Advanced Statistics - Biology 6030

Bowling Green State University, Fall 2017

Analysis of associations between variables: Linear Regression Analysis

Linear Regression Analysis allows us to evaluate whether and to what degree a dependent variable (Y) is explained by an independent variable (X). Towards this goal we examine the data from multiple cases for which both measures are available. Specifically we test whether knowing a value of X for a given case will provide us with a reasonable estimate for its corresponding Y. We first empirically obtain the line that best fits our data set, and we then test whether our data associate significantly (i.e. non-randomly) with this line.

Uses

Interpretation

How this is done

Handouts:

  • Additional Terms derived from the ANOVA Tables

    Considering an ANOVA table for a Regression analysis, understand and develop an intuitive feeling for the derivation and meaning of all terms listed below:

    Worksheet: Regression

    In R you would first import datafile "DummyData.txt", then you create the model for the linear regression, then report the ANOVA Table and results

    > dummy <- read.table("http://caspar.bgsu.edu/~courses/Stats/Labs/Datasets/DummyData.txt", header=TRUE)
    > dummy.lm <- lm(dummy$Brightness~dummy$Size)
    > summary(dummy.lm)
    > anova(dummy.lm)


    last modified: 2/19/15
    This material is copyrighted and MAY NOT be used for commercial purposes, 2001-2017 lobsterman.
    [ Advanced Statistics Course page | About BIO 6030 | Announcements ]
    [ Course syllabus | Exams & Grading | Glossary | Evaluations | Links ]