# analysis of variance

• [46] Power analysis Power analysis is often applied in the context of ANOVA in order to assess the probability of successfully rejecting the null hypothesis if we assume
a certain ANOVA design, effect size in the population, sample size and significance level.

• Fixed-effects models Main article: Fixed effects model The fixed-effects model (class I) of analysis of variance applies to situations in which the experimenter applies
one or more treatments to the subjects of the experiment to see whether the response variable values change.

• There are two methods of concluding the ANOVA hypothesis test, both of which produce the same result: • The textbook method is to compare the observed value of F with the
critical value of F determined from tables.

• However, while standardized effect sizes are commonly used in much of the professional literature, a non-standardized measure of effect size that has immediately “meaningful”
units may be preferable for reporting purposes.

• However, studies of processes that change variances rather than means (called dispersion effects) have been successfully conducted using ANOVA.

• Comparisons can also look at tests of trend, such as linear and quadratic relationships, when the independent variable involves ordered levels.

• [44] Associated analysis Some analysis is required in support of the design of the experiment while other analysis is performed after changes in the factors are formally found
to produce statistically significant changes in the responses.

• : Welch’s heteroscedastic F test, Welch’s heteroscedastic F test with trimmed means and Winsorized variances, Brown-Forsythe test, Alexander-Govern test, James second order
test and Kruskal-Wallis test, available in onewaytests R It is useful to represent each data point in the following form, called a statistical model: where That is, we envision an additive model that says every data point can be represented
by summing three quantities: the true mean, averaged over all factor levels being investigated, plus an incremental component associated with the particular column (factor level), plus a final component associated with everything else affecting
that specific data value.

• [12] Example The analysis of variance can be used to describe otherwise complex relations among variables.

• 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 means.

• [14] Assumptions The analysis of variance has been studied from several approaches, the most common of which uses a linear model that relates the response to the treatments
and blocks.

• In its simplest form, ANOVA provides a statistical test of whether two or more population means are equal, and therefore generalizes the t-test beyond two means.

• Later experiments are often designed to test a hypothesis that a treatment effect has an important magnitude; in this case, the number of experimental units is chosen so that
the experiment is within budget and has adequate power, among other goals.

• [9] His first application of the analysis of variance to data analysis was published in 1921, Studies in Crop Variation I,[10] This divided the variation of a time series
into components representing annual causes and slow deterioration.

• Textbook analysis using a normal distribution The analysis of variance can be presented in terms of a linear model, which makes the following assumptions about the probability
distribution of the responses:[15][16][17][18] • Independence of observations – this is an assumption of the model that simplifies the statistical analysis.

• These include graphical methods based on limiting the probability of false negative errors, graphical methods based on an expected variation increase (above the residuals)
and methods based on achieving a desired confidence interval.

• The number of degrees of freedom DF can be partitioned in a similar way: one of these components (that for error) specifies a chi-squared distribution which describes the
associated sum of squares, while the same is true for “treatments” if there is no treatment effect.

• “[58] In the general case, “The analysis of variance can also be applied to unbalanced data, but then the sums of squares, mean squares, and F-ratios will depend on the order
in which the sources of variation are considered.

• For multiple factors Main article: Two-way analysis of variance ANOVA generalizes to the study of the effects of multiple factors.

• Power analysis can assist in study design by determining what sample size would be required in order to have a reasonable chance of rejecting the null hypothesis when the
alternative hypothesis is true.

• For example, to test the hypothesis that various medical treatments have exactly the same effect, the F-test’s p-values closely approximate the permutation test’s p-values:
The approximation is particularly close when the design is balanced.

• For example, the model for a simplified ANOVA with one type of treatment at different levels.

• The separate assumptions of the textbook model imply that the errors are independently, identically, and normally distributed for fixed effects models, that is, that the errors
() are independent and Randomization-based analysis See also: Random assignment and Randomization test In a randomized controlled experiment, the treatments are randomly assigned to experimental units, following the experimental protocol.

• [52] Residuals should have the appearance of (zero mean normal distribution) noise when plotted as a function of anything including time and modeled data values.

• [47][48][49][50] Effect size Effect size Main article: Effect size Several standardized measures of effect have been proposed for ANOVA to summarize the strength of
the association between a predictor(s) and the dependent variable or the overall standardized difference of the complete model.

