👉🏻 Download Our Free Data Science Career Guide: [ Ссылка ]

It is time for the actual introduction of regressions! Let's start with some dry theory. A linear regression is a linear approximation of a causal relationship between two or more variables. Regressions models are highly valuable as they are one of the most common ways to make inferences and predictions. The process goes like this: you get sample data, come up with a model that explains the data and then make predictions for the whole population based on the model you have developed. There is a dependent variable labeled Y being predicted and an independent variable labeled x1 x2 and so forth these are the predictors Y is a function of the X variables and the regression model is a linear approximation of this function. The easiest regression model is the simple linear regression y is equal to beta 0 plus beta 1 times X plus Epsilon. Let's see what these values mean. Y is the variable we are trying to predict and is called the dependent. Variable X is the independent variable. When using regression analysis we want to predict the value of y provided. We have the value of x but to have a regression Y must depend on X in some causal way. Whenever there is a change in X such change must translate into a change in Y.

► Consider hitting the SUBSCRIBE button if you LIKE the content: [ Ссылка ]

► VISIT our website: [ Ссылка ]

🤝 Connect with us LinkedIn: [ Ссылка ]

365 Data Science is an online educational career website that offers the incredible opportunity to find your way into the data science world no matter your previous knowledge and experience. We have prepared numerous courses that suit the needs of aspiring BI analysts, Data analysts and Data scientists.

We at 365 Data Science are committed educators who believe that curiosity should not be hindered by inability to access good learning resources. This is why we focus all our efforts on creating high-quality educational content which anyone can access online.

Check out our Data Science Career guides: [ Ссылка ]

#LinearRegression #Regression #Statistics

свернуть