1. Manage data, including data manipulation and cleaning using STATA

2. Perform descriptive analysis and Perform Relation Analysis

3. Conduct modeling using STATA

4. Learn the basics of R

5. Learn data manipulation and organization using R

6. Perform relation analysis and interpretation of the results using R

7. Perform statistical modelling and interpretation of the results using R

Session 1:Introduction to STATA

• Key components of STATA and STATA syntax

• STATA commands and do-file

• • Opening and clearing a database

• • Compressing databases

• • Changing the working directory

Session 2: Data management

• Data description, code-book, inspect and summarize

• View, edit and label variables

• Merge datasets/compare datasets/Transpose a dataset

• • Create and replace variables

• • Rename/Recode and drop variables

• • Replace/Fill in missing values

• • Destring and To string variables

Session 3: Descriptive statistics

• Descriptive statistics for nominal and ordinal variables

• Summary statistics for Interval and Ratio variables

• Tabulation and tables

• Correlations, covariances and confidence intervals

• Graphics and data visualization

Session 4: Analyzing relationships between variables

• One and two sample t-tests

• Analysis of variance (ANOVA)

• Hypothesis testing

Session 5: Regression Analysis

• Scatter plots

• Correlation analysis

• Simple and multiple linear regression analysis

• Ordinary Least Squares analysis

• Interpretation of the results

Session 6: Modeling in STATA

• Probit and Logit models and their variations

• Poisson and Binomial models

• Linear probability model

• Marginal Effects

 Data analysis and modelling using R and R Studio

Session 1: Introduction to R and R studio

• Installing R studio and its packages

• Key components of R and Core programming principles

• Importing data into R

• Exploring your dataset using R

Session 2: Descriptive statistics

• Central tendencies: mean, median and mode

• Variance, standard deviation, quantiles and quartiles

• Graphics and Data visualization

Session 3: Relationship/Association Analysis

• Correlation analysis

• Regression analysis

• Simple linear regression analysis

• Multiple regression analysis

• Ordinary Least Squares (OLS)

• Analysis of variance (ANOVA)

Session 4: Probability and Hypothesis testing

• One sample t-test

• Two sample t-test

• Paired samples t-test

Session 5: Statistical modelling

• Logit model and its variations

• Probit model and its variations

• OLS regression model

• Marginal effects and their interpretation