Study your flashcards anywhere!
Download the official Cram app for free >
 Shuffle Toggle OnToggle Off
 Alphabetize Toggle OnToggle Off
 Front First Toggle OnToggle Off
 Both Sides Toggle OnToggle Off
 Read Toggle OnToggle Off
How to study your flashcards.
Right/Left arrow keys: Navigate between flashcards.right arrow keyleft arrow key
Up/Down arrow keys: Flip the card between the front and back.down keyup key
H key: Show hint (3rd side).h key
A key: Read text to speech.a key
26 Cards in this Set
 Front
 Back
Define Multiple Regression Analysis

to analyze the relationship between 2 or more variables


Formula for multiple regression

y = A + Bx + Cz + Dt


How is multiple and simple regression similar

1. the leastsquares criterion is the same
2. the residuals should be random 

Measure of closeness for Multiple Regression is ..

the adjusted correlation coefficient:
Rbarsquared 

Describe Rbarsquared

is based on Rsquared but adjusted to make allowance for the number of righthand side variables included in the regression


Describe the problem of collinearity

when tow (or more) of the x variables are highly correlated. Difficult to discriminate between the effects of the two variables. aka multicollinearity.


What are the remedies for collinearity?

1. Use only one of the variables (subjective)O
2. Amalgamate the variables (if it would have meaning) 3. Substitute one of the variables with a new variable with has similar meaning 

How to test for collinearity?

Inspect the correlation coefficient for each pair of x variables. Highly coefficient pairs are collinear.


Process for multiple regression

1. Inspect scatter diagrams to confirm approx. linear relationships.
2. Complete regression analysis by computer 3. Check residuals 4. Check for collinearity. 

Describe method of carrying out a nonlinear regression.

relies on transforming the nonlinear regression so that it can be treated as if it were linear.


Variable is transformed when ...

a variable x is turned into it square, its reciprocal, or its logarithm


Closeness of Fit can be measured by ...

the ratio of the mean squares of the two sources of variation is a F ratio if there is no linear relationship.


How to test for randomness of residuals?

The Runs Test


What is the limit to the number of variables that can be included in Regression analysis?

Any number, but need computing power. Important criterion  variables must have significant effect on value of y.


Significance test for the coefficients based on what distributions?

Observations < 30  tdistribution
Observations > 30  normal distribution 

What are the steps for significance test for any righthand side variable:

1. Hypothesis is true pop. coefficient for variable is 0.
2. Evidence is just the set of observations from which regression coefficients have been estimated. Also coefficient of the variable in question, standard error must be calculated. 3. Significance level  5% 4. Degrees of freedom are: n  k  1 (k = number of variables) 5. If t Obs exceeds t.025 hypothesis rejected, is significant. less than .025 then not significant. 

How is the accuracy of a prediction calculated?

By calculating the standard error of the residuals.


Uncertainty in a prediction comes from:

1. What regression doesn't deal with  residuals.
2. What the regression does deal with  error in the estimation of regression model coefficients. 

How are confidence levels for predictions calculated?

By calculating the Standard Error of Predicted Values (SE(Pred)).


What are the criterion for SE(Pred)?

1. More than 30 data points
2. 95% of future values of the variable are likely to fall with +2SE(Pred) of the point estimate. 3. If fewer than 30 data points, then tdistribution applies. 

The 95% confidence level for predictions is called?

The forecast interval.


What is the structured approach to Regression problem?

1. Propose a tentative model (scatter diagrams or prior knowledge)  what equation might be like
2. Run the regression and check closeness of fit 3. Check the residuals 4. Decide if any x variables should be discarded 5. Check for collinearity 6. Decide if regression estimates are accurate enough 7. If necessary, formulate a new regression model. 

Similarities of simple and multiple regressioni

1. Substitution of x values in regression to make predictions
2. F test to measure closeness of fit 3. Checking of residuals for randomness 4. Use of SE(Pred) for accuracy 

Differences between simple and multiple regression

1. Adjustment of correlation coefficient to allow for degrees of freedom
2. t test to determine variables to take out 3. Check for collinearity 

What is the Degrees of Freedom for Rthe sum of squares (regression) in a multiple regression?

k, the number of x variables


What are the degrees of freedom for the sum of squares (residuals)?

n  k  1
