## Shiny practice items 'Statistics and business analytics', module 7

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. Explain the idea behind dummy variables

2. Interpret a regression model that includes dummy variables to represent one or more categorical variables

3. Investigate whether the slope of an independent variable depend on the value of another independent variable through an interaction variable

4. Understand the basic concept of the log-log (non-linear) regression model

5. Use SPSS to include categorical and interaction variables in a regression model

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').

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 running regressions, 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.

#### Question 5

What is your predicted value for the index of luxury objects ownership for an Italian owning 7 durable goods? For an American owning 5 durable goods? Use the model in question 4 to obtain a point prediction.

#### Purpose

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

1. Which of the following statements about dummy variables in linear regression models is true?

2. A researcher estimates the following regression model: LN(sales) = 50 - 2*LN(price). LN means natural log. Both price and sales are measured in euros. Both coefficients are significant and the model assumptions are satisfied. What is the interpretation of the coefficient -2?

3. The following coefficients are obtained from the mini-case dataset (gender salary discrimination) that we discussed in module 7 (good practice: reproduce this table!). Which of the following statements is correct? 4. Consider the following scatter plot between (hypothetical) sales and price data. Which of the statements regarding this scenario is correct? 5. Using the mini case data (gender salary discrimination), we estimate the regression model as shown in the following table (good practice: reproduce this table!). Which conclusion can be drawn from this table? 