The red line shown in the chart above tells us the degree by how much you usually sell when it rains a certain amount. This line summarizes the relationship between X and Y. To find out the relationship between the independent variable and dependent variable, draw a line through the chart that runs roughly through the middle of all the data points. how much it has rained in the respective month and what the sales numbers were in the same month. The blue dots in the above diagram represent one month’s data, i.e. sales numbers, and X-axis represents the independent variable of rain. Y-axis represents the dependent variable, i.e. In this example, we are taking rain as a factor to predict the sales numbers. After plotting the data points on a chart, the chart will look like this. Plotting your data is the primary step in determining if there is a relationship between these two variables. Plotting Data on a ChartĪfter you have created the dataset, you will need to plot these data points on a chart. 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. To perform regression analysis, you will need to establish a comprehensive dataset that you will use to work with this regression analysis method. Now that we’ve answered the question of – what is regression analysis – let us understand how it works. 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.īy clicking the above button, you agree to our terms and conditions and our privacy policy. A dependent variable is one that you are trying to predict such as next month’s sales numbers. 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.

Suppose you are a sales manager and you are asked to predict the sales for the next month. Before diving deep into this method, let’s first understand what these two variables are and what their importance is. Regression analysis helps in determining which factors can be ignored, and which factors matter most. 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. Each regression analysis technique has a specific condition where they are best suited to apply. 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. You might be seeing a lot of people talking only about Linear and Logistic regression techniques as these techniques are more popular. There are different types of regression analysis models that you can use. 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. (iv) Assess interaction terms to determine whether the effect of one independent variable depends on the value of another variable (iii) Use polynomial terms to model curvature (ii) Include continuous and categorical variables Regression analysis can handle multiple things such as: Regression analysis can unscramble very intricate problems where the variables are entangled like spaghetti. Making Predictions with Linear Regressionĭid 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.What is Linear Regression in Machine Learning?.Types of Regression Analysis Techniques.Why Should Your Organization Use Regression Analysis?.What is Regression Analysis, and is it a Correlation?.

Simple linear regression equation b0