Fifth EditionFifth Editionsite.iugaza.edu.ps/ssafi/files/2011/02/Chapter-3...Business Statistics: A...

Business Statistics:A First Course

Fifth EditionFifth Edition

Chapter 3

Numerical Descriptive MeasuresNumerical Descriptive Measures

Business Statistics: A First Course, 5e © 2009 Prentice-Hall, Inc. Chap 3-1

Learning ObjectivesLearning Objectives

In this chapter, you learn:To describe the properties of central tendency To describe the properties of central tendency, variation, and shape in numerical data

To calculate descriptive summary measures for a population

To construct and interpret a boxplot To calculate the covariance and the coefficient of To calculate the covariance and the coefficient of

correlation


Summary DefinitionsSummary Definitions

The central tendency is the extent to which all the data values group around a typical or central valuedata values group around a typical or central value.

h i i i h f di i The variation is the amount of dispersion, or scattering, of values

The shape is the pattern of the distribution of values p pfrom the lowest value to the highest value.


Measures of Central Tendency:The MeanThe Mean

The arithmetic mean (often just called “mean”) is the most common measure of central tendency

Pronounced x-bar

For a sample of size n:n

The ith value

XXXX

X n21

n

1ii

Sample size

nnX

Observed values


Sample size Observed values

Measures of Central Tendency:The MeanThe Mean

(continued)

The most common measure of central tendency Mean = sum of values divided by the number of values Mean sum of values divided by the number of values Affected by extreme values (outliers)

0 1 2 3 4 5 6 7 8 9 10 0 1 2 3 4 5 6 7 8 9 10

Mean = 3 Mean = 4ea 3 ea

35

155

54321

4520

5104321


55 55

Measures of Central Tendency:The MedianThe Median

In an ordered array, the median is the “middle” ynumber (50% above, 50% below)

0 1 2 3 4 5 6 7 8 9 10 0 1 2 3 4 5 6 7 8 9 10

Median = 3 Median = 3

Not affected by extreme values


y

Measures of Central Tendency:Locating the MedianLocating the Median

The location of the median when the values are in numerical order (smallest to largest):

dataorderedtheinposition2

1npositionMedian

If the number of values is odd, the median is the middle number

If the number of values is even the median is the average of the If the number of values is even, the median is the average of the two middle numbers

1Note that is not the value of the median, only the position of

the median in the ranked data2

1n


Measures of Central Tendency:The ModeThe Mode

V l th t t ft Value that occurs most often Not affected by extreme values Used for either numerical or categorical data There may be no mode There may be several modes

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 0 1 2 3 4 5 6


Mode = 9 No Mode

Measures of Central Tendency:Review ExampleReview Example

House Prices: Mean: ($3,000,000/5) $2,000,000

$500,000$300 000

= $600,000 Median: middle value of ranked $300,000

$100,000$100,000

data = $300,000

Sum $3,000,000 Mode: most frequent value = $100,000


Measures of Central Tendency:Which Measure to Choose?Which Measure to Choose?

The mean is generally used, unless extreme values ( li ) i(outliers) exist.

The median is often used, since the median is not e ed a s o te used, s ce t e ed a s otsensitive to extreme values. For example, median home prices may be reported for a region; it is less sensitive to outliers.

In some situations it makes sense to report both the In some situations it makes sense to report both the mean and the median.


Measures of Central Tendency:SummarySummary

Central Tendency

Arithmetic Mean

Median Mode

n

n

XX

n

ii

1

Middle value Most n Middle value in the ordered array

frequently observed value


value

Measures of VariationMeasures of Variation

Variation

Standard Deviation

Coefficient of Variation

Range Variance

Measures of variation give Measures of variation give information on the spread or variability or ydispersion of the data values.