• [5] The experimental methods used in the study of the personal equation were later accepted by the emerging field of psychology [6] which developed strong (full factorial)
experimental methods to which randomization and blinding were soon added.

• Besides the power analysis, there are less formal methods for selecting the number of experimental units.

• So ANOVA statistical significance result is independent of constant bias and scaling errors as well as the units used in expressing observations.

• Since the randomization-based analysis is complicated and is closely approximated by the approach using a normal linear model, most teachers emphasize the normal linear model
approach.

• Early experiments are often designed to provide mean-unbiased estimates of treatment effects and of experimental error.

• The method has some advantages over correlation: not all of the data must be numeric and one result of the method is a judgment in the confidence in an explanatory relationship.

• For example, the randomization-based analysis results in a small but (strictly) negative correlation between the observations.

• [40] Caution is advised when encountering interactions; Test interaction terms first and expand the analysis beyond ANOVA if interactions are found.

• Follow-up tests to identify which specific groups, variables, or factors have statistically different means include the Tukey’s range test, and Duncan’s new multiple range
test.

• If the response variable is expected to follow a parametric family of probability distributions, then the statistician may specify (in the protocol for the experiment or observational
study) that the responses be transformed to stabilize the variance.

• [13] Mixed-effects models Main article: Mixed model A mixed-effects model (class III) contains experimental factors of both fixed and random-effects types, with appropriately
different interpretations and analysis for the two types.

• [54] Some popular designs use the following types of ANOVA: • One-way ANOVA is used to test for differences among two or more independent groups (means), e.g.

• “[39] “[W]e think of the analysis of variance as a way of understanding and structuring multilevel models—not as an alternative to regression but as a tool for summarizing
complex high-dimensional inferences …”[39] For a single factor The simplest experiment suitable for ANOVA analysis is the completely randomized experiment with a single factor.

• Regression is first used to fit more complex models to data, then ANOVA is used to compare models with the objective of selecting simple(r) models that adequately describe
the data.

• The treatment variance is based on the deviations of treatment means from the grand mean, the result being multiplied by the number of observations in each treatment to account
for the difference between the variance of observations and the variance of means.

• This ratio is independent of several possible alterations to the experimental observations: Adding a constant to all observations does not alter significance.

• In order to obtain a fully general -way interaction ANOVA we must also concatenate every additional interaction term in the vector and then add an intercept term.

• Two apparent experimental methods of increasing F are increasing the sample size and reducing the error variance by tight experimental controls.

• The random-effects model would determine whether important differences exist among a list of randomly selected texts.

• Often the follow-up tests incorporate a method of adjusting for the multiple comparisons problem.

• It is also common to apply ANOVA to observational data using an appropriate statistical model.

• • Repeated measures ANOVA is used when the same subjects are used for each factor (e.g., in a longitudinal study).

• [29] For observational data, the derivation of confidence intervals must use subjective models, as emphasized by Ronald Fisher and his followers.

• A common use of the method is the analysis of experimental data or the development of models.

• [citation needed] Characteristics ANOVA is used in the analysis of comparative experiments, those in which only the difference in outcomes is of interest.

• Cautions Balanced experiments (those with an equal sample size for each treatment) are relatively easy to interpret; unbalanced experiments offer more complexity.

• In turn, these tests are often followed with a Compact Letter Display (CLD) methodology in order to render the output of the mentioned tests more transparent to a non-statistician
audience.

• Note that the model is linear in parameters but may be nonlinear across factor levels.

• The fundamental technique is a partitioning of the total sum of squares SS into components related to the effects used in the model.

• This allows the experimenter to estimate the ranges of response variable values that the treatment would generate in the population as a whole.

• For example, in one-way, or single-factor ANOVA, statistical significance is tested for by comparing the F test statistic where MS is mean square, is the number of treatments
and is the total number of cases to the F-distribution with being the numerator degrees of freedom and the denominator degrees of freedom.

• There are some alternatives to conventional one-way analysis of variance, e.g.

• [4] Before 1800, astronomers had isolated observational errors resulting from reaction times (the “personal equation”) and had developed methods of reducing the errors.

• The fixed-effects model would compare a list of candidate texts.

• “Provide information on sample size and the process that led to sample size decisions.

