Regression

The post is about the MCQs Regression Analysis Quiz with Answers. There are 20 multiple-choice questions from correlation analysis, regression analysis, correlation matrix, coefficient of determination, residuals, predicted values, Model selection, regularization techniques, etc. Let us start with the MCQs regression analysis quiz.

MCQs Regression Analysis Quiz with Answers

• What term describes an inverse relationship between two variables?
• Regression analysis aims to use math to define the ————– between the sample $X$’s and $Y$’s to understand how the variables interact.
• Regression models are groups of ————– techniques that use data to estimate the relationships between a single dependent variable and one or more independent variables.
• ————- finds the mean of $Y$ given a particular value of $X$.
• ————- is a technique that estimates the relationship between a continuous dependent variable and one or more independent variables.
• The best-fit line is the line that fits the data best by minimizing some —————.
• What is the sum of the squared differences between each observed value and the associated predicted value?
• What does the circumflex symbol, or “hat” (^), indicate when used over a coefficient?
• How does a data professional determine if a linearity assumption is met?
• Which of the following statements accurately describes the normality assumption?
• What type of visualization uses a series of scatterplots that show the relationships between pairs of variables?
• R squared measures the —————- in the dependent variable $Y$, which is explained by the independent variable, $X$.
• Which linear regression evaluation metric is sensitive to large errors?
• Which statements accurately describe coefficients and p-values for regression model interpretation?
• What is the difference between observed or actual values and the predicted values of a regression line?
• Which of the following statements accurately describes a randomized, controlled experiment?
• What concept refers to how two independent variables affect the $Y$ dependent variable?
• Adjusted R squared is a variation of the R squared regression evaluation metric that ————— unnecessary explanatory variables.
• What variable selection process begins with the full model that has all possible independent variables?
• Which of the following are regularized regression techniques?

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The post is about MCQs correlation and regression. There are 20 multiple-choice questions covering topics related to the basics of correlation and regression analysis, best-fitting trends, least square regression lines, interpretation of correlation and regression coefficients, and regression plots. Let us start with the MCQs Correlation Regression Quiz.

Please go to Important MCQs Correlation Regression 5 to view the test

MCQs Correlation Regression Analysis

• 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 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 dependent variable on the basis of one or more independent variables is called
• The method of least squares directs that select 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 is 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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The post is about Correlation Regression Analysis MCQs. There are 20 multiple-choice questions. The quiz covers topics related to the coefficient of correlation, regression analysis, simple linear regression equations, interpretation of correlation, and regression coefficients. Let us start with the Correlation Regression Analysis MCQs Quiz.

Please go to Correlation Regression Analysis MCQs 4 to view the test

Online Correlation Regression Analysis MCQs with Answers

• The value of the coefficient of correlation lies between
• If the scatter diagram is drawn the scatter points lie on a straight line, then it 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 $\beta_{xy} = ?$

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