REGRESSÃO LINEAR SIMPLES ✅ INTRODUÇÃO À ESTATÍSTICA EP 1

LINEAR REGRESSION What is a regression and what is it for? In statistics or econometrics, linear regression is an equation for estimating the conditional (expected value) of a variable y, given the values ​​of some other variables x. (Wikipedia) In short, a regression is used to estimate the value of something based on a series of other historical data, for example: You have a database of the appreciation of your house every month for the last 18 years (2000–2018) and you are going to use this historical basis to predict how much your house will be worth in 2 years, that is, how much your house will cost when it reaches 2020? I don't know! But I know someone who can tell us: Our regression model A linear regression model allows us to study the relationships between two continuous numerical variables (something that constantly increases or decreases). An input variable (x) is also called a predictor / explanatory / independent variable. This is the value you will use to find out how much your house will cost in 2020. An output variable (y) is also called a dependent variable, response/result/dependent. This is the value you want to find out, in the case of the example above, this is the value of your house in 2020. The equation for our simple linear regression model can be written as: y = b+a*x ------------------------------------------------------------------------------------------------------------------------------------------------------------- tags: linear regression, linear regression example, linear regression solved exercises, correlation coefficient, linear correlation exercises, simple linear regression, linear regression example, simple linear regression example, statistics, linear regression exe, Review on linear regression, Simple Linear Regression, linear regression concept, linear regression and correlation, correlation and linear regression examples, statistical regression, regression coefficient Linear regression is the process of drawing a straight line through data in a scatter plot. The straight line summarizes this data, which is useful when making predictions. When to use linear regression? Linear regression quantifies the relationship between one or more predictor variables and an outcome variable. For example, linear regression can be used to quantify the relative impacts of age, sex, and diet (the predictor variables) on height (the outcome variable). What is correlation and linear regression used for? Correlation: summarizes the degree of relationship between two variables (X and Y, for example). Regression: results in a mathematical equation that describes the relationship between variables. The purpose of studying correlation is to determine (measure) the degree of relationship between two variables. Become a member of this channel and gain benefits:    / @murakami.   Now on the Rapidola Mathematics channel, you can become a member of the Basic Statistics and Applied Statistics teams.