Regression Analysis

Regression Analysis: The Basics

Regression analysis?

Regression is a statistical technique that helps us understand the relationship between two variables and in this case that relationship is the average value for a set of variables X and Y. This set of variables may be many things from averages of five different races, to the average movie rating across the board for every movie rated as "fresh". What does a regression analysis do? A regression analysis performs a simulation of the relationship between X and Y to determine which variables have the biggest impact on the values of those variables. For instance, if we want to know what is the average for the various races across a wide variety of states in the U.S.

How does regression analysis work?

As mentioned previously, regression analysis is a sophisticated statistical technique for analyzing data. A regression analysis is the creation of a function that is correlated to one of the dependent variables. Let’s say we want to know what are some common tendencies that people tend to exhibit when they are reading. To do this, we will use a variable called “Readability” which is measured by dividing the reading passage (Figure 1) into an unreadable category and an easily understandable category. The Readability variable then divides each item into a variable of importance, A and B, which will denote whether an item is unreadable or easily understandable. Figure 1 To conduct a regression, you must first know what is the importance of each variable.

What are the different types of regression analyses?

Distribution Plot This is the simplest and most common type of regression analysis. The plot includes a scatter plot of the variable, along with the dependent variable, along with some axis labels, and some dependent variables below the axes. The axis labels can either provide information on which variable dominates the dependent variable, or indicate the statistical relationship between two variables. Distributed maximum likelihood analysis This technique is based on the probability theory that all data are drawn from a common distribution. When applying a hypothesis test to data from a multivariate distribution, we can use the multivariate normal distribution as an approximation to the population distribution.

Conclusion

If you’re considering a career in data analysis, you’re in the right place. I believe that if you can master the basics of statistical analysis, you will be able to identify a specific niche in data science. You can then use your abilities to become a successful data scientist. If you’re ready to learn more about statistical analysis and data science, take a look at our free, step-by-step guide to learning statistics.