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(Simple) Multiple linear regression and Nonlinear models Multiple regression One response (dependent) variable: Y More than one predictor (independent variable) variable: X 1 , X 2 , X 3 etc. – number of predictors = p Number of observations = n

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• (Simple) Multiple linear regression and Nonlinear models

Multiple regression

One response (dependent) variable: Y

More than one predictor (independent variable) variable: X1, X2, X3 etc. number of predictors = p

Number of observations = n

• Multiple regression - graphical interpretation

0 1 2 3 4 5 6 7X1

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7 8 9 10 11 12X2

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Multiple regression graphical explanation.syd

Two possible single variable models:1) yi = 0 + 1xi1 + I2) yi = 0 + 2xi2 + i

Which is a better fit?

Multiple regression - graphical interpretation

Multiple regression graphical explanation.syd

Two possible single variable models:1) yi = 0 + 1xi1 + I2) yi = 0 + 2xi2 + i

Which is a better fit?

0 1 2 3 4 5 6 7X1

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7 8 9 10 11 12X2

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P=0.02r2=0.67

P=0.61r2=0.00

• Multiple regression - graphical interpretation

Multiple regression graphical explanation.syd

Perhaps a multiple regression model work fit better:

yi = 0 + 1xi1 + 2xi2 +i

0 1 2 3 4 5 6 7X1

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X1 Y expected residual X21 4 3.02 0.98 11.52 3 4.58 -1.58 9.253 5 6.14 -1.14 9.254 9 7.7 1.3 11.25 11.5 9.26 2.24 11.96 9 10.82 -1.82 8

residual

y b b xi 0 1 i1y b b xi 0 1 i1

Multiple regression - graphical interpretation

Multiple regression graphical explanation.syd

Perhaps a multiple regression model work fit better:

yi = 0 + 1xi1 + 2xi2 +i

0 1 2 3 4 5 6 7X1

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15

Y

7 8 9 10 11 12X2

-2

-1

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3y b b xi 0 1 i1y b b xi 0 1 i1

y b b xi 0 1 i1y b b xi 0 1 i1

Residual of

• Multiple regression - graphical interpretation

Perhaps a multiple regression model work fit better:

yi = 0 + 1xi1 + 2xi2 +I Estimated by

y b b x b xi 0 1 i1 2 i2Whole Model

Summary of FitRSquareRSquare AdjRoot Mean Square ErrorMean of ResponseObservations (or Sum Wgts)

0.9994690.9991140.1006616.916667

6

Analysis of Variance

SourceModelErrorC. Total

DF235

Sum ofSquares

57.1779350.030398

57.208333

Mean Square28.58900.0101

F Ratio2821.464Prob > F

|t|

• Simple regression results

Multiple regression 1.syd

X1

X1

Y

X2

X2

X3

X3

X4

X4

Y

0.580y = 0+1x4

0.0127y = 0+1x3

0.366y = 0+1x2

• Multiple regression - partial residual plots

Multiple regression 1.syd

y = 0+1x1+2x2+3x3+ 4x4

Model Partial residual

y = 0+2x2+3x3+ 4x4 Ypartial(1)y = 0+1x1+3x3 + 4x4 Ypartial(2)y = 0+1x1+2x2 + 4x4 Ypartial(3)y = 0+1x1 +2x2 +3x3 Ypartial(4)

0 50 100 150 200 250 300 350

X1-200

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YPA

RTI

AL(

1)

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X2-30

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AL(

2)

-15 -10 -5 0 5 10 15

X3-10

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AL(

3)

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X10

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-30 -20 -10 0 10 20 30

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X30

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X40

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Partial residuals vs Xi

Raw data (Y) vs Xi

Ypartial(4)y = 0+1x1 +2x2 +3x3 Ypartial(3)y = 0+1x1+2x2 + 4x4Ypartial(2)y = 0+1x1+3x3 + 4x4Ypartial(1)y = 0+2x2+3x3+ 4x4

