Lecture 11: Covariance and Correlation - Astronomybelz/phys3719/lecture11.pdf · 16 February 2011...

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Transcript of Lecture 11: Covariance and Correlation - Astronomybelz/phys3719/lecture11.pdf · 16 February 2011...

Lecture 11: Covariance and Correlation

Physics 3719Spring Semester 2011

16 February 2011 Physics 3719, Lecture 11 2

Lab 3 Experiments

● Gravitational Constant G Jensen, Blackmon (AM)● Speed of Light C Ramirez, Thomas (PM)● Electron Charge-to-Mass Ratio e/m Brown, Blatter (PM)● Millikan Oil Drop Experiment Baehr, Martineau (AM)● Photoelectric Effect Peterson, Ingebretsen (PM)● Hydrogen Balmer Spectrum Gibbs, Baird (PM)● Franck-Hertz Experiment Rowley, Bernal (AM)● High-Res Optical Spectroscopy Richards, Topham (AM)

16 February 2011 Physics 3719, Lecture 11 3

16 February 2011 Physics 3719, Lecture 11 4

“Old” Terms

New Term

“Covariance” - form intuitive - tends to vanish if x & y vary independently - reduces to former result if no correlation

16 February 2011 Physics 3719, Lecture 11 5

Zero Covariance

Positive Covariance Negative Covariance

Covariance:

16 February 2011 Physics 3719, Lecture 11 6

Example: Determine the area of a rectangle, with uncertainty, for a series of measurements of its length and width.

16 February 2011 Physics 3719, Lecture 11 7

Example: Determine the area of a rectangle, with uncertainty, for a series of measurements of its length and width.

16 February 2011 Physics 3719, Lecture 11 8

Uncorrelated Errors

● L = 1.96

● W = 1.25

● LW

= 0.14

● (calculated) = 21.3

● (w/o

LW) = 20.8

● (direct) = 20.6

16 February 2011 Physics 3719, Lecture 11 9

Correlated Errors

● L = 1.78

● W = 1.25

● LW

= 1.67

● (calculated) = 25.2

● (w/o

LW) = 19.2

● (direct) = 25.3

16 February 2011 Physics 3719, Lecture 11 10

Correlated Errors

● L = 1.78

● W = 1.25

● LW

= 1.67

● (calculated) = 25.2

● (w/o

LW) = 19.2

● (direct) = 25.3

We need LW

term!

16 February 2011 Physics 3719, Lecture 11 11

Quantifying Correlation in terms of Covariance

● We want to know if two variables (not uncertainties!) are likely to be linearly related.

● e.g. Shoe size and height

● Introduce “coefficient of linear correlation” r:

Shoe Size

Heig

ht

(cm

)

perfect anticorrelation

perfect correlation

16 February 2011 Physics 3719, Lecture 11 12

Look at points from earlier example:

uncorrelated

correlated

16 February 2011 Physics 3719, Lecture 11 13

Significance of r Depends on N

points

16 February 2011 Physics 3719, Lecture 11 14

Example

16 February 2011 Physics 3719, Lecture 11 15

Joke/Rating Correlation Analysis

● Mean x = 25.8● Mean y = 3.98

16 February 2011 Physics 3719, Lecture 11 16

Joke/Rating Correlation Analysis

● Mean x = 25.8● Mean y = 3.98

● x = 9.7857

● y = 0.617738

● xy

= -4.704

16 February 2011 Physics 3719, Lecture 11 17

Joke/Rating Correlation Analysis

● Mean x = 25.8● Mean y = 3.98

● x = 9.7857

● y = 0.617738

● xy

= -4.704

● R = -0.778164

● Pchance

~ 15%