Did you know that Regression analysis is one of the most used statistical analysis? It provides tremendous flexibility, which makes it useful in so many different circumstances. Regression analysis can unscramble very intricate problems where the variables are entangled like spaghetti.
Regression analysis can handle multiple things such as:
(i) Model multiple independent variables
(ii) Include continuous and categorical variables
(iii) Use polynomial terms to model curvature
(iv) Assess interaction terms to determine whether the effect of one independent variable depends on the value of another variable
In this “What is Regression?” – Regression Tutorial, I will cover what regression analysis is, how regression analysis works, why your organization should use regression analysis, and the types of regression analysis.
There are different types of regression analysis models that you can use. You might be seeing a lot of people talking only about Linear and Logistic regression techniques as these techniques are more popular. But this is not completely true. The choice of the regression analysis technique depends on the kind of data you have for the dependent variable and the type of model that provides the best fit. Each regression analysis technique has a specific condition where they are best suited to apply.
What is Regression Analysis?
So, what is regression analysis? Regression analysis is a statistical method that allows you to estimate the relationship between a dependent variable and one or more predictable variables. Regression analysis helps in determining which factors can be ignored, and which factors matter most. Before diving deep into this method, let’s first understand what these two variables are and what their importance is.
Let’s take an example. Suppose you are a sales manager and you are asked to predict the sales for the next month. You will consider a few factors to predict those numbers, such as competitor’s promoting the same product, a new and improved product coming into the market, weather, etc. These factors are called variables. A dependent variable is one that you are trying to predict such as next month’s sales numbers. In contrast, an independent variable is one that you suspect has an impact on your dependent variable such as weather conditions, competitors promoting the same product, etc.
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Now that we’ve answered the question of – what is regression analysis – let us understand how it works.
To perform regression analysis, you will need to establish a comprehensive dataset that you will use to work with this regression analysis method. To create this dataset, you can conduct a survey to your audiences of interest that includes questions addressing all the independent variables that you are interested in.
Step 1. Plotting Data on a Chart
After you have created the dataset, you will need to plot these data points on a chart. Plotting your data is the primary step in determining if there is a relationship between these two variables. After plotting the data points on a chart, the chart will look like this.