Suppose we have data on dependent variable y and explanatory variables x 1 and x 2, all measured in units. We estimate the following linear regression: 1659 = 1123.68 - 0.0000 0.5757 0.5752 Number of cbs = E( 2, 1656) Prob > F R-squared Adj R-squared %3! Coef. Std. Err. P>It| x_1 1 x_2 1 cons I 1.003119 .68 80123 .2408667 .0252676 .0253665 .077143 0.000 0.000 0.002 39.70 27.12 3.12 Which of the following statements is correct? Select one: a. We can conclude that there is no nonlinear effect of x_2 on y. b. An increase of 1% in x_1 increases y by 0.3119%, when x_2 remains constant. C. The estimated level of y for someone with x_1 equal to 10 and x 2 equal to 8 is 15.535. d. The estimated level of y for someone with x_1 equal to 10 and x 2 equal to 8 is 15.776.
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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