Statistical analysis techniques refer to a variety of methods used to analyze and interpret data in order to draw meaningful conclusions, identify patterns, make predictions, and test hypotheses.
Some statistical analysis techniques:
Descriptive Statistics: Summarize the main characteristics of a data set. Examples: mean, variance, standard deviation.
Inferential Statistics: Generalize a larger population based on a sample of data. Examples: confidence intervals, t-tests, analysis of variance, regression analysis, and chi-square tests.
Regression Analysis: Examine the relationship between a dependent variable and one or more independent variables. Examples: linear regression, multiple regression, logistic regression, and polynomial regression.
Time Series Analysis: Study patterns, trends, and seasonality in data. Examples: moving averages, exponential smoothing, ARIMA (autoregressive integrated moving average) models, and trend analysis.
Factor Analysis: Identify underlying factors or latent variables that explain the correlations among observed variables.
Cluster Analysis: Identify groups or clusters within a data set based on similarities or dissimilarities among observations. Examples: k-means clustering, hierarchical clustering, and DBSCAN (Density-Based Spatial Clustering of Applications with Noise).
Data Mining: Discover patterns, relationships, and insights in large and complex data sets. Example: decision trees, random forests, support vector machines, and neural networks.