In this example, we are going to examine a dataset of part of the passenger list for the Titanic. Our aim is to use the data to fit a model which can predict each passenger’s survival/nonsurvival, given their age, sex and class of ticket.

Multiple linear regression is a widely used statistical method for modelling the relationship between a dependent variable and one or more independent variables. In this analysis, we aim to develop a robust and interpretable linear regression model while addressing common challenges such as multicollinearity, overfitting, and variable selection. Key steps include evaluating the significance of predictors using the F-statistic and p-values, assessing multicollinearity through variance inflation factors, and exploring the impact of reducing the number of variables on model performance.

A simple data set of 205 automobiles with attributes: horsepower and price is cleaned, examined, analysed and a linear regression model constructed and tested.

Objective: Analyse the dataset 'macro' using tandem analysis (PCA, followed by k-means), k-means analysis and reduced k-means analysis (PCA and k-means simultaneously) and compare the results in order to find the best clustering solution.

Examining the Economic Indicators Dataset ‘macro’ with PCA and Cluster Analysis

Objective: To perform non-hierarchical (k-means) and hierarchical (Ward) clustering on the USArrests dataset, then compare the results.

We are going to identify clusters in the ‘mtcars’ dataset and experiment with different types of bond method. We aim to develop an understanding of how the different bond types affect the solution(s).

Analysis of the iris dataset using kmeans clustering. Is 3 the magic number?