## Shiny practice items 'Statistics and business analytics', module 6

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 difference between correlation and regression analysis

2. Illustrate the logic behind regression analysis using the simple linear regression model

3. Use the multiple linear regression model to explain the variation in the dependent variable

4. Carry out regression diagnostic tasks

5. Use predictions to inform decisions

6. Perform regression analysis in SPSS

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

The manager wonders whether the model has any relevance at all. In other words, do the set of independent variables as a whole explain any variation in (own) Sales? And if so, how much?

#### Question 2

The manager would like to know what the regression equation looks like. Take a scratch paper, and write down the regression equation. Make sure the equation can be understood by others so define the variables that you include in your model.

#### Question 3

The manager first would like to know whether their own decision regarding price and ad spending have any effect on their own sales. And if so, how much extra sales can be expected (or would be lost) if the company increases price or increases ad spending. What would you tell the manager?

#### Question 4

The manager is flabbergasted. From the equation you wrote down for the manager in Question 2 it looks like when competitor 2 increases the price, the sales of the manager's product go down. More specifically, if competitor 2 raises the price with one unit, the sales of the manager's product go down by 0.195 units (or 195HCs). That is counter to what the manager expected. The manager asks you for an explanation: "How is it possible that when the competitor raises the price, our sales go down?" What explanation would you give the manager?

#### Question 5

The manager heard that competitor #1 is planning to increase ad spending. Although the manager does not have too much details, should the company consider a response by increasing their own advertisement? That is, could one expect that the sales may be hurting when competitor #1 increases ad spending? Based on these analyses, what would you say?

#### Question 6

The CEO of the company does not like changing too many things at the same time. During the next board meeting the CEO will propose to either make some modification to the pricing strategy or to the advertisement strategy. The manager already knows what to focus on: pricing. As the manager explains to the CEO, both price and ad spending are drivers of sales (we discovered that in question 3), but looking at the estimated regression equation (question 2), it can be seen that the estimated slope coefficient of price is largest. So working on pricing can have the biggest impact on Sales, according to the manager. You sit in the same meeting and see it all happening. What do you say?

#### Purpose

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

1. For which of the following scenarios can you run a simple linear regression model as we learned it in this module?

2. Which of the following statements about correlation and regression analysis is true?

3. Which of the following statements about the mechanics of regression analysis is not true?

4. Considering the following regression output, where Sales (own sales of the focal company) is the dependent variable and four sets of prices (own price and 3 competitors' prices) are the independent variables. All variables are quantitative. Which of the following statements about this estimated regression model is true?

5. Consider the following simple linear regression model estimated on the La Quinta dataset. We call the intercept $\beta_{0}$ and the slope of the number of rooms within the three mile radius (#Rooms) $\beta_{1}$. What expression is correct?