INFERENTIAL STATISTICS Infographics

INFERENTIAL STATISTICS

It is one of the two branches of statistics that uses a random sample of data taken from a population to explain and make inferences about the population. They are essential when examination of each individual of the population is not possible. 

INFERENTIAL STATISTICS TESTS

Inferential statistics are the sets of statistical researchers. The major inferential statistics come from a general family of statistics model Known as the General Linear Model. Social Researchers must become familiar with its workings. 

There is a wide range of statistical tests. Inferential Statistics is usually analyzed with simple t-test or one-way ANOVA. If the data is standard, then parametric tests should be used, and if it is not healthy, non-parametric tests should be applied.

Types of Inferential Regression Tests

The following are some types of Inferential Regression tests.

  • Linear Regression Analysis

 Predicts the value of the dependent variable based on the possible benefits of the independent variables. For the case of one explanatory variable, the approach is simple linear regression. And for more than one variable, it is multiple linear regression. 

  • Analysis of Variance (ANOVA)

 ANOVA used to test and analyze the variations between two or more means from the data set. It is a collection of statistical models and developed by statisticians. Analysis of experimental data uses an analysis of variance.

  • Analysis of Co-Variance 

Extension of the Analysis of Variance method that involves the inclusion of a continuous co-variance in the calculations. It is a general linear model and blends ANOVA and regression. It increases statistical power.

  • Statistical Significance

  Statistical Significance used to differentiate the means of two groups and read if they are different from each other. It reflects a real difference in the population from which the groups sampled. 

  • Correlation Analysis

Correlation Analysis used to understand the extent to which two variables are reliant on each other, often called sample. It is a numerical measure of some type of correlation. High correlation means that variables have a strong relationship and weak correlation means they have no relation.