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Linear Regression Models. Andy Wang CIS 5930-03 Computer Systems Performance Analysis. Linear Regression Models. What is a (good) model? Estimating model parameters Allocating variation Confidence intervals for regressions Verifying assumptions visually. What Is a (Good) Model?.

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linear regression models

Linear Regression Models

Andy Wang

CIS 5930-03

Computer Systems

Performance Analysis

linear regression models1
Linear Regression Models
  • What is a (good) model?
  • Estimating model parameters
  • Allocating variation
  • Confidence intervals for regressions
  • Verifying assumptions visually
what is a good model
What Is a (Good) Model?
  • For correlated data, model predicts response given an input
  • Model should be equation that fits data
  • Standard definition of “fits” is least-squares
    • Minimize squared error
    • Keep mean error zero
    • Minimizes variance of errors
least squared error
Least-Squared Error
  • If then error in estimate for xi is
  • Minimize Sum of Squared Errors (SSE)
  • Subject to the constraint
estimating model parameters
Estimating Model Parameters
  • Best regression parameters are

where

  • Note error in book!
parameter estimation example
Parameter EstimationExample
  • Execution time of a script for various loop counts:
  • = 6.8, = 2.32, xy = 88.7, x2 = 264
  • b0 = 2.32  (0.30)(6.8) = 0.28
allocating variation
Allocating Variation
  • If no regression, best guess of y is
  • Observed values of y differ from , giving rise to errors (variance)
  • Regression gives better guess, but there are still errors
  • We can evaluate quality of regression by allocating sources of errors
the total sum of squares
The Total Sum of Squares
  • Without regression, squared error is
the sum of squares from regression
The Sum of Squaresfrom Regression
  • Recall that regression error is
  • Error without regression is SST
  • So regression explains SSR = SST - SSE
  • Regression quality measured by coefficient of determination
evaluating coefficient of determination
Evaluating Coefficientof Determination
  • Compute
  • Compute
  • Compute
  • where R = R(x,y) = correlation(x,y)
example of coefficient of determination
Example of Coefficientof Determination
  • For previous regression example
    • y = 11.60, y2 = 29.88, xy = 88.7,
    • SSE = 29.88-(0.28)(11.60)-(0.30)(88.7) = 0.028
    • SST = 29.88-26.9 = 2.97
    • SSR = 2.97-.03 = 2.94
    • R2 = (2.97-0.03)/2.97 = 0.99
standard deviation of errors
Standard Deviationof Errors
  • Variance of errors is SSE divided by degrees of freedom
    • DOF is n2 because we’ve calculated 2 regression parameters from the data
    • So variance (mean squared error, MSE) is SSE/(n2)
  • Standard dev. of errors is square root: (minor error in book)
checking degrees of freedom
Checking Degreesof Freedom
  • Degrees of freedom always equate:
    • SS0 has 1 (computed from )
    • SST has n1 (computed from data and , which uses up 1)
    • SSE has n2 (needs 2 regression parameters)
    • So
example of standard deviation of errors
Example ofStandard Deviation of Errors
  • For regression example, SSE was 0.03, so MSE is 0.03/3 = 0.01 and se = 0.10
  • Note high quality of our regression:
    • R2 = 0.99
    • se = 0.10
confidence intervals for regressions
Confidence Intervals for Regressions
  • Regression is done from a single population sample (size n)
    • Different sample might give different results
    • True model is y = 0 + 1x
    • Parameters b0 and b1 are really means taken from a population sample
calculating intervals for regression parameters
Calculating Intervalsfor Regression Parameters
  • Standard deviations of parameters:
  • Confidence intervals arewhere t has n - 2 degrees of freedom
    • Not divided by sqrt(n)
example of regression confidence intervals
Example of Regression Confidence Intervals
  • Recall se = 0.13, n = 5, x2 = 264, = 6.8
  • So
  • Using 90% confidence level, t0.95;3 = 2.353
regression confidence example cont d
Regression Confidence Example, cont’d
  • Thus, b0 interval is
    • Not significant at 90%
  • And b1 is
    • Significant at 90% (and would survive even 99.9% test)
confidence intervals for predictions
Confidence Intervalsfor Predictions
  • Previous confidence intervals are for parameters
    • How certain can we be that the parameters are correct?
  • Purpose of regression is prediction
    • How accurate are the predictions?
    • Regression gives mean of predicted response, based on sample we took
predicting m samples
Predicting m Samples
  • Standard deviation for mean of future sample of m observations at xpis
  • Note deviation drops as m 
  • Variance minimal at x =
  • Use t-quantiles with n–2 DOF for interval
example of confidence of predictions
Example of Confidenceof Predictions
  • Using previous equation, what is predicted time for a single run of 8 loops?
  • Time = 0.28 + 0.30(8) = 2.68
  • Standard deviation of errors se = 0.10
  • 90% interval is then
verifying assumptions visually
Verifying Assumptions Visually
  • Regressions are based on assumptions:
    • Linear relationship between response y and predictor x
      • Or nonlinear relationship used in fitting
    • Predictor x nonstochastic and error-free
    • Model errors statistically independent
      • With distribution N(0,c) for constant c
  • If assumptions violated, model misleading or invalid
testing linearity
Testing Linearity
  • Scatter plot x vs. y to see basic curve type

Linear

Piecewise Linear

Outlier

Nonlinear (Power)

testing independence of errors
TestingIndependence of Errors
  • Scatter-plot i versus
  • Should be no visible trend
  • Example from our curve fit:
more on testing independence
More on Testing Independence
  • May be useful to plot error residuals versus experiment number
    • In previous example, this gives same plot except for x scaling
  • No foolproof tests
    • “Independence” test really disproves particular dependence
    • Maybe next test will show different dependence
testing for normal errors
Testing for Normal Errors
  • Prepare quantile-quantile plot of errors
  • Example for our regression:
testing for constant standard deviation
Testing for Constant Standard Deviation
  • Tongue-twister: homoscedasticity
  • Return to independence plot
  • Look for trend in spread
  • Example:
linear regression can be misleading
Linear RegressionCan Be Misleading
  • Regression throws away some information about the data
    • To allow more compact summarization
  • Sometimes vital characteristics are thrown away
    • Often, looking at data plots can tell you whether you will have a problem
example of misleading regression
Example ofMisleading Regression

I II III IV

x y x y x y x y

10 8.04 10 9.14 10 7.46 8 6.58

8 6.95 8 8.14 8 6.77 8 5.76

13 7.58 13 8.74 13 12.74 8 7.71

9 8.81 9 8.77 9 7.11 8 8.84

11 8.33 11 9.26 11 7.81 8 8.47

14 9.96 14 8.10 14 8.84 8 7.04

6 7.24 6 6.13 6 6.08 8 5.25

4 4.26 4 3.10 4 5.39 19 12.50

12 10.84 12 9.13 12 8.15 8 5.56

7 4.82 7 7.26 7 6.42 8 7.91

5 5.68 5 4.74 5 5.73 8 6.89

what does regression tell us about these data sets
What Does Regression Tell Us About These Data Sets?
  • Exactly the same thing for each!
  • N = 11
  • Mean of y = 7.5
  • Y = 3 + .5 X
  • Standard error of regression is 0.118
  • All the sums of squares are the same
  • Correlation coefficient = .82
  • R2 = .67
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