11. For the model Yi = Bo + B1x1li + B2x2i + Ui You obtain the fitted residuals and run the regression û; = a0 + a1xli + a2X2i + a3X3i + a4x4i + e; Unfortunately the only information you have retained is the number of observations (341) and the R2 of the auxiliary regression (0.15). What can you conclude about the joint significance of the parameters on x3 and x4 in a model of y which also includes x1 and x2 (at a 1% significance level)? A. Nothing - there is not enough data to do an F-test B. The parameters are jointly significant C. One of the parameters is significant D. The original regression suffers from Heteroskedasticity E. The parameters are both positive
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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