


The Mean and Standard Deviation Lecture 2

The Mean and Standard Deviation 

Mean
– the average for a data set

Median does not use all information

Calculate the mean by

Notation

X_{i} is a data point, or an observation

n is the total number of observations

i is an index number

S is the summation symbol

Mean is central tendency; however, it is sensitive to
outliers

Mode – the data point that occurs most frequently

If the probability distribution is symmetric, then
the mean = mode = median

If the probability distribution is skewed, then the
mean does not equal the mode and the mode does not equal the
median

Example

Unordered: 10 32 5 6 7 5 4 5

Ordered: 4 5 5 5 6 7 10 32

The sum of the numbers is 74

Statistics

The mean is 74 / 8 = 9.25

The mode is 5

The median is (5 + 6)/2 = 5.5

Thus, the distribution is skewed

Standard
Deviation – how spread out the distribution is

Uses all the data points

The s^{2}
is the variance

The hat means it is estimated

n – 1 is called the degrees of freedom

We are calculating (estimating) the variance, then we
lose one piece of information

This is the sample variance

Population – all data that is included in your
analysis

Maybe too costly, or too large, etc to collect
population data

Sample – randomly select out of the population

The population variance is:

Notice – there is no hat; we have all data points
and can calculate the population variance; it does not have to be
estimated!

It is easy to calculate the sample variance from the
population variance and vice versa


Usually rare to have the whole population data, so
sample is always used

The population variance is written as:

Very easy to derive

The trick to the derivation

S is a linear operator

X bar and 2 are constant and can be distributed out

Calculate the variance for the sample
Observations 
X_{i}
–


5 
5
– 4.6 = 0.4 
0.16 
6 
6 – 4.6 = 1.4 
1.96 
3 
3
– 4.6 = 1.6 
2.56 
5 
5
– 4.6 = 0.4 
0.16 
4 
4
– 4.6 = 0.6 
0.36 


5.2 


Variance has one problem. If data is in $’s, then
units for variance is $^{2}

Take the standard deviation (SD)

Standard deviation has the same units as the mean and
data

Probability Distributions 

Statistics
has many probability distributions

At least 20 distributions are popular

The most common is the Normal or Gaussian Distribution

“Bell shaped curve”

The mean and standard deviation can completely
describe this distribution

Normal distribution – as the sample size increases
to infinity, many of the other distributions become normal

Confidence intervals

From the last example,
=4.6
and s
= 1.141

68% of the data lies between

[4.6 – 1.141(1), 4.6 + 1.141(1)] = [3.46, 5.74]

95% of the data lies between

[4.6 – 1.141(2), 4.6 + 1.141(2)] = [2.32 6.88]

99% of the data lies between

[4.6 – 1.141(3), 4.6 + 1.141(3)] = [1.18, 8.02]

Data Transformations 

If you have a positively skewed distribution, then use
a transformation to make distribution “more symmetric.”

An example of a positively skewed distribution

Use natural logarithm

This function flattens the distribution
Data 
Natural
logarithm 

. 
. 

45 
ln45
= 3.8066 

. 
. 

50 
ln50
= 3.912 
This is
the mean 
. 
. 

100 
ln100
= 4.605 
An
outlier 

Note – the mean of the data and the mean of log of
the data will not equal

ln and exp are inverses of each other

The natural logarithm of a negatively skewed
distribution will not work

Measurement Errors 

Measurement
Errors – errors in measuring the data

Within subject (or intra subject) – if you take
another measurement on the same person, you get a different
measurement

We can measure this variability

Coefficient of Variability (CV) is

Use CV to check variability of our measurement on one
person

Between subject (or inter subject) – measurement
error on each subject in sample

Example

One person’s heart beat is 60 beats per second and
CV = 3%

Another persons’ heart beat is 80 beats per second
and CV = 10%

Each person’s heart is different

Each sample has intra and inter measurement errors


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