Cluster analysis is a type of multivariate data mining that groups items (like products, respondents, or other entities) based on characteristics or attributes chosen by the user.
It is the first and most crucial step in data mining. It is a common way to look at statistical data and is used in many different areas, such as data compression, machine learning, pattern recognition, and finding information.
Linear Regression Importance. Read on to understand how to Prepare for a Linear Regression Analysis, its advantages and understanding Basic Concepts in Statistics
The ANOVA, or analysis of variance, basically analyses the different estimates by utilising the F-distribution to evaluate if the population means are equal or not.
Box plots show the five-number summary of a set of data: the minimum score, first (lower) quartile, median, third (upper) quartile, and maximum score.
You can summarize categorical data by first sorting the values according to the categories of the variable. Then, placing the count, amount, or percentage of each category into a summary table or into one of several types of charts.
Standardization of variables is the method of placing different variables on an identical scale thereby making it simpler to compare and analyze the data.
If you are from a non-statistical background, it is essentially…
PROC TTEST procedure is used to compare the equality of means for a one sample, two-sample (independent group) or paired t-test.
Hypothesis testing is the statistical process of either retaining a claim or belief made by a person that is usually about population parameters such as mean or proportion and we seek evidence from a sample for the support of the claim.
