Analysis of Variance also termed as ANOVA. It is procedure followed by statisticans to check the potential difference between scale-level dependent variable by a nominal-level variable having two or more categories. It was developed by Ronald Fisher in 1918 and it extends t-test and z-test which compares only nominal level variable to have just two categories.

## Types of ANOVA

ANOVAs are majorly of three types:

**One-way ANOVA**– One-way ANOVA have only one independent variable and refers to numbers in this variable. For example, to assess differences in IQ by country, you can have 1, 2, and more countries data to compare.**Two-way ANOVA**– Two way ANOVA uses two independent variables. For example, to access differences in IQ by country (variable 1) and gender(variable 2). Here you can examine the interaction between two independent variables. Such Interactions may indicate that differences in IQ is not uniform across a independent variable. For examples females may have higher IQ score over males and have very high score over males in Europe than in America.Two-way ANOVAs are also termed as factorial ANOVA and can be balanced as well as unbalanced. Balanced refers to having same number of participants in each group where as unbalanced refers to having different number of participants in each group. Following special kind of ANOVAs can be used to handle unbalanced groups.

**Hierarchical approach(Type 1)**-If data was not intentionaly unbalanced and has some type of hierarchy between the factors.**Classical experimental approach(Type 2)**– If data was not intentionaly unbalanced and has no hierarchy between the factors.**Full Regression approach(Type 3)**– If data was intentionaly unbalanced because of population.

**N-way or Multivariate ANOVA**– N-way ANOVA have multiple independent variables. For example, to assess differences in IQ by country, gender, age etc. simultaneously, N-way ANOVA is to be deployed.

## ANOVA Test Procedure

Following are the general steps to carry out ANOVA.

- Setup null and alternative hypothesis where null hypothesis states that there is no significant difference among the groups. And alternative hypothesis assumes that there is a significant difference among the groups.
- Calculate F-ratio and probability of F.
- Compare p-value of the F-ratio with the established alpha or significance level.
- If p-value of F is less than 0.5 then reject the null hypothesis.
- If null hypothesis is rejected, conclude that mean of groups are not equal.

Table of Contents

1.statistics adjusted rsquared

2.statistics analysis of variance

4.statistics arithmetic median

8.statistics best point estimation

9.statistics beta distribution

10.statistics binomial distribution

11.statistics blackscholes model

13.statistics central limit theorem

14.statistics chebyshevs theorem

15.statistics chisquared distribution

16.statistics chi squared table

17.statistics circular permutation

18.statistics cluster sampling

19.statistics cohens kappa coefficient

21.statistics combination with replacement

23.statistics continuous uniform distribution

24.statistics cumulative frequency

25.statistics coefficient of variation

26.statistics correlation coefficient

27.statistics cumulative plots

28.statistics cumulative poisson distribution

30.statistics data collection questionaire designing

31.statistics data collection observation

32.statistics data collection case study method

34.statistics deciles statistics

36.statistics exponential distribution

40.statistics frequency distribution

41.statistics gamma distribution

43.statistics geometric probability distribution

46.statistics gumbel distribution

49.statistics harmonic resonance frequency

51.statistics hypergeometric distribution

52.statistics hypothesis testing

53.statistics interval estimation

54.statistics inverse gamma distribution

55.statistics kolmogorov smirnov test

57.statistics laplace distribution

58.statistics linear regression

59.statistics log gamma distribution

60.statistics logistic regression

63.statistics means difference

64.statistics multinomial distribution

65.statistics negative binomial distribution

66.statistics normal distribution

67.statistics odd and even permutation

68.statistics one proportion z test

69.statistics outlier function

71.statistics permutation with replacement

73.statistics poisson distribution

74.statistics pooled variance r

75.statistics power calculator

77.statistics probability additive theorem

78.statistics probability multiplicative theorem

79.statistics probability bayes theorem

80.statistics probability density function

81.statistics process capability cp amp process performance pp

83.statistics quadratic regression equation

84.statistics qualitative data vs quantitative data

85.statistics quartile deviation

86.statistics range rule of thumb

87.statistics rayleigh distribution

88.statistics regression intercept confidence interval

89.statistics relative standard deviation

90.statistics reliability coefficient

91.statistics required sample size

92.statistics residual analysis

93.statistics residual sum of squares

94.statistics root mean square

96.statistics sampling methods

98.statistics shannon wiener diversity index

99.statistics signal to noise ratio

100.statistics simple random sampling

102.statistics standard deviation

103.statistics standard error se

104.statistics standard normal table

105.statistics statistical significance

108.statistics stem and leaf plot

109.statistics stratified sampling

112.statistics tdistribution table

113.statistics ti 83 exponential regression

114.statistics transformations

116.statistics type i amp ii errors

119.statistics weak law of large numbers