Dataquest has a great article on predictive modeling, using some of the demo datasets available to R. I wanted to use real world data, so I saved 20 years of team stats from Pro Football Reference as CSV files. As described in the article, I collected the various stats and then normalize...
If you are interested in diving into statistical models, go ahead and check the course on Statistical Modeling in R. Enhance Your Team's Data Skills with Continuous Learning As linear regression forms the foundation of many advanced analytical techniques, ensuring your team has a strong grasp of...
Vandana SonkerRajeev SinghAllied Business Academies
3.2 Prelude to Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-2 3.3 Missing Values for Different Types of Response Variables . . . . . . . . 3-3 3.4 Problems With Simple Alternatives to Imputation . . . . . . . . . . . 3-4 ...
energyRt(energysystem modelingR-toolbox /ˈɛnərdʒi ɑrt/) is a set of classes, methods, and functions that define a macro-language for energy system modeling within the R environment. This package offers a high-level, user-friendly interface that simplifies the development and analys...
Simple linear regression is also a versatile modeling technique.(简单线性回归还是一种多用途的建模技术。) This is all the same as we saw in the regression model.(这与我们在回归模型中看到的没有差别。) But if there are manual regression tests we will have to be selective.(要是有需要手工完...
Regression Modeling Strategies Current Goals Implement estimation and prediction methods for the Bayesian partial proportional odds modelblrmfunction Web Sites Overall:http://hbiostat.org/R/rms/ Book:http://hbiostat.org/rms/ CRAN:http://cran.r-project.org/web/packages/rms/ ...
Logistic regression is a standard tool for modeling data with a binary response variable. In R, you fit a logistic regression using the glm function, specifying a binomial family and the logit link function. In RevoScaleR, you can use rxGlm in the same way (see Fitting Generalized Linear Mod...
Linear regression has been taught repeatedly in self-claimed China-top1 statistics school since I was admitted. Nonlinearity arises in various ways in statistical and econometric modeling and applications. For instance, Constant Elasticity of Substituion production function: ...
In supervised learning, we have a label which may be categorical-like (high, middle, and low) or numerical-like (2.56, 7.83, and 3.32). When predicting a category, this is known as classification modeling and we can compare the predicted category to the actual result. A regression model ...