# Learn to analyse with

STATA is a software package used for statistical data analysis, data management, and graphical representation. It is extensively used by researchers and professionals in the field of social sciences economics, epidemiology, biostatics, and finance.

## Learning outcomes of this STATA module

• The distinction between Regular series, Time Series, Univariate, Multivariate, and Panel Data.
• Establishment of important properties of data like moving average, autoregressive average, first differencing and lags.
• The distinction between Stationary and Non-Stationary Time Series.
• Testing the time series on the basis of Stationarity, Heteroskedasticity, Autocorrelation, and Stability.
• Proposing Multivariate analysis on more than one-time series.
• The distinction between Pooled OLS regression and Panel Data Set regression.
• The choice between Fixed Effect and Random Effect Models.
• Panel level Heteroskedasticity and Autocorrelation.

## Getting started with STATA

The articles in this section discuss different types of data that STATA  handles. Furthermore, it discusses the management of dependent and independent variables and how to import data from other formats.

## Basic statistical tools

This section comprises of articles on basic statistical tools of correlation and regression. Two types of regression namely linear regression and non-linear regression are discussed. Furthermore, how they are analyzed in STATA and how the results can be interpreted.

## Time series analysis

This module section discusses the analysis of continuous data sets that represent time dimensions. Furthermore, it also covers stationarity, normality, and stability in data. This section is designed in a way to introduce the Univariate Time Series and Multivariate Time series. It explores the structural economic model building and critical appraisal of models based on statistical testing methods.

## Panel data analysis

This section is designed in a way to introduce a panel data series and its estimation. Here the articles focus on pooled OLS regression, fixed effect models and random effect models. The issue of choice between fixed effect and random effect and panel level heteroskedasticity and autocorrelation are also covered.