January 22, 2015 by akhilendra

Business Analytic Part 9-Regression Analysis

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Welcome to the chapter 9 of business analytics tutorial where we will start with subjects related to predictive modelling in data science.

In this chapter, we will cover Regression analysis and its variants. We will first go through the definitions and concepts and later cover them in detail with case studies to see their real life implementations.

Regression Analysis

Regression Analysis is the science of identifying and establishing the relationship between variables. Regression analysis deals with identifying the relationship between one dependent variable and one or more independent variables. For example, relationship between a discount offer(price reduction) and sale volumes.

Regression Analysis is performed by the usage of following formulae;

Y = f(X, β)

Y is the dependent variable

X is the independent variable

β is the unknown coefficient

Regression analysis techniques are one of the most widely used techniques in predictive modelling for forecasting purposes.

Regression analysis contains variety of Regression Techniques.

Regression analysis is classified under two categories;

Univariate Regression Analysis
Bivariate Regression Analysis
Multivariate Regression Analysis

Univariate Regression Analysis

It is the simplest form of regression analysis where only one variable is analyzed. It means that analysis of the sample is done with the assumption that only one factor is influencing the result or output. For example, if you are studying the age group distribution of a sample which can be simply plotted on a bar chart to reflect the age group concentration of the sample for a particular occurrence.

Bivariate Regression Analysis

Bivariate is the extension of univariate regression analysis where instead of one, two variables are studied.

Multivariate Regression Analysis

By this time, you must have some idea what are we talking about when we say, multivariate regression analysis. In multivariate analysis, predictions are made based on the relationship or impact of multiple variables or factors. it is widely used in various research fields like;

Quality Assurance
Process Optimization
R&D across sectors

Now we will look into the types of univariate and multivariate data or regression analysis. Univaraite and multivariate are classified into two categories;

Linear Regression Analysis
Non Linear Regression Analysis

In the next chapter we will study linear and non linear analysis which will further help you in understanding regression analysis applications.

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