Same center, different variation

Measures of Variation:The RangeThe Range

Simplest measure of variationDifference between the largest and the smallest values: Difference between the largest and the smallest values:

Range = Xlargest – Xsmallest

Example:

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14

Range = 13 - 1 = 12


Range = 13 - 1 = 12

Measures of Variation:Wh Th R C B Mi l diWhy The Range Can Be Misleading

Ignores the way in which data are distributed

7 8 9 10 11 12Range = 12 - 7 = 5

7 8 9 10 11 12Range = 12 - 7 = 5

Sensitive to outliers1,1,1,1,1,1,1,1,1,1,1,2,2,2,2,2,2,2,2,3,3,3,3,4,5

Range = 5 - 1 = 4

1,1,1,1,1,1,1,1,1,1,1,2,2,2,2,2,2,2,2,3,3,3,3,4,120R 120 1 119


Range = 120 - 1 = 119

Measures of Variation:Th V iThe Variance

Average (approximately) of squared deviations of values from the meanof values from the mean

S l in

2 Sample variance: )X(XS 1i

2i

2

1-nS

Where = arithmetic mean

n = sample size

X


Xi = ith value of the variable X

Measures of Variation:The Standard DeviationThe Standard Deviation

Most commonly used measure of variation Shows variation about the meanShows variation about the mean Is the square root of the variance

Has the same units as the original data Has the same units as the original data

Sample standard deviation: )X(XS

n

1i

2i

1-nS 1i


Measures of Variation:The Standard DeviationThe Standard Deviation

Steps for Computing Standard Deviation

1. Compute the difference between each value and the mean.

2. Square each difference.3. Add the squared differences.4. Divide this total by n-1 to get the sample variance.5. Take the square root of the sample variance to get

the sample standard deviation.


Measures of Variation:Sample Standard Deviation:Calculation Example

Sample Data (Xi) : 10 12 14 15 17 18 18 24

n = 8 Mean = X = 16

)X(24)X(14)X(12)X(10 2222

1n)X(24)X(14)X(12)X(10S

2222

1816)(2416)(1416)(1216)(10 2222

4 3095130

18

A measure of the “average”


4.30957

130

gscatter around the mean

Measures of Variation:C i S d d D i iComparing Standard Deviations

Mean = 15.5Data A

S = 3.33811 12 13 14 15 16 17 18 19 20 21

11 12 13 14 15 16 17 18 19 20

Data B Mean = 15.5S = 0.92611 12 13 14 15 16 17 18 19 20

21S 0.926

Mean = 15 5Data C

11 12 13 14 15 16 17 18 19 20 21

Mean = 15.5S = 4.570

Data C


Measures of Variation:Comparing Standard DeviationsComparing Standard Deviations

Smaller standard deviation

L t d d d i tiLarger standard deviation


Measures of Variation:Summary CharacteristicsSummary Characteristics

The more the data are spread out, the greater the range, variance, and standard deviation.

The more the data are concentrated, the smaller the range, variance, and standard deviation.

If the values are all the same (no variation), all these measures will be zero.

None of these measures are ever negative.


g

Measures of Variation:The Coefficient of VariationThe Coefficient of Variation

Measures relative variationAl i t (%) Always in percentage (%)

Shows variation relative to mean Can be used to compare the variability of two or

more sets of data measured in different unitsmore sets of data measured in different units

S 100%

XSCV


X

Measures of Variation:C i C ffi i t f V i tiComparing Coefficients of Variation

Stock A: Average price last year = $50

St d d d i ti $5 Standard deviation = $5

10%100%$5100%SCV

Stock B:Both stocks have the same standard

10%100%$50

100%X

CVA

Average price last year = $100 Standard deviation = $5

standard deviation, but stock B is less variable relativevariable relative to its price

5%100%$100$5100%

XSCVB


$100X

Locating Extreme Outliers:Z ScoreZ-Score

To compute the Z-score of a data value, subtract the mean and divide by the standard deviationmean and divide by the standard deviation.

The Z-score is the number of standard deviations a data value is from the mean.

A data value is considered an extreme outlier if its Z-score is less than -3.0 or greater than +3.0.

The larger the absolute value of the Z-score, the farther the data value is from the mean.


farther the data value is from the mean.


XXZ

SZ

where X represents the data valueX is the sample meanX is the sample meanS is the sample standard deviation



Suppose the mean math SAT score is 490, with a standard deviation of 100standard deviation of 100.

Compute the Z-score for a test score of 620.

3.1130490620

XXZ 3.1100100S

Z

A score of 620 is 1.3 standard deviations above the mean and would not be considered an outlier.