• Linearly re-order the data so that -th observation is associated with a response and factors where denotes the different factors and is the total number of factors.

• Many statisticians base ANOVA on the design of the experiment,[53] especially on the protocol that specifies the random assignment of treatments to subjects; the protocol’s
description of the assignment mechanism should include a specification of the structure of the treatments and of any blocking.

• [36] Partitioning of the sum of squares Main article: Partition of sums of squares One-factor ANOVA table showing example output data See also: Lack-of-fit sum of squares

• “Such models could be fit without any reference to ANOVA, but ANOVA tools could then be used to make some sense of the fitted models, and to test hypotheses about batches
of coefficients.

• Because the levels themselves are random variables, some assumptions and the method of contrasting the treatments (a multi-variable generalization of simple differences) differ
from the fixed-effects model.

• [25] The test statistics of this derived linear model are closely approximated by the test statistics of an appropriate normal linear model, according to approximation theorems
and simulation studies.

• Reporting sample size analysis is generally required in psychology.

Works Cited

[‘Unit-treatment additivity is simply termed additivity in most texts. Hinkelmann and Kempthorne add adjectives and distinguish between additivity in the strict and broad senses. This allows a detailed consideration of multiple error sources (treatment,
state, selection, measurement and sampling) on page 161.
1. ^ Rosenbaum (2002, page 40) cites Section 5.7 (Permutation Tests), Theorem 2.3 (actually Theorem 3, page 184) of Lehmann’s Testing Statistical Hypotheses (1959).
2. ^ The F-test for
the comparison of variances has a mixed reputation. It is not recommended as a hypothesis test to determine whether two different samples have the same variance. It is recommended for ANOVA where two estimates of the variance of the same sample are
compared. While the F-test is not generally robust against departures from normality, it has been found to be robust in the special case of ANOVA. Citations from Moore & McCabe (2003): “Analysis of variance uses F statistics, but these are not the
same as the F statistic for comparing two population standard deviations.” (page 554) “The F test and other procedures for inference about variances are so lacking in robustness as to be of little use in practice.” (page 556) “[The ANOVA F-test] is
relatively insensitive to moderate nonnormality and unequal variances, especially when the sample sizes are similar.” (page 763) ANOVA assumes homoscedasticity, but it is robust. The statistical test for homoscedasticity (the F-test) is not robust.
Moore & McCabe recommend a rule of thumb.
3. Stigler (1986)
4. ^ Stigler (1986, p 134)
5. ^ Stigler (1986, p 153)
6. ^ Stigler (1986, pp 154–155)
7. ^ Stigler (1986, pp 240–242)
8. ^ Stigler (1986, Chapter 7 – Psychophysics as a Counterpoint)
9. ^
Stigler (1986, p 253)
10. ^ Stigler (1986, pp 314–315)
11. ^ The Correlation Between Relatives on the Supposition of Mendelian Inheritance. Ronald A. Fisher. Philosophical Transactions of the Royal Society of Edinburgh. 1918. (volume 52, pages
399–433)
12. ^ Fisher, Ronald A. (1921). “) Studies in Crop Variation. I. An Examination of the Yield of Dressed Grain from Broadbalk”. Journal of Agricultural Science. 11 (2): 107–135. doi:10.1017/S0021859600003750. hdl:2440/15170. S2CID 86029217.
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Fisher, Ronald A. (1923). “) Studies in Crop Variation. II. The Manurial Response of Different Potato Varieties”. Journal of Agricultural Science. 13 (3): 311–320. doi:10.1017/S0021859600003592. hdl:2440/15179. S2CID 85985907.
14. ^ Scheffé (1959,
p 291, “Randomization models were first formulated by Neyman (1923) for the completely randomized design, by Neyman (1935) for randomized blocks, by Welch (1937) and Pitman (1937) for the Latin square under a certain null hypothesis, and by Kempthorne
(1952, 1955) and Wilk (1955) for many other designs.”)
