![]() ![]() 14.4 Predictions and local calibration of mixed models.14.3.1 Case study: Predicting tree height with mixed models.14.2 Comparing ordinary least squares and mixed effects models.13.4 Evaluating count regression models.13.3.1 Fitting zero-inflated models in R.13.2.1 Case study: predicting fishing success.13.2 Count data and their distributions.12.2 An overview of generalized linear models.11.2.2 ANCOVA hypothesis tests and outcomes.11.2.1 Testing equality of slopes in ANCOVA.10.4.2 Visualizing differences across groups.10.2.1 Partitioning the variability in ANOVA.9.3.2 Evaluating multiple linear regression models.8.6.3 How to remedy poor regressions: transformations.8.5.4 Making predictions with a regression model.8.5.3 Inference for regression coefficients.8.5.2 Coefficient of determination (R-squared).8.5.1 Partitioning the variability in regression.7.3.2 Calculating power of statistical tests.6.3 The chi-squared test for two-way tables.5.4 Two-sample hypothesis tests for proportions.5.3 Sampling distribution and one-sample hypothesis test for a proportion.5.2 The normal approximation for a binomial distribution.4.2 Statistical significance and hypothesis tests.4 Hypothesis tests for means and variances.3.5 Continuous probabilities: the normal distribution.3.4 Discrete probabilities: the Bernoulli and binomial distributions.3.2.5 Rule 5: If one event happening does not influence another, the events are independent.3.2.4 Rule 4: The complement of a probability is measured when it does not occur.3.2.3 Rule 3: If two events are mutually exclusive, apply the addition rule.3.2.2 Rule 2: All probabilities sum to one.3.2.1 Rule 1: Probabilities are between 0 and 1.2.3.2 Sampling distribution of the mean.1.4.2 Adjusting plot layouts and themes. ![]()
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