Shape of a DistributionShape of a Distribution

Describes how data are distributedM f h Measures of shape Symmetric or skewed

Right-SkewedLeft-Skewed SymmetricMean = MedianMean < Median Median < Mean


General Descriptive Stats Using Microsoft ExcelMicrosoft Excel

1. Select Tools.

2 Select Data Analysis2. Select Data Analysis.

3. Select Descriptive Statistics and click OKStatistics and click OK.


General Descriptive Stats Using Microsoft ExcelMicrosoft Excel

4. Enter the cell range.

5 Check the5. Check the Summary Statistics box.

6. Click OK


Excel outputExcel output

Microsoft Excel descriptive statistics output, using the house price data:

House Prices:

$2,000,000500,000300,000100,000100,000

Chap 3-30Business Statistics: A First Course, 5e © 2009 Prentice-Hall, Inc.

Minitab OutputMinitab Output

Descriptive Statistics: House Price

TotalVariable Count Mean SE Mean StDev Variance Sum MinimumHouse Price 5 600000 357771 800000 6.40000E+11 3000000 100000

N forVariable Median Maximum Range Mode Skewness KurtosisHouse Price 300000 2000000 1900000 100000 2.01 4.13


Numerical Descriptive Measures for a PopulationMeasures for a Population

Descriptive statistics discussed previously described a sample, not the population.p , p p

Summary measures describing a population, called y g p p ,parameters, are denoted with Greek letters.

Important population parameters are the population mean, variance, and standard deviation.


Numerical Descriptive Measures for a Population: The mean µ

The population mean is the sum of the values in the population divided by the population size Nthe population divided by the population size, N

XXXX

N21

N

1ii

NNN211i

μ = population mean

N = population size

Where



Numerical Descriptive Measures For A Population: The Variance σ2For A Population: The Variance σ2

Average of squared deviations of values from the meanthe mean

P l ti i μ)(XN

2 Population variance:

N

μ)(Xσ 1i

2i

2

N

Where μ = population mean

N = population size



Numerical Descriptive Measures For A P l ti Th St d d D i tiPopulation: The Standard Deviation σ

Most commonly used measure of variation Shows variation about the meanShows variation about the mean Is the square root of the population variance

Has the same units as the original data Has the same units as the original data

Population standard deviation: μ)(XN

2i

Nσ 1i


Sample statistics versus population parameterspopulation parameters

Measure Population Sample pParameter

pStatistic

MeanMean

Variance

X

Variance

St d d

2S2

Standard Deviation S


The Empirical RuleThe Empirical Rule

The empirical rule approximates the variation of data in a bell-shaped distribution

Approximately 68% of the data in a bell shaped distribution is within 1 standard deviation of thedistribution is within 1 standard deviation of the mean or 1σμ

68%

μ


1σμ

The Empirical Rule

Approximately 95% of the data in a bell shaped

The Empirical Rule

Approximately 95% of the data in a bell-shaped distribution lies within two standard deviations of the mean, or µ ± 2σmean, or µ ± 2σ

Approximately 99 7% of the data in a bell-shaped Approximately 99.7% of the data in a bell shaped distribution lies within three standard deviations of the mean, or µ ± 3σ

99.7%95%


3σμ2σμ

Using the Empirical RuleUsing the Empirical Rule

Suppose that the variable Math SAT scores is bell-shaped with a mean of 500 and a standard deviation pof 90. Then,

68% f ll k d b 410 d 590 68% of all test takers scored between 410 and 590 (500 ± 90).

95% of all test takers scored between 320 and 680 (500 ± 180).

99.7% of all test takers scored between 230 and 770 (500 ± 270).


Chebyshev RuleChebyshev Rule

Regardless of how the data are distributed, at least (1 - 1/k2) x 100% of the values will a eas ( / ) 00% o e a uesfall within k standard deviations of the mean (for k > 1)( )

Examples:

withinAt least

(1 - 1/22) x 100% = 75% …........ k=2 (μ ± 2σ)(1 - 1/32) x 100% = 89% ………. k=3 (μ ± 3σ)


Quartile MeasuresQuartile Measures

Quartiles split the ranked data into 4 segments with an equal number of values per segment

25% 25% 25% 25%

The first quartile Q1 is the value for which 25% of the

Q1 Q2 Q3

The first quartile, Q1, is the value for which 25% of the observations are smaller and 75% are larger