Partial residualModel

Ypartial(4)y = 0+1x1 +2x2 +3x3 Ypartial(3)y = 0+1x1+2x2 + 4x4Ypartial(2)y = 0+1x1+3x3 + 4x4Ypartial(1)y = 0+2x2+3x3+ 4x4

Partial residualModel

• Regression models

Linear model:

yi = 0 + 1xi1 + 2xi2 + .... + i

Sample equation:

...y b b x b xi 0 1 i1 2 i2

Partial regression coefficients H0: 1 = 0 Partial population regression coefficient

(slope) for Y on X1, holding all other Xs constant, equals zero

Example: assume Y = bird abundance, X1=Patch Area and X2=Year slope of regression of Y against patch area,

holding years constant, equals 0.

• Multiple regression plane

Bird

Abu

ndan

ce

Years Patch Area

Testing H0: i = 0

Use partial t-tests: t = bi / SEbi Compare with t-distribution with n-2 df Separate t-test for each partial

regression coefficient in model Usual logic of t-tests:

reject H0 if P < 0.05 (again this is convention dont feel tied to this)

• Overall regression model

H0: 1 = 2 = ... = 0 (all population slopes equal zero).

Test of whether overall regression equation is significant.

Use ANOVA F-test: Variation explained by regression Unexplained (residual) variation

Assumptions

Normality and homogeneity of variance for response variable (previously discussed)

Independence of observations (previously discussed)

Linearity (previously discussed) No collinearity (big deal in multiple

regression)

• Collinearity

Collinearity: predictors correlated

Assumption of no collinearity: predictor variables uncorrelated with (ie.

independent of) each other Effect of collinearity:

estimates of is and significance tests unreliable

Checks for collinearity Correlation matrix and/or SPLOM between

predictors Tolerance for each predictor:

1-r2 for regression of that predictor on all others if tolerance is low (near 0.1) then collinearity is a

problem VIF values

1/tolerance (variance inflator function) look for large values

(>10) Condition indices (not in JMP Pro)

Greater than 15 be cautious Greater than 30 a serious problem

Look at all indicators to determine extent of colinearity

• Scatterplots Scatterplot matrix (SPLOM)

pairwise plots for all variables Example: build a multiple regression model to predict total

employment using values of six independent variables. See Longley.syd MODEL total = CONSTANT + deflator + gnp + unemployment +

armforce + population + timeDEFLATOR

DE

FLAT

OR

GNP UNEMPLOY ARMFORCE POPULATN TIME

DE

FLATO

R

GN

P

GN

P

UN

EMP

LOY

UN

EM

PLOY

ARM

FOR

CE

AR

MFO

RC

E

POPU

LATN

PO

PU

LATN

DEFLATOR

TIM

E

GNP UNEMPLOY ARMFORCE POPULATN TIME

TIME

Look at relationship between predictor variables immediately you can see colinearity problems

Checks for collinearity Correlation matrix and/or SPLOM between

predictors Tolerance for each predictor:

1-r2 for regression of that predictor on all others if tolerance is low (near 0.1) then collinearity is a

problem VIF values

1/tolerance (variance inflator function) look for large values

(>10) Condition indices

Greater than 15 be cautious Greater than 30 a serious problem

Look at all indicators to determine extent of colinearity

• Condition indices1 2 3 4 5

1.00000 9.14172 12.25574 25.33661 230.42395

6 71048.08030 43275.04738

Dependent Variable TOTAL N 16 Multiple R 0.998 Squared Multiple R 0.995 Adjusted Squared Multiple R 0.992 Standard Error of Estimate 304.854

