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Lecture (3) Lecture (3) Description of Description of Central Tendency Central Tendency

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Lecture (3). Description of Central Tendency. Hydrological Records. Population vs. Sample Notation. Different Types of Means or Averages. Arithmetic Geometric Harmonic Quadratic Consider a sample of n observations, X1, X2, …, Xi, …, Xn which can be grouped into k classes - PowerPoint PPT Presentation

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Page 1: Lecture (3)

Lecture (3)Lecture (3)

Description of Central Description of Central TendencyTendency

Page 2: Lecture (3)

Hydrological Records Hydrological Records

Page 3: Lecture (3)

Population vs. Sample Notation

Population Vs Sample

World People Arabs

Infinite Record (i.e. very long)

selected year

Page 4: Lecture (3)

Different Types of Means or AveragesDifferent Types of Means or Averages

 

ArithmeticGeometricHarmonicQuadratic

Consider a sample of n observations, X1, X2, …, Xi, …, Xn which can be grouped into k classes with class marks x1,x2,…, xi,…, xk with corresponding absolute frequencies, f1,f2,…,fi,…,fk.

Page 5: Lecture (3)

Arithmetic MeanArithmetic Mean

 

1 2 31

1 1 2 2 3 31

1

1 1...

1 1...

n

n ii

k

k k i iki

ii

X = X X X X Xn n

and

x = f x f x f x f x f xn

f

x X

X

tX1

X2 Xi Xn

Page 6: Lecture (3)

The Short Cut MethodThe Short Cut Method

 

1

1

k

i i ji

k

ii

j

f x x

f

x x

Assume the mean is <x>=xjCalculate the deviation from the assumed mean, (xi-xj)

1

n

i ji

j

X X

n

X X

Page 7: Lecture (3)

Geometric MeanGeometric Mean

1 2 2 1

1 2 31

1 2 31

. . ...

. . ...

k

ik ii

nn n

g n ii

nff ff f fng n i

i

X = X X X X X

and

x = x x x x x

Page 8: Lecture (3)

Geometric Mean (cont.)Geometric Mean (cont.)

1

1

1 2 31

1log

log

1 1 2 2 3 3

1

1

1log

log

1 1log log log log ... log log

1log log log log ... log

1log log

n

ig i

k

i ig i

n

g n ii

XX n

g

g k nk

ii

k

g i ii

f xx n

g

X X X X X Xn n

X e e

and

x f x f x f x f xf

x f xn

x e e

Page 9: Lecture (3)

Harmonic MeanHarmonic Mean

11 2 3

1 2 3

31 2

11 2 3

1 1 1 1 1...

...

...

h n

in i

kh k

k i

ik i

n nX =

X X X X X

and

f f f f nx =

f f ff fx x x x x

Page 10: Lecture (3)

Quadratic Mean (Mean Square Value)Quadratic Mean (Mean Square Value)

2 2 2 21 2 3 2

1

2 2 2 21 1 2 2 3 3 2

11 2 3

1

... 1

... 1

...

nn

q ii

kk k

q i ikik

ii

X X X XX = X

n n

and

f x f x f x f xx = f x

f f f ff

X

tX1

X2 Xi Xn

Page 11: Lecture (3)

General Formula General Formula

1

1

For ungrouped data

1

For grouped data

1

nr

ri

i

kr

ri i

i

M Xn

m f xn

, for 1

, for -1

, for 2

, for 0

h h

q q

g g

M X m x r

M X m x r

M X m x r

M X m x r

Page 12: Lecture (3)

Applications and Limitations Applications and Limitations

0

20

40

60

80

100

120

140

1601-1-1978

1-3-1978

1-5-1978

1-7-1978

1-9-1978

1-11-1978

1-1-1979

1-3-1979

1-5-1979

1-7-1979

1-9-1979

1-11-1979

Series1

Page 13: Lecture (3)

Applications and Limitations (Cont.) Applications and Limitations (Cont.)

Page 14: Lecture (3)

Applications and Limitations (Cont.) Applications and Limitations (Cont.)

