Analysis of variance | Wikipedia audio article
Channel: wikipedia tts
Date: 07/05/2019

This is an audio version of the Wikipedia Article:
https://en.wikipedia.org/wiki/Analysis_of_variance


00:01:10 1 History
00:04:17 2 Motivating example
00:08:15 3 Background and terminology
00:12:15 3.1 Design-of-experiments terms
00:16:09 4 Classes of models
00:16:27 4.1 Fixed-effects models
00:17:04 4.2 Random-effects models
00:17:42 4.3 Mixed-effects models
00:18:59 5 Assumptions
00:19:38 5.1 Textbook analysis using a normal distribution
00:21:01 5.2 Randomization-based analysis
00:22:02 5.2.1 Unit-treatment additivity
00:24:47 5.2.2 Derived linear model
00:26:22 5.2.3 Statistical models for observational data
00:27:16 5.3 Summary of assumptions
00:29:44 6 Characteristics
00:31:10 7 Logic
00:31:54 7.1 Partitioning of the sum of squares
00:35:32 7.2 The iF/i-test
00:40:52 7.3 Extended logic
00:42:11 8 For a single factor
00:43:13 9 For multiple factors
00:46:36 10 Worked numeric examples
00:47:01 11 Associated analysis
00:47:36 11.1 Preparatory analysis
00:47:46 11.1.1 The number of experimental units
00:49:27 11.1.2 Power analysis
00:50:10 11.1.3 Effect size
00:51:00 11.2 Follow-up analysis
00:51:22 11.2.1 Model confirmation
00:52:19 11.2.2 Follow-up tests
00:54:24 12 Study designs
00:56:44 13 Cautions
00:58:48 14 Generalizations
00:59:25 14.1 Connection to linear regression
01:05:49 14.1.1 Example
01:07:33 15 See also
01:08:38 16 Footnotes



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SUMMARY
=======
Analysis of variance (ANOVA) is a collection of statistical models and their associated estimation procedures (such as the "variation" among and between groups) used to analyze the differences among group means in a sample. ANOVA was developed by statistician and evolutionary biologist Ronald Fisher. In the ANOVA setting, the observed variance in a particular variable is partitioned into components attributable to different sources of variation. In its simplest form, ANOVA provides a statistical test of whether the population means of several groups are equal, and therefore generalizes the t-test to more than two groups. ANOVA is useful for comparing (testing) three or more group means for statistical significance.  It is conceptually similar to multiple two-sample t-tests, but is more conservative, resulting in fewer type I errors, and is therefore suited to a wide range of practical problems.