Effect Coefficient Std Error Std Coef Tolerance t P(2 Tail)CONSTANT -3.48226E+06 8.90420E+05 0.00000 . -3.91080 0.00356DEFLATOR 15.06187 84.91493 0.04628 0.00738 0.17738 0.86314GNP -0.03582 0.03349 -1.01375 0.00056 -1.06952 0.31268UNEMPLOY -2.02023 0.48840 -0.53754 0.02975 -4.13643 0.00254ARMFORCE -1.03323 0.21427 -0.20474 0.27863 -4.82199 0.00094POPULATN -0.05110 0.22607 -0.10122 0.00251 -0.22605 0.82621TIME 1829.15146 455.47850 2.47966 0.00132 4.01589 0.00304

Tolerance and Condition Indices

Longley.syz

Variance Inflator Function (VIF)

Confidence Interval for Regression Coefficients

95.0% Confidence Interval Effect Coefficient Lower Upper VIF---------+----------------------------------------------------------------CONSTANT -3.482259E+006 -5.496529E+006 -1.467988E+006 .DEFLATOR 15.061872 -177.029036 207.152780 135.532438GNP -0.035819 -0.111581 0.039943 1,788.513483UNEMPLOY -2.020230 -3.125067 -0.915393 33.618891ARMFORCE -1.033227 -1.517949 -0.548505 3.588930POPULATN -0.051104 -0.562517 0.460309 399.151022TIME 1,829.151465 798.787513 2,859.515416 758.980597

• Solutions to collinearity

Simplest - Drop redundant (correlated) predictors

Principal components regression potentially useful

Best model?

Model that best fits the data with fewest predictors

Criteria for comparing fit of different models: r2 generally unsuitable adjusted r2 better Mallows Cp better AIC Best lower values indicate better fit

• Explained variance

r2

proportion of variation in Y explained by linear relationship with X1, X2 etc.

SS RegressionSS Total

Screening models All subsets

recommended many models if many predictors ( a big problem)

Automated stepwise selection: forward, backward, stepwise NOT recommended unless you get the same

model both ways Check AIC values Hierarchical partitioning

contribution of each predictor to r2

• Model comparison (simple version)

Fit full model: y = 0+1x1+2x2+3x3+

Fit reduced models (e.g.): y = 0+2x2+3x3+

Compare

Multiple regression 1

X1

X1

X2 X3 X4 Y

X1

X2 X2

X3 X3

X4 X4

X1

Y

X2 X3 X4 Y

Y

y = 0+1x1+2x2+3x3+ 4x4

Any evidence of Colinearity?

Model Building

• Again check for colinearity

Compare Models using AIC

Model 1:

AIC 78.67 Corrected AIC 85.67

Model 2

AIC 77.06 Corrected AIC 81.67

y = 0+1x1+2x2+3x3+ 4x4

y = 0+1x1+2x2+3x3

• Formally: Akaike information criterion (AIC, AICc)

Sometimes the following equation is used: AIC = 2k + n[ln(RSS/n)]

where, k = number of fitted parametersn = number of observations

= residual sum of squares (RSS) / AICc = corrected for small sample sizeLower score means better fit

ln 2 1 2 1ln 2 1 2 1 2 1

AIC:AICc:

Model SelectionAll Possible Models

Ordered up to best 4 models up to 4 terms per model.

ModelX1X3X2X4X1,X2X1,X3X1,X4X3,X4X1,X2,X3X1,X2,X4X1,X3,X4X2,X3,X4X1,X2,X3,X4

Number1111222233334

RSquare0.97020.31340.04820.01840.99630.97670.97180.33460.99980.99640.97890.34400.9998

RMSE17.556184.260999.2053100.7486.3536

16.012117.591385.49731.59036.4809

15.740187.67651.6295

AICc168.292227.895234.100234.686131.774166.899170.473230.55481.6718135.060168.780234.04285.6721

BIC169.525229.129235.333235.919132.695167.819171.394231.47581.7786135.167168.887234.14984.3388

• How important is each predictor variable to the model?

