A friend has told you that his multiple regression has a high R-squared but all the estimates of the regression slopes are insignificantly different from zero on the basis of t tests of significance. This has probably happened because the Group of answer choices Explanatory variables are highly colinear Explanatory variables are not correlated Dependent variable does not change much Error term is heteroscedastic Error term is autocorrelated
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.
A friend has told you that his multiple regression has a high R-squared but all the estimates of the regression slopes are insignificantly different from zero on the basis of t tests of significance. This has probably happened because the Group of answer choices
Explanatory variables are highly colinear
Explanatory variables are not
Dependent variable does not change much
Error term is heteroscedastic
Error term is autocorrelated
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