Below are the solutions to these exercises on model diagnostics using residual plots. #################### # # # Exercise 1 # # # #################### data(“cars”) head(cars) ## speed dist ## 1 4 2 ## 2 4 10 ## 3 7 4 ## 4 7 22 ## 5 8 16 ## 6 9 10 #################### # […]

# statistics

## Regression Model Assumptions Exercises

You might fit a statistical model to a set of data and obtain parameter estimates. However, you are not done at this point. You need to make sure the assumptions of the particular model you used were met. One tool is to examine the model residuals. We previously discussed this in a tutorial. The residuals […]

## Regression Model Assumptions Tutorial

Regression is used to explore the relationship between one variable (often termed the response) and one or more other variables (termed explanatory). Several exercises are already available on simple linear regression or multiple regression. These are fantastic tools that are used frequently. However, each has a number of assumptions that need to be met. Unfortunately, […]

## Generalized linear functions (Beginners)

On this set of exercises, we are going to use the lm and glm functions to perform several generalized linear models on one dataset. Since this is a basic set of exercises we will take a closer look at the arguments of these functions and how to take advantage of the output of each function […]

## Applying machine learning algorithms – exercises

INTRODUCTION Dear reader, If you are a newbie in the world of machine learning, then this tutorial is exactly what you need in order to introduce yourself to this exciting new part of the data science world. This post includes a full machine learning project that will guide you step by step to create a […]

## How to prepare and apply machine learning to your dataset

INTRODUCTION Dear reader, If you are a newbie in the world of machine learning, then this tutorial is exactly what you need in order to introduce yourself to this exciting new part of the data science world. This post includes a full machine learning project that will guide you step by step to create a […]

## Probability functions beginner

On this set of exercises, we are going to explore some of the probability functions in R with practical applications. Basic probability knowledge is required. Note: We are going to use random number functions and random process functions in R such as runif, a problem with these functions is that every time you run them […]

## Hacking statistics or: How I Learned to Stop Worrying About Calculus and Love Stats Exercises (Part-6)

Statistics are often taught in school by and for people who like Mathematics. As a consequence, in those class emphasis is put on leaning equations, solving calculus problems and creating mathematics models instead of building an intuition for probabilistic problems. But, if you read this, you know a bit of R programming and have access […]

## Volatility modelling in R exercises (Part-4)

This is the fourth part of the series on volatility modelling. For other parts of the series follow the tag volatility. In this exercise set we will explore GARCH-M and E-GARCH models. We will also use these models to generate rolling window forecasts, bootstrap forecasts and perform simulations. Answers to the exercises are available here. […]

## Volatility modelling in R exercises (Part-3)

This is the third part of the series on volatility modelling. For other parts of the series follow the tag volatility. In this exercise set we will use GARCH models to forecast volatility. Answers to the exercises are available here. Exercise 1 Load the rugarch and the FinTS packages. Next, load the m.ibmspln dataset from […]