Transcript Linear Regression - Lyle School of Engineering

CSE 5331/7331
Fall 2007
Regression
Margaret H. Dunham
Department of Computer Science and Engineering
Southern Methodist University
Some slides extracted from Data Mining, Introductory and Advanced Topics, Prentice Hall, 2002.
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Table of Contents
Linear Regression
 Nonlinear Regression
 Logistic Regression
 Metrics
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Remember High School?
Y= mx + b
 You need two points to determine a
straight line.
 You need two points to find values for m
and b.

THIS IS REGRESSION
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Regression
Predict future values based on past
values
 Linear Regression assumes linear
relationship exists.
y = c 0 + c1 x 1 + … + c n x n
 Find values to best fit the data
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Linear Regression
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Linear Regression

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Assume data fits a predefined function
Determine best values for regression
coefficients c0,c1,…,cn.
Assume an error: y = c0+c1x1+…+cnxn+e
Estimate error using mean squared error for
training set:
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Linear Regression Poor Fit
Why
use sum of least squares?
http://curvefit.com/sum_of_squares.htm
Linear
doesn’t always work well
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Nonlinear Regression
Data does not nicely fit a straight line
 Fit data to a curve
 Many possible functions
 Not as easy and straightforward as
linear regression
 How nonlinear regression works:

http://curvefit.com/how_nonlin_works.htm
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Logistic Regression
Generalized linear model
 Predict discrete outcome

– Binomial (binary) logistic regression
– Multinomial logistic regression
One dependent variable
 Logistic Regression by Gerard E. Dallal
http://www.tufts.edu/~gdallal/logistic.htm
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Logistic Regression (cont’d)
p
log(
)   0  1 x
1 p

Log Odds Function:

P is probability that outcome is 1
Odds – The probability the event occurs
divided by the probability that it does not
occur
Log Odds function is strictly increasing as p
increases
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Why Log Odds?

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
Shape of curve is desirable
Relationship to probability
Range –  to + 
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P-value
The probability that a variable has a
value greater than the observed value
 http://en.wikipedia.org/wiki/P-value
 http://sportsci.org/resource/stats/pvalue
s.html
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Correlation
Examine the degree to which the values
for two variables behave similarly.
 Correlation coefficient r:

• 1 = perfect correlation
• -1 = perfect but opposite correlation
• 0 = no correlation
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Covariance
Degree to which two variables vary in
the same manner
 Correlation is normalized and
covariance is not
 http://www.ds.unifi.it/VL/VL_EN/expect/e
xpect3.html
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Residual
Error
 Difference between desired output and
predicted output
 May actually use sum of squares
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