Flow parallel to the layers

Flow perpendicular to the layers

1

1h n

i i

nK

K

1

1 n

a ii

K Kn

Page 15: Lecture (3)

Applications and Limitations (Cont.) Applications and Limitations (Cont.)

22

1 1

1 1n n

i ii i

Y X Xn n

Quadratic mean describes dispersion, spread or scatter around the mean, and is known as the standard deviation from the mean.

Page 16: Lecture (3)

Median

• Any value M for which at least 50% of all observations are at or above M and at least 50% are at or below M.

Page 17: Lecture (3)

Median Estimation

Order all observations from smallest to largest.If the number of observations is odd, it is the “middle”

object, namely the [(n+1)/2]th observation.For n = 61, it is the 31st

If the number of observations is even then, to get a unique value, take the average of the (n/2)th and the (n/2 +1)th observation. For = 60, it is the average of the 30th and the 31st observation.

Page 18: Lecture (3)

The median has “nice” properties

• Easy to understand (½ data above, ½ data below)

• Resistant measure of central tendency (location) not affected by extreme (unusual) observations.

Page 19: Lecture (3)

Percentiles and Quartiles Percentiles and Quartiles

In the cumulative distribution diagram, the range is from 0 to 100%.If this range is divided into a hundred equal parts. The projection of these parts on the x-axis are percentiles and denoted by, X_0.01, X_0.02,…, X_0.99.

0

0.2

0.4

0.6

0.8

1

-3 -2 -1 0 1 2 3

x

F(x)

f(x)

F(x) or f(x)

Page 20: Lecture (3)

Percentiles, Quartiles and Median Percentiles, Quartiles and Median (Cont.)(Cont.)

The 25th and 75th percentiles correspond to the first and third quartiles.

Median (Xm): it is the second quartile, X_0.50, divides the set of

observations into two numerically equal groups.

Median: geometrically is the value that divides the frequency

histogram into two parts having equal areas.

Page 21: Lecture (3)

Graphical Representation Graphical Representation

0

0.2

0.4

0.6

0.8

1

-3 -2 -1 0 1 2 3

x

F(x)

f(x)

F(x) or f(x)

X_0.25

X_0.50 X_0.75

Page 22: Lecture (3)

ModeModeThe mode is the variate that corresponds to the largest ordinate of a frequency curve.

Frequency distributions can be described as:

Uni-modal, bi-model, multi-model: if it has one, two or more modes.

Page 23: Lecture (3)

Mode in a Histogram

1. Mode(s)2. Median3. Mean

0

2

46

810

1214

16

1820

1 2 3 4 5 6 7

Page 24: Lecture (3)

Four Rules of Summation

naaaa ...

n

naan

i

1

Page 25: Lecture (3)

Four Rules of Summation

nn XXXaaXaXaX ...... 2121

n

ii

n

ii XaaX

11

Page 26: Lecture (3)

Four Rules of Summation

naXXX

aXaXaX

n

n

)...(

)(...)()(

21

21

naXaXn

ii

n

ii

11

)(

Page 27: Lecture (3)

Four Rules of Summation

)...()...(

)(...)()(

2121

2211

nn

nn

YYYXXX

YXYXYX

n

ii

n

ii

n

iii YXYX

111

)(

Page 28: Lecture (3)

Excel Application

• See Excel

Page 29: Lecture (3)

Mean, Median, Mode

• Use AVERAGE or AVERAGEA to calculate the arithmetic meanCell =AVERAGE(number1, number2, etc.)

• Use MEDIAN to return the middle numberCell =MEDIAN(number1, number2, etc)

• Use MODE to return the most common valueCell =MODE(number1, number2, etc)

Page 30: Lecture (3)

Geometric Mean

• Use GEOMEAN to calculate the geometric meanCell =GEOMEAN (number1, number2,

etc.)

Page 31: Lecture (3)

Percentiles and Quartiles

• Use PERCENTILE to return the kth percentile of a data set Cell =PERCENTILE(array, percentile)– Percentile argument is a value between 0 and

1

• Use QUARTILE to return the given quartile of a data set

• Cell =QUARTILE(array, quart)– Quart is 1, 2, 3 or 4– IQR = Q3-Q1

• May return different values to statistical package