Correlation and Regression Analysis MCQs 4

This Post contains Correlation and Regression Analysis MCQs, 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 with the Correlation and Regression Analysis MCQs.

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.

Online Correlation and Regression Analysis MCQs

• The value of the coefficient of correlation lies between
• If the scatter diagram is drawn the scatter points lie on a straight line, then indicates
• In the model $Y= mX+ a\,\,\,$, $Y$ is also known as the:
• The regression equation is the line with a slope passing through
• If the regression equation is equal to $Y=23.6 – 54.2X$, then $23.6$ is the _________ while $-54.2$ is the ________ of the regression line.
• The sample coefficient of correlation
• If the equation of the regression line is $y = 5$, then what result will you take out from it?
• Which of the following relationships holds
• In regression equation $y=\alpha + \beta X + e$, both $X$ and $y$ variables are
• If $R^2$ is zero, that is no collinearity/ Multicollinearity, the variance inflation factor (VIF) will be
• The method of least squares finds the best-fit line that _________ the error between observed & estimated points on the line
• The predicted rate of response of the dependent variable to changes in the independent variable is called
• The slope of the regression line of $Y$ on $X$ is also called
• In a simple regression, the number of unknown constants are
• In a simple regression equation, the number of variables are
• If $Y=2+0.6x$ then the value of the slope will be
• Which of the following can never be taken as the coefficient of correlation?
• When $\beta_{yx}$ is positive, then $\beta_{xy}$ will be
• If $Y=2+0.6X$ then the value of $Y$-intercept will be
• If $r=0.6$ and $\beta_{yx}=1.8$ then $\beta_{xy} = ?$

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 a graphical representation between the variables or numerical computation using an 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.

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