Tagged: MCQs Regression

This quiz is about MCQs Regression and Correlation analysis.

This Quiz contains MCQs about Correlation and Regression Analysis, Multiple Regression Analysis, Coefficient of Determination (Explained Variation), Unexplained Variation, Model Selection Criteria, Model Assumptions, Interpretation of results, Intercept, Slope, Partial Correlation, Significance tests, OLS Assumptions, Multicollinearity, Heteroscedasticity, Autocorrelation, graphical representation of the relationship between the variables, etc. Let us start MCQs about Correlation and Regression Analysis.

Correlation is a statistical measure used to determine the strength and direction of the mutual relationship between two quantitative variables. The value of the correlation lies between $-1$ and $1$. The regression describes how an explanatory variable is numerically related to the dependent variables.

Both of the tools are used to represent the linear relationship between the two quantitative variables. The relationship between variables can be observed either using graphical representation between the variables or numerical computation using appropriate computational formula.

Note that neither regression nor correlation analyses can be interpreted as establishing some cause-and-effect relationships. Both of these can be used to indicate only how or to what extent the variables under study are associated (or mutually related) with each other. The correlation coefficient measures only the degree (strength) and direction of linear association between the two variables. Any conclusions about a cause-and-effect relationship must be based on the judgment of the analyst.

This Quiz contains MCQs about Correlation and Regression Analysis, Multiple Regression Analysis, Coefficient of Determination (Explained Variation), Unexplained Variation, Model Selection Criteria, Model Assumptions, Interpretation of results, Intercept, Slope, Partial Correlation, Significance tests, OLS Assumptions, Multicollinearity, Heteroscedasticity, Autocorrelation, graphical representation of the relationship between the variables, etc. Let us start MCQs about Correlation and Regression Analysis.

Please go to Correlation and Regression 2 to view the test

Correlation is a statistical measure used to determine the strength and direction of the mutual relationship between two quantitative variables. The value of correlation lies between $-1$ and $1$. The regression describes how an explanatory variable is numerically related to the dependent variables.

Both of the tools are used to represent the linear relationship between the two quantitative variables. The relationship between variables can be observed either using graphical representation between the variables or numerical computation using appropriate computational formula.

Note that neither regression nor correlation analyses can be interpreted as establishing some cause-and-effect relationships. Both of these can be used to indicate only how or to what extent the variables under study are associated (or mutually related) with each other. The correlation coefficient measures only the degree (strength) and direction of linear association between the two variables. Any conclusions about a cause-and-effect relationship must be based on the judgment of the analyst.

This quiz is about MCQ on correlation and regression analysis.

This Section contains MCQs on Correlation Analysis, Simple Regression Analysis, Multiple Regression Analysis, Coefficient of Determination (Explained Variation), Unexplained Variation, Model Selection Criteria, Model Assumptions, Interpretation of results, Intercept, Slope, Partial Correlation, Significance tests, OLS Assumptions, Multicollinearity, Heteroscedasticity, Autocorrelation, etc. Let us start MCQ on Correlation and Regression Analysis

Please go to Correlation and Regression 1 to view the test

Correlation is a statistical measure used to determine the strength and direction of the mutual relationship between two quantitative variables. The regression describes how an explanatory variable is numerically related to the dependent variables.

Both of the tools are used to represent the linear relationship between the two quantitative variables. The relationship between variables can be observed either using graphical representation between the variables or numerical computation using appropriate computational formula.

Note that neither regression nor correlation analyses can be interpreted as establishing some cause-and-effect relationships. Both of these can be used to indicate only how or to what extent the variables under study are associated (or mutually related) with each other. The correlation coefficient measures only the degree (strength) and direction of linear association between the two variables. Any conclusions about a cause-and-effect relationship must be based on the judgment of the analyst.