Week 9 Linear Regression Flashcards

1
Q

Name the 2 types of relationships in linear regression

A
  1. Deterministic (or functional) relationship
  2. Statistical relationship
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2
Q

The relationship between Celsius and Fahrenheit would be described as a ___________ relationship

A

Deterministic (or functional)

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3
Q

The relationship between Celsius and Fahrenheit would be described as a ___________ relationship

A

Deterministic (or functional)

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4
Q

If you graph a deterministic relationship, where will the relationships line fall?

A

Directly on the data points

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5
Q

The relationship between height and weight would be described as a _________ relationship

A

Statistical

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6
Q

Name this relationship: Alcohol consumed and blood alcohol content

A

Statistical

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7
Q

Name this relationship: Speed and gas mileage

A

Statistical (As speed increases you would expect gas mileage to decrease)

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8
Q

Name this relationship: Speed and gas mileage

A

Statistical (As speed increases you would expect gas mileage to decrease)

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8
Q

In this relationship, the equation exactly describes the relationship between the two variables

A

Deterministic

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9
Q

When do you use Regression?

A

When you have a series of continuous data that follows some sort of pattern

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10
Q

Regression determines the _______ of the ____________ between the dependent variable and a series of other variables (known as ___________ variables)

A

strength of the relationship, independent

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11
Q

Name this type of regression: ALlows us to summarize and study relationships between two continuous variables

A

Simple linear

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12
Q

In simple linear regression we have one variable denoted as x, which is the ________ variable, and a variable denoted as y, which is the ________ variable

A

independent, dependent

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13
Q

This variable is known as the predictor

A

independent variable

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14
Q

This variable is known as the response

A

dependent variable

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15
Q

For a single line, Linear regression uses these parameters

h(x) = Θ0 + Θ1 x

What is this the equivalent of?

A

y = mx + b

16
Q

Name this: A single line that is the best-fit line to a data set. The line is then used to predict real values for continuous output

A

Linear regression with one variable

17
Q

What else is linear regression with one variable called?

A

Univariate linear regression

18
Q

How are the best Θ0 and Θ1 values chosen for linear regression?

A

They chosen using a cost function

19
Q

How does a cost function work?

A

The total error is calculated between the predicted values and actual values.

We continue to change the values until we find the minimum error.

20
Q

A line that fits the data “best” will be the one with the

A

minimum amount of prediction errors

21
Q

What we use to find the overall prediction error?

A

the least squares criterion

22
Q

To measure accuracy we can calculate the “Coefficient of determination”, which is also called the residual or r^2

What does r represent?

A

The correlation coefficient

23
Q

What is the range of r^2?

A

It’s a number between 0 and 1

24
What does it mean if r^2 is higher? What does it mean if it's lower?
The closer to 1, the closer the data is to the line. The closer to 0, the further the data is from the line
25
In a multiple regression model, what does each number mean next to the variable?
The strength/type of relationships each variable has with the class variable. Type = whether it's positive or negative
26
Is multivariate regression the same as multiple regression? How?
No. Multivariate = multiple dependent variables, possible multiple independent variables Multiple regression = single dependent variable, multiple independent variables
27
Name this: Models a relationship between independent (predictor) variables and a categorical response variable Through this we can estimate the probability of falling into a certain level of the categorical response, given a set of predictors
Logistic regression