Regression

The post is about the MCQs Correlation and Regression Quiz. There are 20 multiple-choice questions about correlation and regression analysis, covering MCQs about correlation analysis, regression analysis assumptions, coefficient of determination, predicted and predictor variables, etc. Let us start with the MCQS correlation and Regression Quiz.

Online MCQs Correlation and Regression Quiz

• The distribution of sample correlation is
• The major difference between regression analysis and correlation analysis is that in regression analysis:
• The term homoscedasticity refers to
• The sum of squares of which type of deviations is minimized by the least square regression:
• A random sample of paired observations has been selected and the sample correlation coefficient is -1. From this result, we know that:
• The output of a certain chemical-processing machine is linearly related to temperature. At $-10^\circ C$ the processor output is 200 KGs per hour at $40^\circ C$ the output is 220 KGs per hour. Calculate the linear equation for KGs per hour of output ($Y$) as a function of temperature in degrees Celsius ($X$):
• What information is given by a value of the coefficient of determination?
• Whenever predictions are made from the estimated regression line, the relation between $X$ and $Y$ is assumed to be:
• The estimated coefficient of determination is equal to all except which of the following?
• The coefficient of partial determination differs from the coefficient of multiple determinations in that
• The coefficient of multiple determination is 0.81. Thus, the multiple correlation coefficient is
• In regression equation $y=\alpha + \beta x + \varepsilon$, both $x$ and $y$ variables are
• The dependent variable is also known as
• The regression coefficients may have the wrong sign for the following reasons:
• The regression model can be used to
• If one wished to test the relationship between social and juvenile delinquency with the number of siblings held constant, the most appropriate technique would be
• Given that $X=1.50 + 0.50Y$, what is the predicted value for a $Y$ value of 6?
• Which of the following regression equations represents the strongest relationship between $X$ and $Y$?
• Which of the following is NOT an assumption underlying regression analysis?
• Two variables are said to be uncorrelated if

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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.

Please go to MCQs Regression Analysis Quiz 7 to view the test

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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