Analysis of Variance and Covariance
Analysis of variance (ANOVA) is a procedure for assigning sample variance to different sources and deciding whether the variation arises within or among different population groups. Samples are described in terms of variation around group means and variation of group means around an overall mean. If variations within groups are small relative to variations between groups, a difference in group means may be inferred. Hypothesis tests are used to quantify decisions.
|Analysis of variance (ANOVA) results|
|One-way analysis of variance|
|Two-way analysis of variance|
|N-way analysis of variance|
|Interactive analysis of covariance|
|Create dummy variables|
|Multiple comparison test|
- One-Way ANOVA
Use one-way ANOVA to determine whether data from several groups (levels) of a single factor have a common mean.
- Two-Way ANOVA
In two-way ANOVA, the effects of two factors on a response variable are of interest.
- N-Way ANOVA
In N-way ANOVA, the effects of N factors on a response variable are of interest.
- ANOVA with Random Effects
ANOVA with random effects is used where a factor's levels represent a random selection from a larger (infinite) set of possible levels.
- Other ANOVA Models
N-way ANOVA can also be used when factors are nested, or when some factors are to be treated as continuous variables.
- Multiple Comparisons
Multiple comparison procedures can accurately determine the significance of differences between multiple group means.
- Analysis of Covariance
Analysis of covariance is a technique for analyzing grouped data having a response (y, the variable to be predicted) and a predictor (x, the variable used to do the prediction).
- Nonparametric Methods
Statistics and Machine Learning Toolbox™ functions include nonparametric versions of one-way and two-way analysis of variance.