✅ Regressão Múltipla e Análise dos Pressupostos Estatísticos

✅ Multiple Regression and Analysis of Statistical Assumptions Prof. Dr. Leonardo Flach Post-doctoral fellow at the Massachusetts Institute of Technology (MIT/USA) ➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖ Multiple linear regression is a statistical method that refers to the causal relationship with more than two variables. That is, when the behavior of Y (dependent variable) is explained by more than one independent variable X1, X2, ....Xn. This method can be applied when you want to simultaneously investigate the effects, on Y, of 2 or more predictor variables. It is possible to apply the initial part of multiple regression in Excel. First step: Select the data tab in Excel and click on “Data Analysis”. In the menu that appears, select regression. Second Step: Select the column referring to the dependent variable and then select the columns that store the data of the explanatory variables. It is possible to standardize the output of the results. Linear regression is one of the most widely used statistical concepts in machine learning. Linear regression is so named because it is a straight line drawn from a relationship in a scatter plot. This regression line, in simple linear regression, summarizes a relationship between the data of two variables and can also be used to make predictions. The origin of linear regression comes from linear correlation, which consists of verifying the existence of a relationship between two or more variables. For example: considering two variables, X and Y, we can initially check how much X is associated with Y. If there is a strong association between the variables, we can later check how much X impacts Y. To do this, linear regression uses data points to find the best fit line to model this relationship. The result of linear regression allows you to check, within a database (dataset), if there is any kind of constant growth or decrease trend. There are several software programs that allow you to perform linear regression calculations. It is possible to perform linear regression in Excel, linear regression in Stata, linear regression in R, linear regression in Gretl, linear regression in Python. One way to implement it using free Python software is with the use of the "Scikit-learn" library. You can follow this model: from sklearn import linear_model, datasets #digit dataset from sklearn digits = datasets.load_digits() #create the LinearRegression model clf = linear_model.LinearRegression() #set training set x, y = digits.data[:-1], digits.target[:-1] #train model clf.fit(x, y) #predict y_pred = clf.predict([digits.data[-1]]) y_true = digits.target[-1] print(y_pred) print(y_true) For the Python implementation, using this or another library, you don't need to create the algorithm. You only need to correctly determine the variables and evaluate beforehand whether linear regression is the best way to make this prediction. #LinearRegression #MachineLearning #LinearRegression 💡 Subscribe to the channel! It's free! Share it. Subscribe to the channel, like, share the video, and activate the bell to help this video reach more people. We want to help spread scientific knowledge. Check out the entire playlist to learn more! ➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖ ⬛ ▶ Professor Leonardo Flach's Website – https://leonardoflach.paginas.ufsc.br ➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖ Chapters 00:00 – Welcome! 00:02 – Subscribe to the channel! 05:00 – Introduction 10:00 – Development ➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖ ☑️ Thank you for visiting! #MultipleRegression #RegressionAnalysis ➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖ DO NOT CLICK HERE: https://bit.ly/3b3DcK1