Q2 is the same as the median (50% of the observations ll d 50% l )are smaller and 50% are larger)

Only 25% of the observations are greater than the third quartile


quartile

Quartile Measures:L ti Q tilLocating Quartiles

Find a quartile by determining the value in the appropriate position in the ranked data whereappropriate position in the ranked data, where

First quartile position: Q1 = (n+1)/4 ranked valueFirst quartile position: Q1 (n+1)/4 ranked value

Second quartile position: Q2 = (n+1)/2 ranked value

Third quartile position: Q3 = 3(n+1)/4 ranked value

where n is the number of observed values


Quartile Measures:Calculation RulesCalculation Rules

When calculating the ranked position use the following rulesg If the result is a whole number then it is the ranked

position to use

If the result is a fractional half (e.g. 2.5, 7.5, 8.5, etc.) then average the two corresponding data valuesthen average the two corresponding data values.

If the result is not a whole number or a fractional half If the result is not a whole number or a fractional half then round the result to the nearest integer to find the ranked position.


Quartile Measures:Locating QuartilesLocating Quartiles

Sample Data in Ordered Array: 11 12 13 16 16 17 18 21 22

(n = 9)(n = 9)Q1 is in the (9+1)/4 = 2.5 position of the ranked data

so use the value half way between the 2nd and 3rd values,

so Q = 12 5so Q1 = 12.5

Q and Q are measures of non central location


Q1 and Q3 are measures of non-central locationQ2 = median, is a measure of central tendency

Quartile MeasuresCalculating The Quartiles: ExampleCalculating The Quartiles: Example

(n = 9)

Sample Data in Ordered Array: 11 12 13 16 16 17 18 21 22

( )Q1 is in the (9+1)/4 = 2.5 position of the ranked data,

so Q1 = (12+13)/2 = 12.51 ( )

Q2 is in the (9+1)/2 = 5th position of the ranked data,Q di 16so Q2 = median = 16

Q3 is in the 3(9+1)/4 = 7.5 position of the ranked data,3

so Q3 = (18+21)/2 = 19.5Q1 and Q3 are measures of non-central location


Q1 and Q3 are measures of non central locationQ2 = median, is a measure of central tendency

Quartile Measures:The Interquartile Range (IQR)The Interquartile Range (IQR)

The IQR is Q3 – Q1 and measures the spread in the middle 50% of the datamiddle 50% of the data

The IQR is also called the midspread because it covers th iddl 50% f th d tthe middle 50% of the data

The IQR is a measure of variability that is notThe IQR is a measure of variability that is not influenced by outliers or extreme values

M lik Q Q d IQR th t t i fl d Measures like Q1, Q3, and IQR that are not influenced by outliers are called resistant measures


Calculating The Interquartile RangeRange

Example:Median

(Q2)X

maximumXminimum Q1 Q3

25% 25% 25% 25%

12 30 45 57 70

Interquartile rangeInterquartile range = 57 – 30 = 27


The Five Number SummaryThe Five Number Summary

The five numbers that help describe the center, spread and shape of data are:and shape of data are: Xsmallest

Fi t Q til (Q ) First Quartile (Q1) Median (Q2) Third Quartile (Q3) Xlargestlargest


Relationships among the five-number d di t ib ti hsummary and distribution shape

Left-Skewed Symmetric Right-SkewedMedian – X Median – X Median – XMedian – Xsmallest

>

X Median

Median – Xsmallest

≈

X Median

Median – Xsmallest

<

X MedianXlargest – Median Xlargest – Median Xlargest – Median

Q1 – Xsmallest Q1 – Xsmallest Q1 – Xsmallest

>

Xlargest – Q3

≈

Xlargest – Q3

<

Xlargest – Q3

M di Q M di Q M di QMedian – Q1

>

Median – Q1

≈

Median – Q1

<


Q3 – Median Q3 – Median Q3 – Median

Five Number Summary andThe BoxplotThe Boxplot

The Boxplot: A Graphical display of the data based on the five-number summary:based on the five number summary:

Xsmallest -- Q1 -- Median -- Q3 -- Xlargest

Example:

