What Ordinary linear regression is
Ordinary linear regression is a statistical method for predicting the value of a dependent variable (e.g. house price) based on the values of one or more independent variables (e.g. square footage). The goal of the regression is to find the linear relationship between the independent and dependent variables.
Steps for Ordinary Linear Regression:
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Collect the data – Gather the data that you need to perform linear regression.
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Examine the data – Look at the data to determine its structure and determine if there are any outliers or patterns that should be taken into account.
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Determine the equation – Calculate the slope and intercept of the line that best fits the data.
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Test the equation – Use statistical tests to determine the reliability and accuracy of the equation.
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Make predictions – Use the equation to make predictions about the dependent variable given the values of the independent variables.
Examples
- Predicting the price of a house based on its size.
- Estimating the number of taxi rides in a city based on the population size.
- Predicting the cost of a hospital stay based on the type of procedure.
- Forecasting the demand for a product based on its price.
- Estimating the cost of car insurance based on the driver’s age and gender.