Book - Chapter 6 Analytical Theory Regression

Question 1

Linear regression is a useful tool for answering what question

Answer 1

What is a persons expected income

Question 2

Logistic regression is a popular method for answering what question

Answer 2

What is the probability that an applicant will default on the loan

Question 3

In a linear regression what is the output

Answer 3

Continuous variable

Question 4

In linear regression what is the input

Answer 4

Continuous or discrete variables

Question 5

What is a key assumption of linear regression

Answer 5

That the relationship between an input variable and an output variable is linear

Question 6

What is a linear regression model

Answer 6

A probabilistic one that accounts for the randomness that can affect any particular outcome

Question 7

Where would you use linear regression

Answer 7

Real estate demand forecasting and medical for example proposed radiation treatment and reducing tumour sizes

Question 8

What is the model outcome of linear regression

Answer 8

A set of estimated coefficient to indicates the relative impact of each input variable

Question 9

In the linear regression what is a common technique to estimate the para metres

Answer 9

Ordinary least squares (0LS)

Question 10

What is the goal of OLS

Answer 10

Find the line the best approximates relationship between the outcome variable and the input variable

Question 11

What is a categorical variable

Answer 11

For example female or male

Question 12

In regression what is the proper way to implement a categorical variable that can take on M different values

Answer 12

M -1 binary

Question 13

What is the confidence percentage for linear regression

Answer 13

95%

Question 14

Linear regression what a confidence intervals used for

Answer 14

To draw inferences on the populations expected outcome, and prediction intervals are used to draw inferences on the next possible outcome

Question 15

What is a major assumption in linear regression modelling

Answer 15

That the relationship between the input variables and the output variable is linear

Question 16

How would you evaluate a relationship between the input variable and the output variable

Answer 16

To plot the output variable against each input variable

Question 17

What are common transformations in the linear regression

Answer 17

Taking square roots or the logarithm of the variables
Create a new input variables such as the age squared and added to the linear regression model to fit a quadratic relationship between an input variable and the output

Question 18

What is N fold cross validation

Answer 18

Common practice to randomly split the entire dataset into training set and a testing set

Question 19

What occurs in N fold cross validation

Answer 19

The entire dataset is randomly split into N data sets of approximately equal size
A model is trained against N -1 of these dataset and tested against the remaining dataset. A measure of the model area is obtained.
This process is repeated a total of eight times across the various combinations of any data sets taken N -1 at a time
The observed n model errors or averaged over the n folds

Question 20

What are outliers

Answer 20

They can result from bad data collection, data processing errors, or an actual rare occurrence

Question 21

What is the impact of logistic regression

Answer 21

Continuous or discrete variables

Question 22

What is the output of logistic regression

Answer 22

Coefficients that indicate the impact of each driver

Question 23

What are the use cases for logistic regression

Answer 23

Medical in the way you measure the likelihood of A patient response to treatment
Finance to determine the probability then after we default on the loan
Marketing to determine if the customer will switch carriers
Engineering the probability of a mechanical part experience a malfunction

Question 24

Logistical progression as the value of wine increases what happens the probability

Answer 24

The probability of the outcome occurring increases

Question 25

What is MLE

Answer 25

Maximum likelihood estimation and its use to estimate the model parameters

Question 26

In logistical aggression what is null deviance

Answer 26

Is the value where the likelihood function is based only on the intercept term

Question 27

What is the residual deviance in logistic regression

Answer 27

The value where the likelihood function is based on the parameters in the specified logistic model

Question 28

What is pseudo-r squared

Answer 28

A measure of how well the fitted model explains the data as compared to the default model of no predictor variables and only and intercept term

Question 29

If the pseudo R squared value is near one what does that indicate

Answer 29

A good fit over the simple null model

Question 30

How is logistic regression used as a classifier

Answer 30

To assign class labels to a person, item, or transaction based on the predicted probability provided by the model

Question 31

What is the default probability threshold in logistical regression

Answer 31

0.5

Question 32

How do you work out the false positive rate

Answer 32

Number of falls positives divided against number of negatives

Question 33

How do you work out the true positive rate

Answer 33

But of true positives divided by number of positives

Question 34

What is the receiver operating characteristic (ROC) curve

Answer 34

It is the plot of the true positive rate against the full positive rate

Question 35

When is the RAC curve useful

Answer 35

For evaluating other classifiers