25% of data 25% 25% 25% of dataof data of data

Xsmallest Q1 Median Q3 Xlargest


Five Number Summary:Sh f B l tShape of Boxplots

If data are symmetric around the median then the box and central line are centered between the endpoints

X Q Median Q X

A Boxplot can be shown in either a vertical or horizontal

Xsmallest Q1 Median Q3 Xlargest

A Boxplot can be shown in either a vertical or horizontal orientation


Distribution Shape and Th B l tThe Boxplot

Right-SkewedLeft-Skewed Symmetric

Q Q QQ1 Q2 Q3 Q1 Q2 Q3Q1 Q2 Q3


Boxplot ExampleBoxplot Example

Below is a Boxplot for the following data:

0 2 2 2 3 3 4 5 5 9 27Xsmallest Q1 Q2 Q3 Xlargest

0 2 2 2 3 3 4 5 5 9 27

0 2 3 5 27

The data are right skewed as the plot depicts

0 2 3 5 270 2 3 5 27


The data are right skewed, as the plot depicts

Boxplot example showing an outlierBoxplot example showing an outlier

•The boxplot below of the same data shows the outlier value of 27 plotted separately

•A value is considered an outlier if it is more than 1.5 times the interquartile range below Q1 or above Q3

Example Boxplot Showing An Outlier

0 5 10 15 20 25 30

Sample Data


The CovarianceThe Covariance

The covariance measures the strength of the linear relationship between two numerical variables (X & Y)

The sample covariance:

)YY)(XX(n

ii 1n

))(()Y,X(cov 1i

ii

Only concerned with the strength of the relationship

N l ff t i i li d


No causal effect is implied

Interpreting CovarianceInterpreting Covariance

Covariance between two variables:cov(X,Y) > 0 X and Y tend to move in the same direction

cov(X,Y) < 0 X and Y tend to move in opposite directions( , ) pp

cov(X,Y) = 0 X and Y are independent

The covariance has a major flaw: It is not possible to determine the relative strength of the

relationship from the size of the covariance


Coefficient of CorrelationCoefficient of Correlation

Measures the relative strength of the linear relationship between two numerical variables

Sample coefficient of correlation:

n

yxnyx ii

2222

1i

ynyxnx

yyr

n

i

n

i

ii

Where and are the means of x and y-values

11 i

ii

i

x y

Question : Will outliers effect the correlation ? YES


Features of theC ffi i f C l iCoefficient of Correlation

The population coefficient of correlation is referred as ρ.

The sample coefficient of correlation is referred to as r The sample coefficient of correlation is referred to as r.

Either ρ or r have the following features:U it f Unit free

Ranges between –1 and 1

Th l t 1 th t th ti li l ti hi The closer to –1, the stronger the negative linear relationship

The closer to 1, the stronger the positive linear relationship

Th l t 0 th k th li l ti hi The closer to 0, the weaker the linear relationship


Scatter Plots of Sample Data with pVarious Coefficients of Correlation

YY Y

X XX Xr = -1 r = -.6

YYY

Y

XX X


XXr = +.3r = +1

Xr = 0

Example: Recall the scatterplot data for the heights of fathersand their sons.

Father’s Height Son’s Height64 65

Father’s Height Son’s Height74 7064 65

68 6768 7070 72

74 7075 7375 7676 7770 72

72 7576 7777 76

We decided that the father’s heights was the explanatory variableand the son’s heights was the response variable.

The average of the x terms is 71.9 and the standard deviation is 4.25

The average of the y terms is 72.1 and the standard deviation is 4.07

Examplei f ’ i ff ’ i ?Do you think that a father’s height would affect a son’s height?

W i th t i f th ’ h i ht kWe are saying that given a father’s height, can we make any determinations about the son’s height ?

The explanatory variable is : The father’s heightThe explanatory variable is : The father s heightThe response variable is : The son’s heightData Set : Father’s Height Son’s HeightData Set : g g

64 6568 6768 7068 7070 7272 7574 7075 7375 7676 7777 76

CalculationsCalculations

1010101010

1729711051975

51975,52133,51859,721,71911

2

1

2

11

i

iii

ii

ii

ii

i yxyxyx

87.0

1.7210521339.711051859

1.729.711051975

2

1

2

n

i

r

1 i

There is strong positive relationshipbetween father’s and son’s heightg

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