# Linear Regression in R: Abalone Dataset

This tutorial will perform linear regression on a deceptively simple dataset. The abalone dataset from UCI Machine Learning Arvhives comes with the goal of attempting to predict abalone age (through the number of rings on the shell) given various descriptive attributes of the abalone (Shell sizes, weights of whole abalone and parts of shucked abalone). […]

# Multiple Linear Regression (SAS)

In this tutorial, we will be attempting linear regression and variable selection using the cirrhosis dataset. We attempt to predict incidence of cirrhosis on a population using a few descriptor variables from that population. The variables we have are: urbanpop – The size of the urban population lowbirth – the reciprocal of the number of […]

# Simple Linear Regression (SAS)

In this tutorial we will conduct simple linear regression on a dataset on an example dataset. In the dataset there are 62 individuals, and we will be regressing brain weight over body weight. As stated before, in simple linear regression we are trying to find a linear relationship between the dependent and independent variable, that […]

# Logistic Regression (R)

Logistic Regression is a type of classification model. In classification models, we attempt to predict the outcome of categorical dependent variables, using one or more independent variables. The independent variables can be either categorical or numerical. Logistic regression is based on the logistic function, which always takes values between 0 and 1. Replacing the dependent […]

# Multiple Linear Regression (R)

In this tutorial, we are going to be walking through multiple linear regression, and we are going to recognize that it’s not a lot different than simple linear regression. The model in multiple linear regression looks similar to that in simple regression. We’re adding more coefficients to modify the additional independent variables.     We […]

# Model Selection Schema

There are various model selection criteria in use for picking variables in linear regression. Some are applicable to other models outside of linear regression. Akaike’s Information Criterion – A useful criterion for indicating the amount of information contained within variables, and deciding whether to omit certain variables. AIC draws its justification from Information Theory. Coefficient […]

# Simple Linear Regression (R)

In this tutorial we will conduct simple linear regression on a dataset on an example dataset. In the dataset there are 62 individuals, and we will be regressing brain weight over body weight. As stated before, in simple linear regression we are trying to find a linear relationship between the dependent and independent variable, that […]

# Multiple Linear Regression

Multiple Linear Regression (MLR) allows us to find a linear relationship between multiple input variables and a single dependent output variable. This tutorial is going to be given in matrix notation. For more on matrix notation, including the rules of matrix multiplication, I suggest visiting the wikipedia page on the subject here. We are again […]

# Simple Linear Regression

Simple linear regression seeks to find a linear relationship between two variables; the independent ‘predictor’ variable, and the dependent ‘outcome’ variable. We represent this linear relationship with a line, the slope of which allows us to make inferences as to the nature of the relationship. I.e., for every unit change in the predictor variable, the […]