Flashcards in Book - Chapter 6 Analytical Theory Regression Deck (35)

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1

## Linear regression is a useful tool for answering what question

### What is a persons expected income

2

## Logistic regression is a popular method for answering what question

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

3

## In a linear regression what is the output

### Continuous variable

4

## In linear regression what is the input

### Continuous or discrete variables

5

## What is a key assumption of linear regression

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

6

## What is a linear regression model

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

7

## Where would you use linear regression

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

8

## What is the model outcome of linear regression

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

9

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

### Ordinary least squares (0LS)

10

## What is the goal of OLS

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

11

## What is a categorical variable

### For example female or male

12

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

### M -1 binary

13

## What is the confidence percentage for linear regression

### 95%

14

## Linear regression what a confidence intervals used for

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

15

## What is a major assumption in linear regression modelling

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

16

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

### To plot the output variable against each input variable

17

## What are common transformations in the linear regression

###
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

18

## What is N fold cross validation

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

19

## What occurs in N fold cross validation

###
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

20

## What are outliers

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

21

## What is the impact of logistic regression

### Continuous or discrete variables

22

## What is the output of logistic regression

### Coefficients that indicate the impact of each driver

23

## What are the use cases for logistic regression

###
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

24

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

### The probability of the outcome occurring increases

25

## What is MLE

### Maximum likelihood estimation and its use to estimate the model parameters

26

## In logistical aggression what is null deviance

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

27

## What is the residual deviance in logistic regression

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

28

## What is pseudo-r squared

### 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

29

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

### A good fit over the simple null model

30