7 lecture

Question 1

Regression basics video Equation

Answer 1

Y = a(intercept) + b(slope) * X

Question 2

º Intercept

Answer 2

a The independent variable

Question 3

Slope:

Answer 3

b The dependent variable

Question 4

R^2 =

Answer 4

ow much of the variation in the dependent variable is explained by the model?
R2 = 0.9033 = it explains 90,33 of the variation in te number of comments

Question 5

Concepts:

1. Slope

Answer 5

A unit change in X lead to Beta units change in Y

Ex. With every new day (whatever the time unit is) the number of comments is

Question 6

Concepts:2. Intercept

Answer 6

Average value of Y when X=0

Intercept interpretation not always possible (if the value 0 does not make sense for X)

Question 7

Dependent variable Independent variable Interpretation of β (slope)
Y X =

Answer 7

A unit change in X leads to β units change in Y

Question 8

Dependent variable Independent variable Interpretation of β (slope)
Y (log) X =

Answer 8

A 1% change in X leads to a β units change in Y

Question 9

Dependent variable Independent variable Interpretation of β (slope)
(log)Y X =

Answer 9

A unit change in X leads to a β % change in Y

Question 10

Dependent variable Independent variable Interpretation of β (slope)
(log)Y (log) X =

Answer 10

A 1% change in X leads to a β % change in Y

Question 11

What to look at? with a lineair regression model? 2X

Answer 11

1. Is the coefficient for the number of website visits statistically significant? A coefficient is statistically significant when its p-value < 0.05 (5% is a standard level of significance=we are willing to accept a 5% probability of making the wrong conclusion)
2. Is there an effect? If yes, what is the direction (+/-) and magnitude of the effect on purchase value?
   If no, is there some potential explanation?

Question 12

Regression model workflow 5 steps

Answer 12

Regression model workflow
1.	Model specification based on theory and logic
Which variables to include?
Possible interactions?
2.	Estimate parameters using software
3.	Interpret coefficients (significant coefficients only)
Direction and magnitude
4.	Evaluate model
Overall model significance 
Model fit
5.	Use model for prediction

Question 13

Model specification: linear regression
Which variables to include?
5X

Answer 13

• Marketing variables
• Customer characteristics
• Product characteristics
• Competitor activity
• Seasonality

Question 14

what leads to biased results?

Answer 14

Omitting relevant variables or incorrectly specifying the relationship between variables

Question 15

ProductSales_i=

Answer 15

β_0+β_1 VolumeOwned_i+β_2 VolumeEarned_i

Question 16

Natural logarithm (LN) transformations:
ln⁡(ProductSales_i )

Answer 16

ln⁡(ProductSales_i )=β_0+β_1 ln⁡(VolumeOwned_i )

+ β_2 ln⁡(VolumeEarned_i)

Question 17

Earned volume

Answer 17

nr comments from consumers on social media

Question 18

Owned volume =

Answer 18

nr posts made by the brand on its social media

Question 19

How to obtain coefficients that are interpreted as elasticities?

Answer 19

Transforming the variables and calculating by Natural logarithm (Ln) transformations:

Question 20

Interaction effects in regression models
Does the effect of earned social media volume on product sales depend on the volume of owned social media?
In other words: Does the volume of owned social media have an influence on the effect of earned social media volume on product sales
write the equation

Answer 20

Yi: product sales
X1,i: Earned social media volume for product I
X2,i: Owned social media volume for product I
Y_i=β_0+β_1 X_(1,i)+β_2 X_(2,i)+β_3 X_(1,i)×X_(2,i)

Question 21

Model specification: usual steps

WHY not put everything we can think of in a model?

Answer 21

We need to make choices about which variables to include, because large number of parameters require a large sample size to produce reliable estimates of coefficients and standard errors (at least 10 observations for each parameter that needs to be estimated).