Loading [MathJax]/extensions/TeX/boldsymbol.js
Skip to main content
Library homepage
 

Text Color

Text Size

 

Margin Size

 

Font Type

Enable Dyslexic Font
Statistics LibreTexts

Search

  • Filter Results
  • Location
  • Classification
    • Article type
    • Author
    • Cover Page
    • License
    • Show TOC
    • Embed Jupyter
    • Transcluded
    • OER program or Publisher
    • Autonumber Section Headings
    • License Version
  • Include attachments
Searching in
About 2 results
  • https://stats.libretexts.org/Courses/Cerritos_College/Introduction_to_Statistics_with_R/15%3A_Regression_in_R/15.02%3A_Estimating_a_Linear_Regression_Model
    And since this is the right answer, it’s probably worth making a note of the fact that our regression coefficients are estimates (we’re trying to guess the parameters that describe a population!), whi...And since this is the right answer, it’s probably worth making a note of the fact that our regression coefficients are estimates (we’re trying to guess the parameters that describe a population!), which is why I’ve added the little hats, so that we get \ \hat{b_0} and \ \hat{b_1} rather than b0 and b1.
  • https://stats.libretexts.org/Workbench/Learning_Statistics_with_SPSS_-_A_Tutorial_for_Psychology_Students_and_Other_Beginners/12%3A_Linear_Regression/12.02%3A_Estimating_a_Linear_Regression_Model
    When the regression line is good, the residuals (the lengths of the solid black lines) all look pretty small, as shown in Figure 12.4, but when the regression line is a bad one, the residuals are a lo...When the regression line is good, the residuals (the lengths of the solid black lines) all look pretty small, as shown in Figure 12.4, but when the regression line is a bad one, the residuals are a lot larger, as you can see from looking at Figure 12.5.

Support Center

How can we help?