MA-250 Probability and Statistics Nazar Khan PUCIT Lecture 5

MA-250 Probability and Statistics

Nazar KhanPUCIT

Lecture 5

Measurement Error

• In an ideal world, if the same thing is measured several times, the same result would be obtained each time.

• In reality, there are differences.– Each result is thrown off by chance error.

Individual measurement = exact value + chance error

Measurement Error

• No matter how carefully it is made, a measurement could have been different than it is.

• If repeated, it will be different.• But how much different?– Simple answer:• Repeat the measurements.• Consider the SD

Measurement Error

• Variability in measurements reflects the variability in the chance errors

Individual measurement = exact value + chance errorSD(Measurements) = exact value + SD(chance error)

Measurement Error

• An outlier can affect the – Mean– Standard Deviation

• What if the majority data follows a normal curve?– The outliers will affect the mean and SD such that

the 68-95-99 rule might not be followed.• Solution: remove the outliers and then do

the normal approximation.

Outliers

Outliers

1SD is covering ~86% of the data, so the normal approximation cannot be used.

Outliers

1SD is covering ~68% of the data, so the normal approximation can be used now.

Outliers Removed

Bias

• Chance error changes from measurement to measurement – sometimes positive and sometimes negative.

• Bias affects all measurements in the same way.

Individual measurement = exact value + chance error + bias

below.

Dealing with bi-variate data

• So far, we have dealt with uni-variate data– One variable only– Age, Height, Income, Family Size, etc.

• How can we study relationships between 2 variables?– Relationship between height of father and height

of son– Relationship between income and education

• Answer: scatter diagrams

Can we summarize the scatter diagram?

Summarizing a Scatter Diagram

• Mean• Horizontal SD• Vertical SD

• But these statistics do not measure the strength of the association between the 2 variables.

• How can we summarize the strength of association?

Same mean and horizontal and vertical SDs but the left figure shows more association between the 2 variables.

Correlation

• Correlation measures the strength of association between 2 variables– As one increases, what happens to the other?

• Denoted by r• r=average(x in standard units* y in standard units)

Average = 0.4

How does r measure association strength?

• r=average(x in standard units* y in standard units)• When both x and y are simultaneously above or below their

means, their product in standard units is +ve.• When +ve products dominate, the average of products is +ve

(i.e., correlation r is +ve).• Similarly for –ve products.

Correlation

• r is always between 1 and -1.• r=0 implies no association between x and y.• |r|=1 implies strong linear association.– r=1 implies perfectly linear, positive association.– r=-1 implies perfectly linear, negative association.

Very hard to predict y from x

Easy to predict y from x

Negative association between x and y

Some Properties of the Correlation Coefficient

• r has no units. (Why?)– The correlation between June temperatures for

Lahore and Karachi will be the same in Celcius and Fahrenheit.

• r(x,y)=r(y,x) (Why?)

Exceptions!

Strong linear association without outlier but outlier brings r down to almost 0

r measures linear association only, not all kinds of association.

Association is not Causation!

• Correlation measures association but association is not causation.– In kids, shoe-size and reading skills have a strong

positive linear association. Does a larger foot improve your reading skills?

Summary

• Measurement Errors– Chance Error– Bias

• SD(chance errors) = SD(measurements)• Let’s us determine if an error is by chance or not.

• Correlation measures strength of linear association between 2 variables.– Between -1 and 1

• Not useful for summarizing scatter diagrams with – Outliers, or– Non-linear association.

• Association is not causation.