# STAT 512 Midterm

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The flashcards below were created by user MRK on FreezingBlue Flashcards.

1. Simple v. Multiple regression
• Simple: 1 axis
• Multiple: more than 1
• Assuming the relationship is linear
2. Smoothing curve v. Regression line
• Smoothing curve: Not straight, fits a curve using a given percentage of the points
• Regression Line: straight line using a statistical method
3. r2 value (coefficient of multiple determination)
• how well the model fits
• how much of the variation is y is explained by x
adjusts r2 by dividing each sum of squares by its associated degrees of freedom
5. residual
observed-predicted (with the line)
6. Why do we use simple linear regression?
• understand cause and effect relationships
• make decisions on cost
• to predict outcomes
7. case or data point
observed pairs of explanatory x, response y variables
8. Yi = β0 + β1Xi + εi
for i (1 to n)
• Simple linear regression model
• β0 = intercept
• β1 = slope
• ε = independent, normally distributed random errors with mean 0 and variance σ2
9. ε ~iid N(0,σ2)
iid = independent
10. how to find estimates for beta 1 and beta 0
• from plotting the regression line or
• Analytical procedure:
• b1 = Sum(Xi - mean X)(Yi - mean Y) / sum(Xi - mean X)2
• b0 = mean Y - b1 times mean X
11. Maximum Likelihood
A way to find estimators b1 and b0 but they are the same for simple linear regressions
12. to calc b1 by hand with points
• b1 = sum(ki times Yi)
• Ki = (Xi - mean X)/sum(Xi - mean X)2

## Card Set Information

 Author: MRK ID: 293178 Filename: STAT 512 Midterm Updated: 2015-01-28 20:41:06 Tags: Simple linear regression Folders: Description: Notes for the Midterm Show Answers:

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