Regression analysis is a powerful tool in explaining and predicting the behavior of a particular variable of interest (dependent variable) given certain conditions as represented by explanatory variables (independent variables).  Regression analysis is appropriate for data from surveys though with some modifications, it is also used to study time series. Based on period of collection, statistical data may be classified into two types: cross-sectional and time series. The former are gathered in a single period, while the latter are collected over equally-spaced time periods.

This course will deal on coming up with of regression models (linear regression) from cross-sectional data sets and even for time series data which is useful in immediate planning and policy needs of various stakeholders.  During the hands-on sessions, participants will have opportunities to analyze real data using the software EVIEWS.

What will participants gain?

Be able to organize and manage data for regression analysis; apply the different procedures in regression analysis using EVIEWS; estimate simple equation models (linear regression) and conduct time series analysis; and perform diagnostic checking and troubleshooting to select the best regression model estimates that are in accordance with scientific standards.

Who can participate?

Tailored for technical staff whose work requires examination of data sets for structural relationships, and likewise predict behavior of the particular variable of interest. The course is also intended for other technical staff involved in preparing forecasts, economic research, policy analysis and other related functions. Participants are expected to have background in statistics and computational skills (knowledge of MS Excel).

Course Coverage

The Linear Regression Model
The Model Building Process
Example of Building a Linear Regression Model
Indicator Variables, Dummy Variables and Interaction Variables
Standardization of Regression Coefficients
Diagnostic Checking and Remedial Measures
Model Selection and Measures to Evaluate Models
Key Concepts on Time Series Analysis
The ARIMA Approach
Stochastic Processes, Auto-correlation and Partial Auto-correlation Functions (ACFs and PACFs)
Diagnostic Checking and Remedial Measures
Regression Using Time Series Data

Course Duration: 5 days

Registration:

To register and further inquiry, please contact the Training Division at Telefax Nos. (632) 436-1426/929-7543 or email it to japebenito@srtc.gov.ph or cemojica@srtc.gov.ph.

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