MCQs Correlation Regression 5

This Post contains MCQs Correlation 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, Multicollinearity, Heteroscedasticity, Autocorrelation, etc. Let us start MCQs Correlation 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 coefficient lies between $-1$ and $1$. The regression analysis describes how an explanatory variable is numerically related to the dependent variables.

Both of the tools (correlation coefficient and regression analysis) are used to represent and analyze the linear relationship between the two quantitative variables. The relationship between variables can be observed using a graphical representation (visual) between the variables. One can also compute the strength of the relationship between variables by performing numerical calculations using appropriate computational formulas.

Note that neither regression nor correlation analyses can be interpreted as establishing some cause-and-effect relationships. Both correlation and regression are used to indicate 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.

Online MCQs Correlation Regression Analysis with Answers

• In Regression Analysis $\sum\hat{Y}$ is equal to
• In the Least Square Regression Line, $\sum(Y-\hat{Y})^2$ is always
• Which one is equal to explained variation divided by total variation?
• The best-fitting trend is one for which the sum of squares of error is
• If a straight line is fitted to the data then
• In Regression Analysis, the regression line ($Y=\alpha+\beta X$) always intersect at the point
• In the Least Square Regression line, the quantity $\sum(Y-\hat{Y})$ is always
• If all the values fall on the same straight line and the line has a positive slope then what will be the value of the Correlation coefficient $r$:
• For the Least Square trend $\hat{Y}=\alpha+\beta X$
• The regression line always passes through
• The process by which we estimate the value of the dependent variable based on one or more independent variables is called
• The method of least squares directs the selection of a regression line where the sum of the squares of the deviations of the points from the regression line is
• A relationship where the flow of the data points is best represented by a curve is called
• All the data points falling along a straight line are called
• The predicted rate of response of the dependent variable to changes in the independent variable is called
• The independent variable is also called
• In the regression equation $Y=a+bX$, the $Y$ is called
• In the regression equation $Y=a+bX$, the $X$ is called
• The dependent variable in a regression line is
• The correlation coefficient is the _________ of two regression coefficients.

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