Compare models sequential sum of squaresModel Adjusted r2

y = 0+1x1+2x2+3x3+ 4x4y = 0+1x1+2x2+3x3y = 0+1x1+2x2y = 0+1x1

y = 0+1x1+2x2+3x3+ 4x4For reference the output from the full model

• y = 0+1x1+2x2+3x3+ 4x4For reference the output from the full model

Compare models sequential sum of squares

0.968440.027430.00387-0.00001

Contribution to Model r2

0.96844y = 0+1x10.99587y = 0+1x1+2x20.99974y = 0+1x1+2x2+3x30.99973y = 0+1x1+2x2+3x3+ 4x4

0.968440.027430.00387-0.00001

Contribution to Model r2

0.96844y = 0+1x10.99587y = 0+1x1+2x20.99974y = 0+1x1+2x2+3x30.99973y = 0+1x1+2x2+3x3+ 4x4

(Simple) Non-linear regression models

• Non-linear regression

Use when you cannot easily linearize a relationship (that is clearly non-linear_

One response (dependent) variable: Y

One predictor (independent variable) variable: X1

Non-linear functions (of many types)

Regression models

Linear model:

yi = 0 + 1x1 +

Non - Linear model (one of many possible):

yi = 0 + 1x12 +

• Non-linear regression What is the hypothesis??

This is a very big question- lets come back to this What does r2 mean??

In linear regression it is the explained variance divided by total variance

In non-linear it is the same but variance explained can be calculated in two ways

Based on

Based on

2i

y2)( yyi

Raw r2

Mean corrected r2

Non-linear regression

What is the hypothesis??

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• Non-linear regression (for example)

B*Exp(c*x)ay Fit Curve

Model ComparisonModelExponential 3P

AICc81.089952

BIC79.922153

SSE87.897377

MSE7.3247814

RMSE2.7064333

R-Square0.9729491

Plot

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0 5 10 15X

Exponential 3P

Parameter EstimatesParameterAsymptoteScaleGrowth Rate

Estimate1.76096131.57943840.2293354

Std Error1.95590910.78590040.032577

Lower 95%-2.072550.039102

0.1654857

Upper 95%5.59447273.11977480.2931851

What are the hypotheses?

Non-linear regression (many models might be adequate)

What are the hypotheses?

YExponential 2p: Y = a*Exp(b*X)

Exponential 3p: Y = a+b*Exp(c*X)

Polynomial cubic: Y = a+b*X+c*X2+d*X3

• What are the hypotheses?

Exponential 2p: Y = a*Exp(b*X)

Exponential 3p: Y = a+b*Exp(c*X)

Polynomial cubic: Y = a+b*X+c*X2+d*X3

abc

ab

abcd

Comparing regression Models Evaluate assumptions - sometimes (like in the examples

here) there are violations Simple (but not always correct) - compare adjusted r2 Problem: what counts??

Particularly problematic when there are differences in number of estimated parameters

One solution: compared added fit to expected added fit (because of increased numbers of parameters) One major restriction: models that are nested are

easier to compare Means that the general form is the same or can be made

the same simply by modifying parameter values

• Non-linear regression (many models might be adequate)

What are the hypotheses?

Fit Curve

Model ComparisonModelExponential 2PExponential 3PCubic

AICc78.06818281.08995286.847655

AICc Weight0.810952

0.17898890.010059

.2 .4 .6 .8 BIC78.01051579.92215383.72124

SSE92.69032487.89737794.528911

MSE7.13002497.32478148.5935373

RMSE2.67021062.70643332.9314736

R-Square0.971474

0.97294910.9709082

Plot

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50

Y

0 5 10 15X

Exponential 2p: Y = a*Exp(b*X)

Exponential 3p: Y = a+b*Exp(c*X)

Polynomial cubic: Y = a+b*X+c*X2+d*X3

Multiple and Non-Linear Regression

Be careful! Know what your hypotheses are Understand how to build models to test your

hypotheses Understand statistical output you may be