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Data Mining_Anoop Chaturvedi

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Swayam Prabha

Course Title

Multivariate Data Mining- Methods and Applications

Lecture 07

Properties of Estimators and Model Selection Criterion

By

Anoop Chaturvedi

Department of Statistics, University of Allahabad

Prayagraj (India)

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Slides can be downloaded from https://sites.google.com/view/anoopchaturvedi/swayam-prabha

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S.S.

df

M.S.S.

F ratio

ESS

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RSS

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TSS

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Example: mtcars dataset built in R has 32 0bservations on 11 variables. Description of variables:

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1

mpg

Miles/(US) gallon

2

cyl

Number of cylinders

3

disp

Displacement (cu.in.)

4

hp

Gross horsepower (dependent variable)

5

drat

Rear axle ratio

6

wt

Weight (1000 lbs)

7

qsec

1/4 mile time

8

vs

Engine (0 = V-shaped, 1 = straight)

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am

Transmission (0 = automatic, 1 = manual)

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gear

Number of forward gears

11

carb

Number of carburettors

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mpg

hp

drat

wt

qsec

Mazda RX4

21.0

110

3.90

2.620

16.46

Mazda RX4 Wag

21.0

110

3.90

2.875

17.02

Datsun 710

22.8

93

3.85

2.320

18.61

Hornet 4 Drive

21.4

110

3.08

3.215

19.44

Hornet Sportabout

18.7

175

3.15

3.440

17.02

First five observations on five variables of interest

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Multiple Regression using R: lm() function

hp~mpg+wt+drat+qsec

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Estimate

Std. Error

t value

Pr(>|t|)

Intercept

473.779

105.213

4.503

0.000116*

mpg

2.877

2.381

1.209

0.237319

wt

26.037

13.514

1.927

0.064600

drat

4.819

15.952

0.302

0.764910

qsec

-20.751

3.993

5.197

1.79 e-05*

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Residual standard error: 32.25 on 27 degrees of freedom

Multiple R-squared: 0.8073,

Adjusted R-squared: 0.7787

F-statistic: 28.27 on 4 and 27 DF

p-value: 2.647e-09

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