Dealing with missing values in the data
A common issue data analysts need to deal with is missing values. There are really two issues to deal with: To treat or not to treat the...
The logistic regression model
A logistic regression model commonly referred to as LOGIT, is used to link a dichotomous (binary) dependent variable to one or more...
Binary dependent variables
A variable that can have only two possible values is called a binary, or dichotomous, variable. When a modeler seeks to characterize the...
The world is complex... let's use a model to figure it all out!
Economic models are widely used to forecast economic activity, for planning and resource allocation, risk management, policy formation,...
Data extraction
This posting focuses on the very first step that precedes any analysis of data and on the unique challenges it introduces. Data...
Data cleansing
Data is the key to every statistical analysis. Data cleansing involves removing errors and inconsistencies from data to improve its...
More about econometric modeling and some practical first steps
In 1932, Alfred Cowels – a businessman and economist – founded the Cowels Commission for Research in Economics in Colorado Springs. The...
The Linear Regression Model
The workhorse of regression analysis and one of the most widely used techniques in the data analysis world is the Linear Regression...
