Linear Regression In Azure ML

Ginger Grant gives a brief discussion of linear regression:

There are two types of indicators for linear correlation, positive and negative as shown on the following charts. The Y axis represents Grades, and the x axis is changed to show positive and negative correlation of the amount of X on grades. When X is the amount of study hours, there is a positive correlation and the line goes up. When X is changed to watching cat videos, there is a negative correlation. If you can’t draw a line around the points there is no correlation. If I were to create a graph where X indicated the quantity of the bags of Cheese Doodles consumed on grades, it would not be possible to draw a straight line, where the data points cluster around it. Since this is Line-ar regression, if that line doesn’t exist there is no correlation. Knowing there is no correlation is also useful.

Simple linear regression is a powerful tool and gets you to “good enough” more frequently than you’d think.

Related Posts

Azure Data Lake Store Gen2

James Serra gives us the low-down on Azure Data Lake Store Gen2 now that it is generally available: When to use Blob vs ADLS Gen2New analytics projects should use ADLS Gen2, and current Blob storage should be converted to ADLS Gen2, unless these are non-analytical use cases that only need object storage rather than hierarchical storage […]

Read More

Conjoint Analysis In R

Abhijit Telang introduces the concept of conjoint analysis and shows how you can implement this in R: We will need to typically transform the problem of utility modeling from its intangible, abstract form to something that is measurable. That is, we wish to assign a numeric value to the perceived utility by the consumer, and […]

Read More


April 2016
« Mar May »