Lecture 3

Question 1

Residual standdard Error

Answer 1

Average amount that the response deviate from the regression line

y~ is the estimate
n is the sample size

Question 2

RSE small implies model fits data well

Answer 2

True

Question 3

RSE high implies model does not fit Data well

Answer 3

True

Question 4

Any prediction of lpsa based on lweight will still be off by 1.046 units on average.

If it is accepted or not it depends on the problem

Answer 4

True

Question 5

RSE is measured in units of the output

Answer 5

TRUE

Question 6

R-squared is a measure of the fit however without the units

Answer 6

YES

Question 7

RSS: Amount if variablity that is left unexplained after performing the regression

Answer 7

True

Question 8

TSS: total variance in response to Y

Answer 8

Amount of variability in response before regression is performed

Question 9

R squared measure the proportion of variability in response y that can be performed using x

Answer 9

True

Question 10

R squared close to 1 : large proportion of variability is explained by x which is good

Answer 10

True

Question 11

R squared close to 0 => Regression did not explain much of the variability

Answer 11

True

Linear regression thus can be wrong

Question 12

When the application we are considering to approximate is far from being approximated using he model then R2 will be near zero

Answer 12

True

Question 13

R2 is highly affected by the number iof predictors we have

Answer 13

True

Question 14

Since R2 is highly affected by the number of predicotrs we have what is called adjusted R2,(how we pick predicotrs)

Answer 14

Regards

Question 15

We want F-statistics to be as far from 1 as it can be

Answer 15

True

Question 16

The larger the F-statics , the more it indicates that we have a relation between what we are feeding into the model and the response

Answer 16

True

Question 17

Correlation : measure of linear relationship between X and Y

Answer 17

True

Question 18

Correlation does not imply casuality, it meeans how much value vary in the same way

Answer 18

True

Question 19

Multiple linear regression model , we want to add more predictors to our response variable

Answer 19

Trues

Question 20

Interaction effect the or as known as the synergy effect in marketing

Answer 20

Accounting for possible interactions between the predictors

Question 21

I introduce a new coefficient and a new variable given by X1 * X2 which allws me to account for interaction

Answer 21

True

Question 22

Linear regression: I am assuming the relationship between response and the predictor is linear

Answer 22

True

Question 23

Since the relationship between the predictor and the response is not always linear thus we can generate a polynomial regression model

Answer 23

True

Question 24

We should always ask ourselves, is it worth it to create a higher order model?

Answer 24

True

Question 25

I data science we are taking a sample from the population in order to get something that we can say about the population

Answer 25

TRUE

Question 26

W want to keeep part of our data aside in order to test our model

Answer 26

TRUE

Question 27

WE want to see how our model is performing on different subsets of data

Answer 27

TRUE

Question 28

We want to estimate the test prediction error of our model

Answer 28

TRUE

Question 29

Resampling: Given one sample we repeatadily draw samples from it in order to refit our model

Answer 29

TRUE

Question 30

Cross validation is when we want to evaluate the performance of our model by estimating it is test error

Answer 30

TRUE

Question 31

When you have a flexible model the training error might underestimate the test error

Answer 31

True

Question 32

We divide our data into part for creating model and part for testing

Answer 32

TRUE

Question 33

WE need to randomly split our data

Answer 33

TRUE