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
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
