GDP or Gross Domestic Product GDP or Gross domestic product refers to total market value of all the final goods and services produced in an economy in a given period of time. For India, this time period is from 1st April to 31st March. This means it measures the value of final goods and services produced within a geographic boundary regardless of the nationality of the individual or firm. For instance, cars manufactured in India by Japanese company will be included in Indian GDP. Similarly, the Jaguar cars manufactured in UK by Tata will not be counted in India’s GDP. It refers to only final output of such goods and services. The rule that only finished or final goods must be counted is necessary to avoid double or triple counting of raw materials, intermediate products, and final products. For example, the value of automobiles already includes the value of the steel, glass, rubber, and other components that have been used to make them. To be precise, we define the following: a) Final Output: Goods and Services purchased for final use. b) Intermediate Goods/Factors of Production/Raw Materials: Products used as input in the production of some other product. There are two ways to take into account double counting: i. Calculate only the value of the final product. ii. Calculate the value added at each stage of production, from the beginning of the process to the end. Specifically, it is derived by subtracting the value of the intermediate good from the value of the sale.
Linear Regression: Linear regression is a statistical regression method which is used for predictive analysis. It is one of the very simple and easy algorithms which works on regression and shows the relationship between the continuous variables. It is used for solving the regression problem in machine learning. Linear regression shows the linear relationship between the independent variable (X-axis) and the dependent variable (Y-axis), hence called linear regression. If there is only one input variable (x), then such linear regression is called simple linear regression . And if there is more than one input variable, then such linear regression is called multiple linear regression . The relationship between variables in the linear regression model can be explained using the below image. Here we are predicting the salary of an employee on the basis of the year of experience . Below is the mathematical equation for Linear regression: Y= aX+b Here,...
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