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Description
This course is designed to get you up to speed with the most important and powerful methodologies in statistics.
Introduction Statistical Modeling in R is a multi-part course designed to get you up to speed with the most important and powerful methodologies in statistical modeling in R.
In In this introduction, we'll take a look at what statistical modeling is and what it's used for, R tools for model building, using models for prediction (and using prediction to test models), and how to account for the combined influences of multiple variables.This course has been written from scratch, specifically for DataCamp users. As you'll see, by using computing and concepts from machine learning, we'll be able to leapfrog many of the marginal and esoteric topics encountered in traditional 'regression' courses.
The intermediate course will get you up to speed with the most important and powerful methodologies in statistics.
Tags
Syllabus
What is statistical modeling?
-This chapter explores what a statistical model is, R objects which build models, and the basic R notation, called formulas used for models.
Designing, training, and evaluating models
-In this chapter, you'll start building models: specifying what variables models should relate to one another and training models on the available data. You'll also provide new inputs to models to generate the corresponding outputs.
Assessing prediction performance
-This chapter is about techniques for deciding whether an explanatory variable improves the prediction performance of a model. You'll use cross validation to compare different models.
Exploring data with models
-This chapter is about constructing models to explore masses of data, for instance to generate hypotheses about what factors are important in how a system works. You'll see how the recursive partitioning model architecture, which has an internal logic for selecting explanatory variables, can be used to explore potentially complex relationships among variables. The chapter also covers the evaluation of prediction performance in models where the response variable is categorical, that is, models used for classification.
Covariates and effect size
-Real-world systems are complicated. To faithfully reflect that complexity, models can incorporate multiple explanatory variables. This chapter introduces the notion of covariates and how they allow you to model the effect of an explanatory variable while taking into account the effects of other variables.
-This chapter explores what a statistical model is, R objects which build models, and the basic R notation, called formulas used for models.
Designing, training, and evaluating models
-In this chapter, you'll start building models: specifying what variables models should relate to one another and training models on the available data. You'll also provide new inputs to models to generate the corresponding outputs.
Assessing prediction performance
-This chapter is about techniques for deciding whether an explanatory variable improves the prediction performance of a model. You'll use cross validation to compare different models.
Exploring data with models
-This chapter is about constructing models to explore masses of data, for instance to generate hypotheses about what factors are important in how a system works. You'll see how the recursive partitioning model architecture, which has an internal logic for selecting explanatory variables, can be used to explore potentially complex relationships among variables. The chapter also covers the evaluation of prediction performance in models where the response variable is categorical, that is, models used for classification.
Covariates and effect size
-Real-world systems are complicated. To faithfully reflect that complexity, models can incorporate multiple explanatory variables. This chapter introduces the notion of covariates and how they allow you to model the effect of an explanatory variable while taking into account the effects of other variables.
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TypeOnline Courses
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ProviderDataCamp
This course is designed to get you up to speed with the most important and powerful methodologies in statistics.
Introduction Statistical Modeling in R is a multi-part course designed to get you up to speed with the most important and powerful methodologies in statistical modeling in R.
In In this introduction, we'll take a look at what statistical modeling is and what it's used for, R tools for model building, using models for prediction (and using prediction to test models), and how to account for the combined influences of multiple variables.This course has been written from scratch, specifically for DataCamp users. As you'll see, by using computing and concepts from machine learning, we'll be able to leapfrog many of the marginal and esoteric topics encountered in traditional 'regression' courses.
The intermediate course will get you up to speed with the most important and powerful methodologies in statistics.
What is statistical modeling?
-This chapter explores what a statistical model is, R objects which build models, and the basic R notation, called formulas used for models.
Designing, training, and evaluating models
-In this chapter, you'll start building models: specifying what variables models should relate to one another and training models on the available data. You'll also provide new inputs to models to generate the corresponding outputs.
Assessing prediction performance
-This chapter is about techniques for deciding whether an explanatory variable improves the prediction performance of a model. You'll use cross validation to compare different models.
Exploring data with models
-This chapter is about constructing models to explore masses of data, for instance to generate hypotheses about what factors are important in how a system works. You'll see how the recursive partitioning model architecture, which has an internal logic for selecting explanatory variables, can be used to explore potentially complex relationships among variables. The chapter also covers the evaluation of prediction performance in models where the response variable is categorical, that is, models used for classification.
Covariates and effect size
-Real-world systems are complicated. To faithfully reflect that complexity, models can incorporate multiple explanatory variables. This chapter introduces the notion of covariates and how they allow you to model the effect of an explanatory variable while taking into account the effects of other variables.
-This chapter explores what a statistical model is, R objects which build models, and the basic R notation, called formulas used for models.
Designing, training, and evaluating models
-In this chapter, you'll start building models: specifying what variables models should relate to one another and training models on the available data. You'll also provide new inputs to models to generate the corresponding outputs.
Assessing prediction performance
-This chapter is about techniques for deciding whether an explanatory variable improves the prediction performance of a model. You'll use cross validation to compare different models.
Exploring data with models
-This chapter is about constructing models to explore masses of data, for instance to generate hypotheses about what factors are important in how a system works. You'll see how the recursive partitioning model architecture, which has an internal logic for selecting explanatory variables, can be used to explore potentially complex relationships among variables. The chapter also covers the evaluation of prediction performance in models where the response variable is categorical, that is, models used for classification.
Covariates and effect size
-Real-world systems are complicated. To faithfully reflect that complexity, models can incorporate multiple explanatory variables. This chapter introduces the notion of covariates and how they allow you to model the effect of an explanatory variable while taking into account the effects of other variables.
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