What is Linear Regression?
In statistical modeling, Regression Analysis is a set of statistical processes for estimating the relationships between a dependent variable (often called the ‘outcome variable’) and one or more independent variables (often called ‘predictors’ or ‘features’). The most commonly used regression is LINEAR REGRESSION.
Why to use Linear Regression?
The idea of using linear regression is to examine 2 things:
1. Does a set of predictor variables do a good job in predicting an outcome (dependent) variable?
2 Which variables are significant predictors of the outcome variable, and in what way do they–indicated by the magnitude and sign of the beta estimates–impact the outcome variable?
These regression estimates are usually used to explain the relationship between dependent variable and independent variables. Below is the formula of simplest form of regression equation with one dependent and one independent variable.
y = c + b*x, where y = estimated dependent variable score, x = score on the independent variable, c = constant, b = regression coefficient.
Three major uses of regression analysis:
- Determining the strength of Predictors: It means identifying the strength of the independent variable on the dependent variable. If I explain it with an example, it means is what is the strength of the relationship between advertisement and sales or age and income.
- Predict the effect: In simple words, it simply means how much the dependent variable changed with a change in the independent variable. For e.g, how much sales income(dependent variable) will I get in spending 500$ on marketing(independent variable)?
- Trend forecasting: As its name suggests, it will predict the trend of the final equation or future values. For e.g what will be the price of GOLD in the next 6 months?
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