Multiple Multivariate Linear Regression
Multivariate Multiple Regression is the method of modeling multiple responses, or dependent variables, with a single set of predictor variables. For example, we might want to model both math and reading SAT scores as a function of gender, race, parent income, and so forth. This allows us to evaluate the relationship of, say, gender with each score. You may be thinking, “why not just run separate regressions for each dependent variable?” That’s actually a good idea! And in fact that’s pretty much what multivariate multiple regression does. It regresses each dependent variable separately on the predictors. However, because we have multiple responses, we have to modify our hypothesis tests for regression parameters and our confidence intervals for predictions. Multivariate Multiple Linear Regression is a statistical test used to predict multiple outcome variables using one or more other variables. It also is used to determine the numerical relationship between these sets of variables and others. The variable you want to predict should be continuous and your data should meet the other assumptions listed below. he assumptions for Multivariate Multiple Linear Regression include Linearity, No Outliers, Similar Spread across Range, Normality of Residuals, No Multicollinearity.
Applications of Multiple Multivariate Linear Regression? Let's Understand!
● Sales prediction in E-commerce companies
About Multiple Multivariate Linear Regression: Let's See!
●Tensorflow
●sci-kit learn
●Py torch
● Keras
●NumPy
●Pandas
●Matplotlib
●Theano
●Scipy
●Plotly
●Statsmodels
