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BUS 173
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Chap 3-1
Chapter 3
Describing Data: Numerical
Instructor: Nahid Farnaz (Nhn)North South University
BUS173: Applied Statistics
Chap 3-2
Measures of Central Tendency
Central Tendency
Mean Median Mode
n
xx
n
1ii
Overview
Midpoint of ranked values
Most frequently observed value
Arithmetic average
Chap 3-3
Arithmetic Mean
The arithmetic mean (mean) is the most common measure of central tendency
For a population of N values:
For a sample of size n:
Sample sizen
xxx
n
xx n21
n
1ii
Observed
values
N
xxx
N
xμ N21
N
1ii
Population size
Population values
Chap 3-4
Arithmetic Mean
The most common measure of central tendency Mean = sum of values divided by the number of values Affected by extreme values (outliers)
(continued)
0 1 2 3 4 5 6 7 8 9 10
Mean = 3
0 1 2 3 4 5 6 7 8 9 10
Mean = 4
35
15
5
54321
4
5
20
5
104321
Chap 3-5
Median
In an ordered list, the median is the “middle” number (50% above, 50% below)
Not affected by extreme values
0 1 2 3 4 5 6 7 8 9 10
Median = 3
0 1 2 3 4 5 6 7 8 9 10
Median = 3
Chap 3-6
Finding the Median
The location of the median:
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 two middle numbers
Note that is not the value of the median, only the
position of the median in the ranked data
dataorderedtheinposition2
1npositionMedian
2
1n
Chap 3-7
Mode
A measure of central tendency Value that occurs most often Not affected by extreme values Used for either numerical or categorical data There may may be no mode There may be several modes
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
Mode = 9
0 1 2 3 4 5 6
No Mode
Chap 3-8
Mean is generally used, unless extreme values (outliers) exist
Then median is often used, since the median is not sensitive to extreme values. Example: Median home prices may be
reported for a region – less sensitive to outliers
Which measure of location is the “best”?
Chap 3-9
Shape of a Distribution
Describes how data are distributed Measures of shape
Symmetric or skewed
Mean = Median Mean < Median Median < Mean
Right-SkewedLeft-Skewed Symmetric
Exercise 3.2
A department store manager is interested in the number of complaints received by the customer service dept. about the quality of electrical products sold. Records over a 5 week period show the following number of complaints for each week:
13, 15, 8, 16, 8
a.Compute the mean number of weekly complaints
b.Compute the median
c.Find the mode
Ans: a.12; b.13; c.8
Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Chap 3-10
Ex. 3.6
The demand for bottled water increases during the hurricane season in Florida. A random sample of 7 hours showed that the following numbers of 1 gallon bottles were sold in one store:
40, 55, 62, 43, 50, 60, 65.
a. Describe the central tendency of the data
b. Comment on symmetry or skewness
Ans: Mean=53.57; no unique mode; median=55; left skewed
Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Chap 3-11
Chap 3-12
Same center, different variation
Measures of Variability
Variation
Variance Standard Deviation
Coefficient of Variation
Range Interquartile Range
Measures of variation give information on the spread or variability of the data values.
Chap 3-13
Range
Simplest measure of variation Difference between the largest and the smallest
observations:
Range = Xlargest – Xsmallest
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
Range = 14 - 1 = 13
Example:
Chap 3-14
Ignores the way in which data are distributed
Sensitive to outliers
7 8 9 10 11 12
Range = 12 - 7 = 5
7 8 9 10 11 12
Range = 12 - 7 = 5
Disadvantages of the Range
1,1,1,1,1,1,1,1,1,1,1,2,2,2,2,2,2,2,2,3,3,3,3,4,5
1,1,1,1,1,1,1,1,1,1,1,2,2,2,2,2,2,2,2,3,3,3,3,4,120
Range = 5 - 1 = 4
Range = 120 - 1 = 119
Chap 3-15
Interquartile Range
Can eliminate some outlier problems by using the interquartile range
Eliminate high- and low-valued observations and calculate the range of the middle 50% of the data
Interquartile range = 3rd quartile – 1st quartile
IQR = Q3 – Q1
Chap 3-16
Quartile Formulas
Find a quartile by determining the value in the appropriate position in the ranked data, where
First quartile position: Q1 = 0.25(n+1)
Second quartile position: Q2 = 0.50(n+1) (the median position)
Third quartile position: Q3 = 0.75(n+1)
where n is the number of observed values
Chap 3-17
Interquartile Range
Median(Q2)
XmaximumX
minimum Q1 Q3
Example:
25% 25% 25% 25%
12 30 45 57 70
Interquartile range = 57 – 30 = 27
Five number summary:Minimum < Q1 < Median Q2 < Q3 < Maximum
Chap 3-18
(n = 9)
Q1 = is in the 0.25(9+1) = 2.5 position of the ranked data
so use the value half way between the 2nd and 3rd values,
so Q1 = 12.5
Quartiles
Sample Ranked Data: 11 12 13 16 16 17 18 21 22
Example: Find the first quartile
Example 3.4: Waiting Times at Gilotti’s Grocery (five number summary)
A stem and leaf display for a sample of 25 waiting times (in seconds) is given below. Compute the 5 number summary.Calculate and interpret IQR
Minutes N = 25
Stem Unit =10.0; Leaf Unit = 1.0
Chap 3-19
1 1 2 4 6 7 8 8 9 9
2 1 2 2 2 4 6 8 9 9
3 0 1 2 3 4
4 0 2
Chap 3-20
Average of squared deviations of values from the mean
Population variance:
Population Variance
Where = population mean
N = population size
xi = ith value of the variable x
μ
Chap 3-21
Average (approximately) of squared deviations of values from the mean
Sample variance:
