Lesson 14

Question 1

• Predicts values of one variable based on information of the values of one or more variables.
• Uses the relation between two or more quantitative variables to predict the values of one variables using the values of the other variables

Answer 1

Regression Analysis

Question 2

In regression analysis, ____? relationships cannot be determined

Answer 2

Causal

Question 3

• Straight line that describes how the values of a response variable (y) depends on the values of an explanatory variable (x)
• The line with the “best fit” i.e. the line that minimizes the differences between the line and all the data points

Answer 3

Regression Line

Question 4

Relationship approximated with a straight line

Answer 4

Linear Relationship

Question 5

Determine (Perfect) Linear Relationship

Answer 5

• A perfect Straight line
• All X values correspond to Y

Question 6

• Technique that produces the least square line
• Produces the prediction line that minimizes the sum of the squares of the deviations of the observed values from the predicted Y.
• The sum of squared deviations (Or the sum of the squared errors) denoted by SSE.

Answer 6

Ordinary Least Squares (OLS) Method

Question 7

Correlation vs Regression

Answer 7

Correlation: Two values being measured are treated interchangeably
Regression: There is a clear asymmetry in the relationship between variables

Question 8

Method for evaluating the relationship between 2 continuous variable

Answer 8

Simple Linear Regression

Question 9

• Method used to visualize whether a relationship between two interval/ratio variables is linear
• The values of IV are placed on the x-axis while the values of DV are placed on the y-axis

Answer 9

Scatterplot or Scatter Diagram

Question 10

In Simple Linear Regression, (X) is regarded as the ___?

Answer 10

Predictor, Explanatory, or Independent Variable

Question 11

In Simple Linear Regression, (Y) is regarded as the ___?

Answer 11

Response, Outcome, or Dependent Variable

Question 12

In the prediction equation 𝑌(hat) = α + βX + ε, what is Y(hat)?

Answer 12

Predicted value of the dependent variable, Y

Question 13

In the prediction equation 𝑌(hat) = α + βX + ε, what is X?

Answer 13

Value of the independent variable, X

Question 14

In the prediction equation 𝑌(hat) = α + βX + ε, what is ε?

Answer 14

Residuals or prediction error, e

Question 15

In the prediction equation 𝑌(hat) = α + βX + ε, what is α?

Answer 15

The point where the line crosses the y-axis (X=0), intercept

Question 16

In the prediction equation 𝑌(hat) = α + βX + ε, what is β?

Answer 16

How much the value of the DV changes when the IV increases by one unit, slope