8.2 A linear model can be fit to data with continuous, discrete, or categorical \(X\) variables.8.1.3 Comparing the error-draw and conditional-draw ways of specifying the linear model.8.1.2 The “conditional draw” specification.8.1 Two specifications of a linear model.7.2 A non-Neyman-Pearson concept of power.6.12.1 Primary sources for recommendations.6.11.1 Controlling the family-wise error rate when all tests are testing the same hypothesis.6.11 Multiple testing and controlling for false positives.6.9 On using p-values in experimental biology.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: \(p > 0.05\) means there is no effect of treatment.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. 5.7.1 95% of 95% CIs of the difference include the true difference.5.7 Confidence limits of a difference between means.5.6.1 The standard error of a difference between means is the standard deviation of the sampling distribution of the difference.5.6 Standard error of a difference between means.5.5.4 A plot of a “parametric” CI vs. bootstrap CI of the means.5.5.2 Interpretation of a confidence interval.5.4.1 An example of bootstrapped standard errors.5.3.2 Using R to generate fake data to explore the standard error.5.3.1 Using Google Sheets to generate fake data to explore the standard error.5.3 Simulations – using fake data as an intuition pump.5.1 Standard errors are used to compute p-values and confidence intervals.5 Variability and Uncertainty (Standard Deviations, Standard Errors, and Confidence Intervals).Part III: Some Fundamentals of Statistical Modeling.4.2.9 How to add the interaction effect to response and effects plots.4.2.8 How to combine the response and effects plots.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |