Regression

The “Regression Analysis Quiz” is a multiple-choice assessment designed to test your understanding of key concepts in regression analysis. It covers topics such as: Simple & Multiple Linear Regression (model formulation, assumptions), Coefficient Interpretation (slope, intercept, significance), Model Evaluation Metrics (R², Adjusted R², F-test), Diagnostic Plots (residual analysis, training vs. testing loss curves), Overfitting & Underfitting (bias-variance tradeoff).

With 20 questions, this Regression Analysis Quiz evaluates both theoretical knowledge and practical application, making it useful for students or professionals reviewing regression techniques in statistics or machine learning. Let us start with the Regression Analysis Quiz now.

Online Regression Analysis Quiz with Answers

• What does the R-squared ($R^2$) metric indicate in the context of a regression model?
• What are some potential signs of overfitting in a regression model when examining training and testing loss values?
• What is the primary purpose of plotting the training and testing loss values of a regression model?
• Why is preprocessing input data important before using it in a house price prediction model?
• Which of the following steps are essential when utilizing a trained model for house price prediction?
• A regression analysis between sales (in Rs 1000) and price (in Rupees) resulted in the following equation $\hat{Y} = 5000 – 8X$. The equation implies that an
• In regression analysis, if the independent variable is measured in kilograms, the dependent variable
• A residual plot
• A regression analysis is inappropriate when
• If the slope of the regression equation $y=b_0 + b_1x$ is positive, then
• A residual is defined as
• A linear regression (LR) analysis produces the equation $Y=0.4X + 3$. This indicates that
• If the t-ratio for testing the significance of the slope of a simple linear regression equation is $-2.58$ and the critical values of the t-distribution at the 1% and 5% levels, respectively, are 3.499 and 2.365, then the slope is
• Ordinary least squares are used to estimate a linear relationship between a firm’s total revenue per week (in 1000s) and the average percentage discount from the list price allowed to customers by salespersons. A 95% confidence interval on the slope is calculated from the regression output. The interval ranges from 1.05 to 2.38. Based on this result, the researcher
• Multiple regression analysis is used when
• The adjusted value of the coefficient of determination
• If the F-test statistic for a regression is greater than the critical value from the F-distribution, it implies that
• The standard error of the regression measures the
• The following one is not the type of Linear Regression
• What does the $Y$ intercept ($b_0$) represent?

Statistical Modeling in R Language

The post is about Evaluating Regression Models Quiz with answers. There are 20 multiple-choice questions about regression models and their evaluation, covering regression analysis, assumptions of regression, coefficient of determination, predicted and predictor variables, etc. Let us start with the Evaluating Regression Models Quiz now.

Please go to Evaluating Regression Models Quiz 11 to view the test

MCQs Evaluating Regression Models Quiz with Answers

• When using the poly() function to fit a polynomial regression model, you must specify “raw = FALSE” so you can get the expected coefficients.
• A third-order polynomial regression model is described as which of the following?
• When evaluating models, what is the term used to describe a situation where a model fits the training data very well but performs poorly when predicting new data?
• An underfit model is said to have which of the following?
• What does regularization introduce into a model that results in a drop in variance?
• When tuning a model, a grid search attempts to find the value of a parameter that has the smallest —————-.
• Which situations are helped by using the cross-validation method to train your model?
• What is a strategy you can employ to address an underfit model?
• What is the difference between Ridge and Lasso regression?
• A training set is ————–.
• A testing set is —————.
• Regression coefficients may have the wrong sign for the following reasons
• The ratio of explained variation to the total variation of the following regression model is called $y_i = \beta_0 + \beta_1 x_{1i} + \beta_2x_{2i} + \varepsilon_i, \quad i=1,2,\cdots, n$.
• One cannot apply test of significance if $\varepsilon_i$ in the model $y_i = \alpha + \beta X_i+\varepsilon_i$ are
• The test used to test the individual partial coefficient in the multiple regression is
• When we fit a linear regression model we make strong assumptions about the relationships between variables and variance. These assumptions need to be assessed to be valid if we are to be confident in estimated model parameters. The questions below will help ascertain that you know what assumptions are made and how to verify these. Which of these is not assumed when fitting a linear regression model?
• Parveen previously fitted a linear regression model to quantify the relationship between age and lung function measured by FEV1. After she fitted her linear regression model she decided to assess the validity of the linear regression assumptions. She knew she could do this by assessing the residuals and so produced the following plot known as a QQ plot. How can she use this plot to see if her residuals satisfy the requirements for a linear regression?
• How can the following plot be used to see if residuals satisfy the requirements for a linear regression?
• Let the value of the $R^2$ for a model is 0.0104. What does this tell?
• The residuals are the distance between the observed values and the fitted regression line. If the assumptions of linear regression hold how would we expect the residuals to behave?

Performing Statistical Models in R

The post is about the MCQs correlation and regression Quiz. 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 and regression Quiz now.

Please go to MCQs Correlation and Regression 10 to view the test

MCQs Correlation and Regression

• Which of the following statement(s) about correlations is/are right? I. When dealing with a positive Pearson’s r, the line goes up. II. When the observations cluster around a straight line, we deal with a linear relation between the variables. III. The steeper the line, the smaller the correlation.
• Suppose you have collected the following data about how much chocolate people eat and how happy these people are. Amount of chocolate bars a week: 2, 4, 1.5, 2, 3. Grades for happiness: 7, 3, 8, 8, 6. (Note that the data follows paired observations) The Pearson Correlation between these two variables will be
• Suppose, you have investigated how eating chocolate bars influences the grades of students. For this purpose, you keep track of their chocolate intake (in bars per week) and assess their exam results one day later. Which statement(s) about the regression line $\hat{y} = 0.66x + 1.99$ is/are true?
• A professor uses the following formula to grade a statistics exam: $\hat{y} = 0.5 + 0.53x$. After obtaining the results the professor realizes that the grades are very low, so he might have been too strict. He decides to level up all results by one point. What will be the new grading equation?
• What is the explained variance? And how can you measure it?
• A teacher asks his students to fill in a form about how many cigarettes they smoke every week and how much they weigh. After obtaining the data/results, he makes a scatterplot and analyses the data points. Pearson’s r is computed to assess the correlation and found to of 0.80. From the correlation results, it is concluded that smoking more cigarettes causes high body weight. What is wrong with this analysis?
• What can you conclude about a Pearson’s r that is bigger than 1?
• Why do we use squared residuals when computing the regression line?
• What technique is used to help identify the nature of the relationship between two variables?
• Regression is a form of this?
• The correlation coefficient is used to determine
• If there is a very strong correlation between two variables then the correlation coefficient must be
• Regression modeling is a statistical framework for developing a mathematical equation that describes how
• In the least squares regression, which of the following is not a required assumption about the error term $\varepsilon$?
• In a regression analysis if $R^2=1$ then
• In a regression analysis, the variable that is used to explain the change in the outcome of an experiment, or some natural process, is called
• For a mathematical model related to a straight line, if a value for the x variable is specified, then
• When a regression line passes through the origin then
• The range of the multiple correlation coefficient is
• In regression analysis, the variable that is being predicted is

Computer MCQs Online Test

MCQs in Statistics