ANOVA, Analysis of Variance refers to an analysis tool in statistics separating the observed aggregate variability in the data set. There are two factors in ANOVA, the first one is systematic factors and the second one is random factors. Factors in systematic have influence on the data set. Meanwhile the random factors do not have any influence on the data set. Analysts utilize ANOVA tests to determine whether or not the influence of independent variables occurs in the dependent variable.
In the 20th century statistical analysts used the t and z tests method to come up with the variance method.
Later, ANOVA term is also known for the Fisher analysis of variance. It is actually the further method of t-and z-tests. Fisher wrote a book entitled Statistical Methods for Research Workers. It was actually experimental psychology but the subject became more comprehensive. The key point why people use this method of analysis is because they want to set a step for factor analysis affecting the given data set. When the set completes, the analysts would conduct more tests on the methodical factors. This would measure the contribution to the inconsistency especially in the data set.
Most analysts use ANOVA test results in the form of f-test to create more data aligning to the regression models. Regression here means a statistical method in finance, investing, as well as other disciplines to indicate the relationship between variables. The variables are one dependent variable like in Y and other variables like in the independent variables. However, the technique mostly uses linear regression instead of simple regression.
In the linear regression there would be a linear relationship among two variables on a line of best fit. A line of best fit is a plot line of a data pointing the relationship between one data to the other.
