It is called statistical analysis, which means to describe the data. It consists on using statistical methods to gather, arrange, analyze information and interpret data and present it.
Key Statistical Concepts
Descriptive Statistics : Descriptive statistics describes the basic features of data in a study. It provides simple summaries about the sample and measures including measures of central tendency( mean, median, mode) and measure of dispersion ( variance, standard deviation).
Inferential Statistics : Making inferences about a population from a sample It relies on concepts such as hypothesis testing and confidence intervals.
Probability Theory: The mathematical study of uncertainty and the quantification of uncertain events
Data Visualization: The graphical presentation of data that is used to recognize patterns, trends, and anomalies.
Common Statistical Techniques
Correlation Analysis: The measure of relationship between two variables.
Regression Analysis: Explains the dependent variable in terms of one or more independent variables.
Hypothesis Testing: Tests the validity of a claim (hypothesis)
ANOVA (Analysis of Variance) — Compares the means of three or more groups, to assess whether their population means differ.
Cluster Analysis: This method identifies groups of objects or persons that are similar based on their characteristics.
Factor Analysis: This analysis identifies the underlying residual factor or factors that best account for why certain variables correlate with each other.
Uses of Statistical Analysis Statistical analysis can be applied in various fields, such as:
Business: Market Research, Financial Analysis and Operations Research
Healthcare: Clinical trials, epidemiological studies, analyses of health services and policy
Social Science: Sociology, psychology, economics and political science.
Natural sciences constitute physics, chemistry, biology and environmental science.
Statistical Analysis Tools Several statistical software packages are utilized for data analysis like the following.
SPSS (Statistical Package for the Social Sciences)
R
Python (and the NumPy, pandas, and SciPy libraries)
MATLAB
Stata
Through statistical analysis and the use of suitable tools, researchers as well as analysts can take out meaningful information from data, enabling facts-based conclusions that shall push forward innovation.
