Regression
Regression is a way to quantify the relationship between two or more variables. It predicts 108.30.20 Data Analysis - Predictive analysis a “dependent variable” from a set of “predictor variables”, which are called the “independent variables”.
Regression is an ideal tool for understanding the drivers of demand and for demand prediction. Particularly good for determining optimal prices.
Linear regression is the most common type. Linear regression draws a linear line down the middle of the plot of independent variables. Say, a plot of Price & Quantity, a regression would draw a line through the middle of the plotted points. Then, given either of the independent variables you can predict a likely value for the other variable. !
Regression models
113.020.010.10 Regression - Regression Models
References: 1. https://www.coursera.org/learn/wharton-customer-analytics/home - Week 3 slides, Iyenger 2.
Graph:
- 113.020.010 Statistics - Regression to 113.020 Math - Statistics
- 113.020.010 Statistics - Regression to 108.30.20 Data Analysis - Predictive analysis
- 113.020.010 Statistics - Regression to 113.020.010.10 Regression - Regression Models
- 113.020 Math - Statistics to 113.020.010 Statistics - Regression
- 113.020.010.10 Regression - Regression Models to 113.020.010 Statistics - Regression