Q14 The OLS estimator is derived by: A) connecting the $Y_i$ corresponding to the lowest $X_i$ observation with the $Y_i$ corresponding to the highest $X_i$ observation. B) making sure that the standard error of the regression equals the standard error of the slope estimator. C) minimizing the sum of absolute residuals. D) minimizing the sum of squared residuals
Added by Josefa S.
Close
Step 1
OLS stands for Ordinary Least Squares, which is a method used to estimate the parameters of a linear regression model. The goal of OLS is to find the line that best fits the data points by minimizing the difference between the observed values (Yi) and the Show more…
Show all steps
Your feedback will help us improve your experience
Tim Thornhill and 51 other Intro Stats / AP Statistics educators are ready to help you.
Ask a new question
Labs
Want to see this concept in action?
Explore this concept interactively to see how it behaves as you change inputs.
Key Concepts
Recommended Videos
Madhur L.
In simple linear regression, the method of least squares determines the line that minimizes the sum of squared deviations between the observed y values and: a. the average of the y values b. the average of the x values c. the fitted line d. the line of residual errors
Arun B.
The Ordinary Least Squares (OLS) estimators for β0 and β1 in the y = β0 + β1X + e model are formulas derived by minimizing _____________. options: 1) the sum of the error terms or residuals 2) the sum of the squared residuals 3) the slope of the regression line 4) the fit of the regression line to the observed data.
Recommended Textbooks
Elementary Statistics a Step by Step Approach
The Practice of Statistics for AP
Introductory Statistics
Transcript
Watch the video solution with this free unlock.
EMAIL
PASSWORD