{"id":18495,"date":"2021-11-18T19:14:00","date_gmt":"2021-11-18T13:44:00","guid":{"rendered":"https:\/\/www.mygreatlearning.com\/blog\/introduction-to-multivariate-regression\/"},"modified":"2024-09-02T15:24:22","modified_gmt":"2024-09-02T09:54:22","slug":"introduction-to-multivariate-regression","status":"publish","type":"post","link":"https:\/\/www.mygreatlearning.com\/blog\/introduction-to-multivariate-regression\/","title":{"rendered":"Introduction to Multivariate Regression Analysis"},"content":{"rendered":"\n

Contributed by: Pooja Korwar <\/a><\/em><\/p>\n\n\n\n

What is Multivariate Regression?<\/strong><\/h2>\n\n\n\n

Multivariate regression shows the linear relationship between more than one predictor or independent variable and more than one output or dependent variable. <\/p>\n\n\n\n

In comparison, simple regression refers to the linear relationship between one predictor and the output variable.<\/p>\n\n\n\n

It is a supervised machine learning<\/a> algorithm involving multiple data variables for analyzing and predicting the output based on various independent variables.<\/p>\n\n\n\n

Multivariate regression tries to find a formula that can explain how variable factors respond simultaneously to changes in others. Follow along to learn more about it through some real-life examples. <\/p>\n\n\n\n

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Examples of Multivariate Regression<\/strong><\/h2>\n\n\n\n

In today\u2019s world, data is everywhere. Data is just facts and figures that provide meaningful information if analyzed properly. <\/p>\n\n\n\n

Hence, data analysis is essential. Data analysis applies statistical analysis<\/a> and logical techniques to describe, visualize, reduce, revise, summarize, and assess data into useful information that provides a better context for the data. <\/p>\n\n\n\n

Multivariate regression has numerous applications in various areas. Let\u2019s look at some examples to understand it better.<\/p>\n\n\n\n

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  1. Praneeta wants to estimate the price of a house. She will collect details such as the location, number of bedrooms, size of square feet, amenities available, etc. Based on these details, she can predict the price of the house and how each variable is interrelated.<\/li>\n<\/ol>\n\n\n\n
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    1. An agriculture scientist wants to predict the total crop yield expected for the summer. He collected details of the expected amount of rainfall, fertilizers to use, and soil conditions.By building a Multivariate regression model, scientists can predict crop yield. With the crop yield, the scientist also tries to understand the relationship among the variables.<\/li>\n<\/ol>\n\n\n\n
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      1. Suppose an organization wants to know how much it has to pay a new hire. In that case, it will consider details such as education level, years of experience, job location, and whether the employee has niche skills. Based on this information, you can predict an employee\u2019s salary, and these variables help estimate the salary.<\/li>\n<\/ol>\n\n\n\n
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        1. Economists can use Multivariate regression to predict the GDP growth of a state or a country based on parameters such as the total amount spent by consumers, import expenditures, total gains from exports, and total savings.<\/li>\n<\/ol>\n\n\n\n
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          1. A company wants to predict an apartment\u2019s electricity bill. The details needed here are the number of flats, the number of appliances used, the number of people at home, etc. These variables can help predict the electricity bill.<\/li>\n<\/ol>\n\n\n\n

            The above example uses multivariate regression with many independent and single-dependent variables.<\/p>\n\n\n\n

            Check out the Statistical Analysis course<\/a> to learn the statistical methods involved in data analysis.<\/p>\n\n\n\n

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