6.7.5 Misconception: a low p-value indicates high model fit or high predictive capacity.
6.7.4 Misconception: a low p-value indicates an important effect.
6.7.3 Misconception: 0.05 is the lifetime rate of false discoveries.
6.7.2 Misconception: a p-value is repeatable.
6.7.1 Misconception: p is the probability that the null is true and \(1-p\) is probability that the alternative is true.
6.7 Some major misconceptions of the p-value.
6.6.3 Two interpretations of the p-value.
6.6.2 This book covers frequentist approaches to statistical modeling and when a probability arises, such as the p-value of a test statistic, this will be a frequentist probability.
6.6 frequentist probability and the interpretation of p-values.
6.5 Parametric vs. non-parametric statistics.
6.4 P-values from the perspective of permutation.
6.3 A null distribution of t-values – the t distribution.
6.2 Pump your intuition – Creating a null distribution.
6.1 A p-value is the probability of sampling a value as or more extreme than the test statistic if sampling from a null distribution.
5.5.1 Interpretation of a confidence interval.
5.4.1 An example of bootstrapped standard errors using vole data.
5.3.4 Part IV – Generating fake data with for-loops.
5.3.3 part III - how do SD and SE change as sample size (n) increases?.
5.3 Using R to generate fake data to explore the standard error.
5.2 Using Google Sheets to generate fake data to explore the standard error.
5.1 The sample standard deviation vs. the standard error of the mean.
5 Variability and Uncertainty (Standard Deviations, Standard Errors, Confidence Intervals).
Part IV: Some Fundamentals of Statistical Modeling.
#ANOVA TWO WAY MINITAB 18 HOW TO#
4.2.8 How to add the interaction effect to response and effects plots.
4.2.7 How to combine the response and effects plots.
4.2.5 How to generate a Response Plot with a grid of treatments using ggplot2.
4.2.4 How to generate a Response Plot using ggpubr.
4.2.3 How to use the Plot the Model functions.
4.1.3 Combining Effects and Modeled mean and CI plots – an Effects and response plot.
4.1.2 Pretty good plot component 2: Modeled mean and CI plot.
4.1.1 Pretty good plot component 1: Modeled effects plot.
4.1 Pretty good plots show the model and the data.
3.3.2 Reshaping data – Transpose (turning the columns into rows).
3 Data – Reading, Wrangling, and Writing.
2.13 Figure 2i – Effect of ASK1 deletion on liver TG.
2.11 Figure 2g – Effect of ASK1 deletion on tissue-specific glucose uptake.
2.10 Figure 2f – Effect of ASK1 deletion on glucose infusion rate.
2.9.5 Figure 2e – inference from the model.
2.9 Figure 2e – Effect of ASK1 deletion on glucose tolerance (summary measure).
2.8 Figure 2d – Effect of ASK1 KO on glucose tolerance (whole curve).
2.7.8 Figure 2c – inference from the model.
2.7.6 Figure 2c – fit the model: m2 (gamma glm).
2.7.4 Figure 2c – fit the model: m1 (lm).
2.7.2 Figure 2c – check own computation of weight change v imported value.
2.7 Figure 2c – Effect of ASK1 deletion on final body weight.
2.6 figure 2b – effect of ASK1 deletion on growth (body weight).
Analyses for Figure 2 of “ASK1 inhibits browning of white adipose tissue in obesity”.
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 the estimation of treatment effects and the uncertainty in our estimates using linear models.
2 Analyzing 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.
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