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Simple Linear Regression

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Brief Description: Linear Regression is the oldest, simple and widely used supervised machine learning algorithm for predictive analysis.In a data set we can characterize features or variables as either quantitative or qualitative (also known as categorical). Quantitative variables are nothing but numerical values like a person’s weight or temperature of a city and qualitative variables are values in one of ’n’ different classes, or categories like gender (male or female), different blog categories (technical, cooking, fashion etc.,). We tend to refer to problems with a quantitative response as regression problems. The response variable here is referred to as target or dependent variable and the other independent variables are predictors.

Linear regression is used for finding linear relationship between target and one or more predictors. There are two types of linear regression- Simple and Multiple. In Simple linear regression we find the relationship between a dependent Y and independent variable X, the mathematical equation that approximates linear relationship between X and Y is

The following can be some of the best applications of Linear Regression:

1. If a company’s sales have increased steadily every month for the past few years, by conducting a linear analysis on the sales data with monthly sales, the company could forecast sales in future months.
2. Linear regression can also be used to analyze the marketing effectiveness, pricing and promotions on sales of a product.
3. Linear regressions can be used in business to evaluate trends and make estimates or forecasts.
4. If a company, wants to know if the funds that they have invested in marketing a particular brand has given them substantial return on investment, they can use linear regression.
5. Supposing two campaigns are run on TV and Radio in parallel, a linear regression can capture the isolated as well as the combined impact of running this ad together.
6. What will be my monthly spending for next year?
7. How monthly income and trips per month are correlated with monthly spending?