Shiny practice items 'Statistics and business analytics', module 9

This app allows you to practice various aspects covered in this module.

Please make sure that you have fully completed the following tasks before continuing with this app:

1. Read the module's learning plan

2. Watch the topic videos and review the lecture notes

3. Work through the SPSS How to guide

The module has the following objectives:

1. Discuss the logic behind cluster analysis

2. Explain the K-means approach to clustering

3. Decide on the number of clusters underlying the data

4. Interpret clustering output for decision making

5. Perform a cluster analysis with R/Shiny

Please proceed with the practice items by clicking on the links in the top bar of this app. It is not necessary to complete these items in order. We encourage you to work together with a classmate!

There are three types of practice items: theory ('TH'), SPSS ('SP') and multiple choice ('MC').

These practice items will help you come prepared to the lab and help you perform better on the quizzes.

Thus, you should complete these practice items before joining the lab meeting corresponding to this module.

MBA program
Peter Ebbes

Question 1

Before you get started with any fancy-pancy data analytic approach, it is always a good idea to imagine how the data table would need to look like. Write down on scratch paper, how the data table could look like for this context.

Purpose

Test your knowledge about the subjects of this module. Let's do it!

1. A researcher fits a regression with a large set of independent variables. He celebrates because the R-square is 98%. Yet, a few months later, the researcher is dissapointed as the model did not work at all to predict for new observations. He had checked the VIFs (all close to 1) and he was not extrapolating the data. What may be going on?

2. A financial analyst is running cluster analysis on a large set of stock returns. Which of the following expressions is not true?

3. Which of the following expressions about cluster analysis is not true?

4. K-means clustering...

5. Cross-validation...