15. ^ Montgomery (2001, Chapter 12: Experiments with random factors)
16. ^ Gelman (2005, pp. 20–21)
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23. ^ Cox, D. R. (1992). Planning
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24. ^ Kempthorne (1979, p 30)
25. ^ Jump up to:a b Cox (1958, Chapter 2: Some Key Assumptions)
26. ^ Hinkelmann and Kempthorne (2008, Volume 1, Throughout. Introduced in Section 2.3.3: Principles
of experimental design; The linear model; Outline of a model)
27. ^ Hinkelmann and Kempthorne (2008, Volume 1, Section 6.3: Completely Randomized Design; Derived Linear Model)
28. ^ Jump up to:a b Hinkelmann and Kempthorne (2008, Volume 1, Section
6.6: Completely randomized design; Approximating the randomization test)
29. ^ Bailey (2008, Chapter 2.14 “A More General Model” in Bailey, pp. 38–40)
30. ^ Hinkelmann and Kempthorne (2008, Volume 1, Chapter 7: Comparison of Treatments)
31. ^
Kempthorne (1979, pp 125–126, “The experimenter must decide which of the various causes that he feels will produce variations in his results must be controlled experimentally. Those causes that he does not control experimentally, because he is not
cognizant of them, he must control by the device of randomization.” “[O]nly when the treatments in the experiment are applied by the experimenter using the full randomization procedure is the chain of inductive inference sound. It is only under these
circumstances that the experimenter can attribute whatever effects he observes to the treatment and the treatment only. Under these circumstances his conclusions are reliable in the statistical sense.”)
32. ^ Freedman[full citation needed]
33. ^
Montgomery (2001, Section 3.8: Discovering dispersion effects)
34. ^ Hinkelmann and Kempthorne (2008, Volume 1, Section 6.10: Completely randomized design; Transformations)
35. ^ Bailey (2008)
36. ^ Montgomery (2001, Section 3-3: Experiments
with a single factor: The analysis of variance; Analysis of the fixed effects model)
37. ^ Cochran & Cox (1992, p 2 example)
38. ^ Cochran & Cox (1992, p 49)
39. ^ Hinkelmann and Kempthorne (2008, Volume 1, Section 6.7: Completely randomized
design; CRD with unequal numbers of replications)
40. ^ Moore and McCabe (2003, page 763)
41. ^ Jump up to:a b c Gelman (2008)
42. ^ Jump up to:a b Montgomery (2001, Section 5-2: Introduction to factorial designs; The advantages of factorials)
43. ^
Belle (2008, Section 8.4: High-order interactions occur rarely)
44. ^ Montgomery (2001, Section 5-1: Introduction to factorial designs; Basic definitions and principles)
45. ^ Cox (1958, Chapter 6: Basic ideas about factorial experiments)
46. ^
Montgomery (2001, Section 5-3.7: Introduction to factorial designs; The two-factor factorial design; One observation per cell)
47. ^ Wilkinson (1999, p 596)
48. ^ Montgomery (2001, Section 3-7: Determining sample size)
49. ^ Howell (2002, Chapter
8: Power)
50. ^ Howell (2002, Section 11.12: Power (in ANOVA))
51. ^ Howell (2002, Section 13.7: Power analysis for factorial experiments)
52. ^ Moore and McCabe (2003, pp 778–780)
53. ^ Jump up to:a b Wilkinson (1999, p 599)
54. ^ Montgomery
(2001, Section 3-4: Model adequacy checking)
55. ^ Cochran & Cox (1957, p 9, “The general rule [is] that the way in which the experiment is conducted determines not only whether inferences can be made, but also the calculations required to make
them.”)
56. ^ “ANOVA Design”. bluebox.creighton.edu. Retrieved 23 January 2023.
57. ^ “One-way/single factor ANOVA”. Archived from the original on 7 November 2014.
58. ^ “The Probable Error of a Mean” (PDF). Biometrika. 6: 1–25. 1908. doi:10.1093/biomet/6.1.1.
hdl:10338.dmlcz/143545.
59. ^ Montgomery (2001, Section 3-3.4: Unbalanced data)
60. ^ Montgomery (2001, Section 14-2: Unbalanced data in factorial design)
61. ^ Gelman (2005, p.1) (with qualification in the later text)
62. ^ Montgomery (2001,
Section 3.9: The Regression Approach to the Analysis of Variance)
63. ^ Howell (2002, p 604)
64. ^ Howell (2002, Chapter 18: Resampling and nonparametric approaches to data)
65. ^ Montgomery (2001, Section 3-10: Nonparametric methods in the
analysis of variance)
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