DS 4100 Data Collection, Integration, and Analysis

Today we’re doing more on regression model predictions.


Regression models are a mathematical equation used to predict a value based on empirical observations. The prediction is never correct, but, depending on the “fit of data,” it can be reasonably good.

Regression vs Correlation

Constructing a model

Linear Regression

Alternatively, use the =slope and =intercept functions to calculate the slope and intercept of the regression equation or the =linest function to get slope, intercept, and R2 in an array.

Time-Series Regression

The simplest is a linear (straight line) model developed using regression analysis with time as the independent variable.

R squared

The fit of the regression line is measured by the coefficient of determination – R2. The closer R2 is to 1, the better the regression model fit and the more accurate the prediction. Note that R2 is one part of measuring the “quality” of a regression model: the other is statistical significance.


Model Selection