Learn about models as functions, explanatory and response variables, and residuals.
- [Instructor] The fundamental goal of statistical modeling is capturing the way in which the variation in one or more response variables is caused by or asseverated with the variation in one or more explanatory variables. A model can be seen as a function that takes explanatory variables as input and outputs response variables. To fit a model to the data, we apply to the explanatory variables for each case in our data frame and then compare the resulting response variables with the corresponding observed values.
We then modify the parameters of the model until the difference, known as residual, between the predicted and observed values is minimized in a precise mathematical sense. Once we have fit the model, it is useful for two different goals. One, its parameters may reveal important qualities or trends in the population under study. Two, we can use the model to predict the response values for sets of explanatory variables that we have not yet observed.
This is indeed a way in which models are mostly used in machine learning. The question remains of how we can choose the best model to fit the data. Or along similar lines, how to compare the performance of different models. Statistical modeling is a vast topic, and in this chapter, we can only scrape its surface, but I hope I can give you a sense of its development, which will guide you in your further study and exploration.
- Installing and setting up Python
- Importing and cleaning data
- Visualizing data
- Describing distributions and categorical variables
- Using basic statistical inference and modeling techniques
- Bayesian inference
Skill Level Intermediate
R Statistics Essential Trainingwith Barton Poulson5h 59m Intermediate
SPSS Statistics Essential Trainingwith Barton Poulson4h 57m Beginner
1. Installation and Setup
2. Importing and Cleaning Data
3. Visualizing and Describing Data
4. Introduction to Statistical Inference
5. Introduction to Statistical Modeling
Next steps1m 55s
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