Sample Variance
1-n
)x(xs
n
1i
2i
2
Where = sample mean
n = sample size
Xi = ith value of the variable X
X
Chap 3-22
Population Standard Deviation
Most commonly used measure of variation Shows variation about the mean Has the same units as the original data
Population standard deviation:
Chap 3-23
Sample Standard Deviation
Most commonly used measure of variation Shows variation about the mean Has the same units as the original data
Sample standard deviation:
1-n
)x(xS
n
1i
2i
Chap 3-24
Example:Sample Standard Deviation
Sample Data (xi) : 10 12 14 15 17 18 18 24
n = 8 Mean = x = 16
4.24267
126
18
16)(2416)(1416)(1216)(10
1n
)x(24)x(14)x(12)X(10s
2222
2222
A measure of the “average” scatter around the mean
Example 3.5
A professor teaches two large sections of marketing and randomly selects a sample of test scores from both sections. Find the range and standard deviation from each sample:
Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Chap 3-25
Section 1 50 60 70 80 90
Section 2 72 68 70 74 66
Chap 3-26
This theorem established data intervals for any data set, regardless of the shape and distribution
For any population with mean μ and standard deviation σ , and k > 1 , the percentage of observations that fall within the interval
[μ kσ]
Is at least
Chebyshev’s Theorem
)]%(1/k100[1 2
Chap 3-27
Regardless of how the data are distributed, at least (1 - 1/k2) of the values will fall within k standard deviations of the mean (for k > 1)
Examples:
(1 - 1/12) = 0% ……..... k=1 (μ ± 1σ)
(1 - 1/22) = 75% …........ k=2 (μ ± 2σ)
(1 - 1/32) = 89% ………. k=3 (μ ± 3σ)
Chebyshev’s Theorem
withinAt least
(continued)
Chap 3-28
If the data distribution is bell-shaped, then the interval:
contains about 68% of the values in the population or the sample
The Empirical Rule
1σμ
μ
68%
1σμ
Chap 3-29
contains about 95% of the values in the population or the sample
contains about 99.7% of the values in the population or the sample
The Empirical Rule
2σμ
3σμ
3σμ
99.7%95%
2σμ
Exercise 3.18
Use Chebychev’s theorem to approximate each of the following observations if the mean is 250 and S.D. is 20. Approximately what proportion of the observations is
a)Between 190 and 310?
b)Between 210 and 290?
c)Between 230 and 270?
Ans: a) 88.9% k=3; b) 75% k=2; c) 0% k=1
Chap 3-30
Chap 3-31
Coefficient of Variation
Measures relative variation Always in percentage (%) Shows standard deviation relative to mean Can be used to compare two or more sets of
data measured in different units
100%x
sCV
Chap 3-32
Comparing Coefficient of Variation
Stock A: Average price last year = $50 Standard deviation = $5
Stock B: Average price last year = $100 Standard deviation = $5
Both stocks have the same standard deviation, but stock B is less variable relative to its price
10%100%$50
$5100%
x
sCVA
5%100%$100
$5100%
x
sCVB
Chap 3-33
The Sample Covariance The covariance measures the strength of the linear relationship
between two variables
The population covariance:
The sample covariance:
Only concerned with the strength of the relationship No causal effect is implied
N
))(y(xy),(xCov
N
1iyixi
xy
1n
)y)(yx(xsy),(xCov
n
1iii
xy
Chap 3-34
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
Cov(x,y) = 0 x and y are independent
Interpreting Covariance
Chap 3-35
Correlation Coefficient
Measures the relative strength of the linear relationship between two variables
Population correlation coefficient:
Sample correlation coefficient:
YX ss
y),(xCovr
YXσσ
y),(xCovρ
Chap 3-36
Features of Correlation Coefficient, r
Unit free
Ranges between –1 and 1
The closer to –1, the stronger the negative linear
relationship
The closer to 1, the stronger the positive linear
relationship
The closer to 0, the weaker any positive linear
relationship
Chap 3-37
Scatter Plots of Data with Various Correlation Coefficients
Y
X
Y
X
Y
X
Y
X
Y
X
r = -1 r = -.6 r = 0
r = +.3r = +1
Y
Xr = 0
Chap 3-38
Interpreting the Result
r = .733
There is a relatively strong positive linear relationship between test score #1 and test score #2
Students who scored high on the first test tended to score high on second test
Scatter Plot of Test Scores
70
75
80
85
90
95
100
70 75 80 85 90 95 100
Test #1 ScoreT
est
#2 S
core
Example 3.13 (Covariance and Correlation Coefficient)
Rising Hills Manufacturing Inc. wishes to study the relationship between the number of workers, X, and the number of tables produced, Y. it has obtained a random sample of 10 hours of production. The following (x,y) combinations of points were obtained:
(12,20) (30,60) (15,27) (24,50) (14,21)
(18,30) (28,61) (26,54) (19,32) (27,57)
Compute the covariance and correlation coefficient. Briefly discuss the relationship between X and Y.
Chap 3-39
Chap 3-40
Obtaining Linear Relationships
An equation can be fit to show the best linear relationship between two variables:
Y = β0 + β1X
Where Y is the dependent variable and X is the independent variable
Chap 3-41
Least Squares Regression
Estimates for coefficients β0 and β1 are found to minimize the sum of the squared residuals
The least-squares regression line, based on sample data, is
Where b1 is the slope of the line and b0 is the y-intercept:
xbby 10ˆ
x
y
2x
1 s
sr
s
y)Cov(x,b xbyb 10
Example 3.15
Using the answers from Example 3.13 (Rising Hills Inc.), compute the sample regression coefficients b1 and b0.
What is the sample regression line?
Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Chap 3-42