Session 5

Question 1

What is the coefficient of determination ?

Answer 1

It estimates the fraction of the variance in Y that is explained by X.
Always between 0 and 1.

Question 2

What is the regression analysis ?

Answer 2

The regression analysis is a set of empirical methods designed to estimate the relationships between variables, as defined in an equation model.
We use it to test the theories that predict these relationships.

Question 3

Can you give some example of regression analysis ?

Answer 3

Ex :
Theory : wage = f(education, experience)
Model: wage = Bo(date 0) + B1 education + B2experience + e

Question 4

What is B0 in the regression analysis ?

Answer 4

B0 is the ‘constant’ of the model, here the wage with no education and no experience

Question 5

What is “e” in the regression analysis ?

Answer 5

“e” is the error term or disturbance.

“e” contains the unobserved factors and the measurement errors.

Question 6

What is the “ei” ?

Answer 6

Epsilon (ei) = the actual value for Y (Yi) - the predicted value for Y(Y chapeau i)

Question 7

What is OLS ?

Answer 7

OLS = Ordinary least squares regression

Question 8

What is R² ?

Answer 8

R² = explained variation in Y = total variation in Y.

Question 9

What is a good R² ?

Answer 9

It depends, if your goal is to , R = 1 is better.

if your is to look at the impact of the variable, you don’t care about R².

Question 10

What is the similarities between correlation and regression ?

Answer 10

• • 3 similarities :
    the standardized regression coefficient is the same as Pearson’s correlation coefficient
    • The square of Pearson’s correlation coefficient is the same as theR2in simple linear regression
    • Neither simple linear regression nor correlation answer questions of causality directly.

Question 11

When do you reject your correlation hypothesis ?

Answer 11

If p is inferior as a, usually 5% is significant. Than you rejected the non-hypothesis, there is an effect.

If p value is greater than 5% you failed to and you rejected to. Than you accepted the hypothesis.

Question 12

What are the step ?

Answer 12

Look at model fit. If the p-value of the F-test H0 : model does not explain a significant amount of Y variation.

Question 13

What is the difference between multiple R² and adjusted R² ?

Answer 13

Multiple R² = just goes up so

Adjusted R² is the proportion of Y that you explain.

Question 14

What is a multiple regression analyisis ?

Answer 14

Multiple regression is a statistical approach used to quantify the sign and magnitude of the relationship between a focal dependent variable (marketing outcome in our context) and several independent variables (e.g., marketing effo

Question 15

When do you have to use a multiple regression analysis ?

Answer 15

• To determine how one of multiple marketing interventions incrementally affects observed marketing outcomes.
• To compare the effects of multiple marketing interventions on marketing outcomes.
• To predict the likely market outcomes due to various combinations of marketing interventions

Question 16

Is cross elasticity are negative or positive ?

Answer 16

It depends :
Complementary -> negative
if it is the concurrents products -> it is positive

Question 17

How many IVS should you include ?

Answer 17

Each variable should :

• Have a theoretically plausible reason
• Add explanatory power
• Can not highly correlate with any other independent variable

Question 18

What is a directional hypothesis ?

Answer 18

If X goes up, Y goes up, it’s a directional hypothesis
If X goes up, Y goes down, it’s a directional hypothesis.
If X goes up, Y goes I don’t know, it’s not a directional hypothesis.

Question 19

What is the difference between prediction and prescription analysis ?

Answer 19

Last slide.