4.8.5 Figure 2e – inference from the model.4.8 Figure 2e – Effect of ASK1 deletion on glucose tolerance (summary measure).4.7 Figure 2d – Effect of ASK1 KO on glucose tolerance (whole curve).4.6.8 Figure 2c – inference from the model.4.6.6 Figure 2c – fit the model: m2 (gamma glm).4.6.4 Figure 2c – fit the model: m1 (lm).4.6.2 Figure 2c – check own computation of weight change v imported value.4.6 Figure 2c – Effect of ASK1 deletion on final body weight.4.5 figure 2b – effect of ASK1 deletion on growth (body weight).4 Analyses for Figure 2 of “ASK1 inhibits browning of white adipose tissue in obesity”.

3 Background physiology to the experiments in Figure 2 of “ASK1 inhibits browning of white adipose tissue in obesity”.

This, raises the question, what is “an effect?”

2.1 This text is about using linear models to estimate treatment effects and the uncertainty in our estimates.

2 Analyzing experimental data with a linear model.

Part II: An introduction to the analysis of experimental data with a linear model.

1.10 Create an R Markdown file for this Chapter.

1.9 Working on a project, in a nutshell.

1.8 Create an R Studio Project for this textbook.

1.4 If you didn’t modify the workspace preferences from the previous section, go back and do it.

1.3 Open R Studio and modify the workspace preference.

1.2 Download and install R and R studio.

1 Getting Started – R Projects and R Markdown.

including mapping between linear models and classical tests.

0.1 Why bother with linear models – aren’t t-tests and ANOVA good enough?.

Download Data File Converter 5.3.4