Regressions are used to develop the base case forecast demand curves used in the residential, commercial, industrial and transportation sectors. The desired general form for the equations is sensitivity to population, GDP, labor force, weather conditions, etc. For those states which have experienced a substantial structural change in their gas market, dummy variables are used to shift the curves up or down at the time such a change occurs.

For the Residential and Commercial sectors, dummy variables were used where it made sense to do so, improved the regression fit, and a non-routine underlying event identified that precipitated a change in demand. A pure trend variable is also used to reflect efficiency and behavioral changes.

Simple Linear Regression

In statistics, simple linear regression is a linear regression model with a single explanatory variable. That is, it concerns two-dimensional sample points with one independent variable and one dependent variable Linear regression models are used to show or predict the relationship between two variables or factors.

Reference:
https://en.wikipedia.org/wiki/Simple_linear_regression
https://www.thebalancesmb.com/what-is-simple-linear-regression-2296697

Log-log Model

Using natural logs for variables on both sides of econometric specification is called a log-log model. This model is handy when the relationship is nonlinear in parameters, because the log transformation generates the desired linearity in parameters.

Reference:
https://www.dummies.com/education/economics/econometrics/econometrics-and-the-log-log-model/
Log-Log Regression & Interpretation (What do the Regression Coefficient Estimate Results Mean?https://www.youtube.com/watch?v=NZCSt9WkpkI

Main Explanatory Variables

*GDP, PPP – https://www.imf.org/external/pubs/ft/weo/2020/01/weodata/weoselgr.aspx
*Manufacturing GDP, value added (%) – https://data.worldbank.org/indicator/NV.IND.MANF.ZS
*Population – https://data.un.org/Data.aspx?q=population&d=PopDiv&f=variableID%3a12