 0.3997 5.70 1.50 3.80 0.0004 .Compute or x= 265, d 1, and d2 = 0; compute for x= 265, d, = 0, and d2 = 1. (Round your answers to 2 decimal places.) x = 265, di = 1 and d2 = 0 x = 265, d, = 0 and d, = 1 b-1. Interpret d, and d,. (You may select more than one answer. Single click the box with the question mark to produce a check mark for a correct answer and double click the box with the question mark to empty the box for a wrong answer. Any boxes le with a question mark will be automatically graded as incorrect.) 2 When da =1, ŷ is 13.60 units greater than when di = 0, holding everything else constant. ? When d2 =1, ŷ is 5.70 units greater than when d2 - e, holding everything else constant. 7When da - 1, ŷ is 13.60 units less than when d 0, holding everything else constant. 7 When d2 - 1, ý is 5.70 units less when d2 - 0, holding everything else constant. b-2. Are both dummy variables individually significant at the 5% level?
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.
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