Class schedule: TBA Instructor: Christopher Llones e-mail: christopher.llones@vsu.edu.ph Pre-requisites: Basic statistics or COI Course credits: 3 units
Course description
Application of statistical concept and tools in analyzing economic phenomena.
Course objectives
Perform basic operation using R programming as the main statistical software
Demonstrate how to conduct descriptive analysis using statistical software and check necessary assumptions
Demonstrate how to conduct descriptive analysis using stistical
Conduct parametric and non-parametric tests
Test economic relationship using correlation / regression approach using appropriate statistical software and interpret results
Apply appropriate statistical methods to investigate actual economic problems.
Course outline
Topics
Lessons
Description
Module 1: Introduction to R Programming
Installing R and RStudio
R Basics
Working with R scripts
Importing data
Basic data wrangling
Learn to install and configure R and RStudio.
Understand and apply basic R syntax including data types, vectors, and data frames.
Develop proficiency in writing, saving, and executing R scripts.
Import dataset and prepare them for analysis.
Clean and transform data .
Module 2: Introduction to data visualization using ggplot2
Understanding grammar of graphics
Dataset and mapping
Geometries
Statistical transformation and plotting distribution
Position adjustment and scales
Understand the grammar of graphics and its role in structuring visualizations.
Create and customize basic plots including histograms, bar charts, boxplots, and scatterplot.
Map variables to visual aesthetics such as color, shape, and size to enhance interpretability.
Apply faceting techniques to produce multi-panel plots for comparative analysis.
Modify plot themes and coordinate systems to improve clarity and accessibility.
Export visualizations for use in reports, presentations, policy briefs, and others.
Module 3: Reproducible report with Quarto in R
Introduction to Quarto
Creating Quarto document
Embedding R code
Formatting Outputs
Exporting reports
Learn to create dynamic, reproducible documents using Quarto and markdown syntax.
Embed R code and inline calculations within narrative text to integrate analysis and interpretation.
Format outputs such as tables and plots for professional presentation.
Render reports to multiple formats including HTML, PDF, and Word for diverse audiences.
Develop the ability to produce transparent, replicable research outputs for academic and policy contexts.
Module 4: survey research design
Methods of data collection
Sampling design in surveys
Measurement issues in survey research
Questionnaire construction
Basics of interviewing
Creating a codebook
Data entry
Discuss the various methods of data collection including survey, observation and experimental methods.
Discuss different ways for gathering a sample; random and non-random sampling. Discuss rudimentary formulas for sample size calculation.
Discuss the issues in assigning numbers to represent quantities of attributes. Discuss the various scales of measurement. Discuss criteria in constructing good measurement of variables: reliablity and validity.
Discuss the various advantages and disadvantages of interviews and questionnaire over other methods of data collection.
Discuss the do’s and dont’s of an interviewer’s conduct.
Discuss the importance of creating a codebook for survey data.
Discuss rudimentary of data entry.
Module 5: exploratory data analysis
Rudiments of EDA
Charts and tables
Measures of central tendency
Dispersion, parameters, skewness and kurtosis
Contingency tables and scatter plot
Discuss EDA as the first step in data analysis.
Discuss various techniques in summarizing and visualizing data.
Discuss the various measures of central tendency and data location.
Discuss the relevance of various dispersion, parameters, skewness and kurtosis.
Discuss the relevance of contingency tables and scatterplots for summarizing and visualizing data.
Module 6: test on means
Parametric test on means
Non-parametric test on means
Discuss the various t-tests and ANOVA and perform them on a sample data with R.
Perform various non-parametric equivalent of the t-tests and ANOVA on sample data with R.
Module 7: correlation and regression analysis
Correlation analysis
Review of Regression analysis
Discuss the various types of correlation analysis procedures. Interpret the correlation coefficient.
Discuss the various aspects of regression model building.
Module 8: limited-dependent variable models
Review of binary dependent regression
Extension to the logit model.
Censored and truncated regression models.
Count dependent variable models.
Discuss the various aspects of the logit and probit.
Discuss the multinomial and ordinal logit models.
Discuss the Tobit regression model for censored data and truncated regression models.
Discuss Poisson and Negative Binomial regression models in the regression analysis of count-dependent variable models.
Module 9: multivariate statistical analysis
Cluster analysis
Principal component analysis
Exploratory factor analysis
Confirmatory factor anlysis
Structural equation modelling
Understand and apply different clustering methods. Analyze and evaluate the quality and effectiveness of different clusters in dataset.
Learn to perform and interpret principal component analysis to reduce the dimensionality of dataset. Develop the ability to identify and retain significant components for simplifying data without losing critical information.
Identify and estimate underlying factor structures within a set of observed variables.
Understand model-based factor anlaysis and develop proficiency in evaluating model fit and making necessary adjustments to improve analysis.
Apply SEM techniques to understand relationships among variables and construct theoretical models.