Perhaps the most fundamental type of R analysis is linear regression. Linear regression can be used for two closely related, but slightly different purposes. You can use linear regression to predict ...

The major outputs you need to be concerned about for simple linear regression are the R-squared, the intercept (constant) and the GDP's beta (b) coefficient. The R-squared number in this example ...

Learn how to graph linear regression in Excel. ... In contrast to the R 2 value, a smaller p-value is favorable as it indicates a correlation between the dependent and independent variables.

xkcd #2048 is exceptionally relevant to this. Doing linear regression well with a big dataset is difficult! I do this all the time at work and honestly I often show a scatter plot without any ...

A regression equation with a zillion dummy variables in it is hard to read and has little generalizable business value. For example, instead of having a factor “city” with many different levels/values ...

This post will show how to estimate and interpret linear regression models with survey data using R. We’ll use data taken from a Pew Research Center 2016 post-election survey, and you can download the ...

The next time you cover this topic, consider teaching students how to perform a simple linear regression analysis in Excel. Below is an example screenshot illustrating 12 months of cost data for a ...

Linear regression forecasting is a time-series method that uses basic statistics to project future values for a target variable. Forecasting Methods The two main categories of forecasting take ...

The diagram in Figure 2 gives you a rough idea of support vector regression for a scenario where there is just one predictor variable x. Each dot is a training data item. The red line is the linear ...

Specialization: Statistical Modeling for Data Science Applications Instructor: Brian Zaharatos, Director, Professional Master’s Degree in Applied Mathematics Prior knowledge needed: Basic calculus ...