course review, syllabus, etc. chapter 1 – introduction chapter 2 – graphical techniques
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Quantitative Business Methods A First Course. Course review, syllabus, etc. Chapter 1 – Introduction Chapter 2 – Graphical Techniques. 3-21-05. Population and Sample. Population. Sample. Use statistics to summarize features. Use parameters to summarize features. - PowerPoint PPT PresentationTRANSCRIPT
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• Course review, syllabus, etc.
• Chapter 1 – Introduction
• Chapter 2 – Graphical Techniques
Quantitative Business Methods
A First Course
3-21-05
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Population and Sample
Population Sample
Use parameters to summarize features
Use statistics to summarize features
Inference on the population from the sample
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Some Important Definitions……
• A ___________________(or universe) is the whole collection of things under consideration.
• A ______________ is a portion of the population selected for analysis.
• A PARAMETER is a summary measure computed to describe a characteristic of the population. µ
• A STATISTIC is a summary measure computed to describe a characteristic of the sample.
Discuss examples…….. Ω
X
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Statistical Methods
•Descriptive Statistics
•Inferential Statistics
Collecting and describing data.
Drawing conclusions and/or making decisions concerning a population based only on sample data.
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• Collect Data– e.g. Survey
• Present Data– e.g. Tables and graphs
• Characterize Data– e.g. Sample Mean =
iX
n
Descriptive Statistics
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Inferential Statistics•Estimation e.g. Estimate the population mean using the sample mean.
•Hypothesis Testing e.g. Test the claim that the population mean weight is 120 pounds.
Drawing conclusion and/or making decisions concerning a population based on sample results.
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1. Write the following in scientific notation (ex. 4.63 x 102 or 4.63E02)(you may use as many significant figures as you wish)
A. 3864159831.025 B. 0.0000062514836
2. Write the following numbers in standard notation (ie. Not in scientific notation)
A. 4.3650217E10 B. 2.1097326 x 10 -6
3. Perform the following calculations, using only your calculator (try to enter it all in to your calculator).
?33
)25(6.
A ?
25
643.
B
4. Perform the following calculation without using your calculator.
?43
)45(2
Analytical Skills Inventory Exercise
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___
X
Use the following information for problems 5-9.
= 4.6 n = 10
i Xi
1 3
2 5
3 2
4 6
5 10
6 4
7 5
8 3
9 7
10 1
?#5.1
n
iiX ?.6#
1
2
n
iiX
?1
.7#
2
niX i ?.8#
___
1
XXn
ii
?.9#1
___
n
ii XX
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1. A. 3.864159831025 x 109 or 3.864E09
B. 6.2514836 x 10-6 or 6.251E-06
2. A. 43650217000
B. 0.0000021097326
3. A. 9B. 10
4. 10
5. 46
6. 274
7. 2116
8. 41.4
9. 0
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Graphical Descriptive Techniques
Graphical Descriptive Techniques
Chapter 2Chapter 2
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2.1 Introduction
Descriptive statistics involves the arrangement, summary, and presentation of data, to enable meaningful interpretation, and to support decision making.Descriptive statistics methods make use of graphical techniques numerical descriptive measures.
The methods presented apply to both the entire population the population sample
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2.2 Types of data and information
A variable - a characteristic of population or sample that is of interest for us. Cereal choice Capital expenditure The waiting time for medical services
Data - the actual values of variables Interval data are numerical observations Nominal data are categorical observations
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Types of data - examples
Interval data
Age - income55 7500042 68000
. .
. .
Age - income55 7500042 68000
. .
. .Weight gain+10+5..
Weight gain+10+5..
Nominal
Person Marital status1 married2 single3 single. .. .
Person Marital status1 married2 single3 single. .. .Computer Brand
1 IBM2 Dell3 IBM. .. .
Computer Brand1 IBM2 Dell3 IBM. .. .
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Types of data - examples
Interval data
Age - income55 7500042 68000
. .
. .
Age - income55 7500042 68000
. .
. .
Nominal data
With nominal data, all we can do is, calculate the proportion of data that falls into each category.
IBM Dell Compaq Other Total 25 11 8 6 50 50% 22% 16% 12%
IBM Dell Compaq Other Total 25 11 8 6 50 50% 22% 16% 12%
Weight gain+10+5..
Weight gain+10+5..
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2.3 Graphical Techniques forInterval Data
Example 2.1: The monthly bills of new subscribers in the first month after signing on with a telephone company. Collect data Prepare a frequency distribution Draw a histogram
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Largest observation
Collect dataBills42.1938.4529.2389.35118.04110.460.0072.8883.05
.
.
(There are 200 data points
Prepare a frequency distributionHow many classes to use?Number of observations Number of classes
Less then 50 5-750 - 200 7-9200 - 500 9-10500 - 1,000 10-111,000 – 5,000 11-135,000- 50,000 13-17More than 50,000 17-20
Class width = [Range] / [# of classes]
[119.63 - 0] / [8] = 14.95 15Largest observationLargest observation
Smallest observationSmallest observationSmallest observation_______ observation
_________observation
Example 2.1: Providing information
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0
20
40
60
80
15 30 45 60 75 90 105 120
Bills
Fre
qu
en
cy
Draw a HistogramBin Frequency
15 7130 3745 1360 975 1090 18
105 28120 14
Example 2.1: Providing information
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0
20
40
60
8015 30 45 60 75 90 10
5
120
Bills
Fre
qu
ency
What information can we extract from this histogramAbout half of all the bills are small
71+37=108 13+9+10=32
A few bills are in the middle range
Relatively,large numberof large bills
18+28+14=60
Example 2.1: Providing information
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It is often preferable to show the relative frequency (proportion) of observations falling into each class, rather than the frequency itself.
Relative frequencies should be used when the population relative frequencies are studied comparing two or more histograms the number of observations of the samples studied are
different
Class relative frequency = Class relative frequency = Class frequency
Total number of observations
Class frequency
Total number of observations
Relative frequency
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There are four typical shape characteristics
Shapes of histograms
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______________ skewed
Negatively skewed
Shapes of histograms
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A modal class is the one with the largest number of observations.
A unimodal histogram
The modal class
Modal classes
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Modal classes
A bimodal histogram
A modal class A modal class
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Bell shaped histograms
“________________________”
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Example 2.3: Comparing students’ performance Students’ performance in two statistics classes. Different in their teaching emphasis
Class A – math analysis and development of theory. Class B – applications and computer based analysis.
The final mark was recorded. Draw histograms and interpret the results.
Interpreting histograms
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Histogram
02040
50 60 70 80 90 100
Marks(Manual)
Fre
qu
en
cy
Histogram
02040
50 60 70 80 90 100
Marks(Manual)
Fre
qu
en
cy
Histogram
02040
50 60 70 80 90 100
Marks(Computer)
Fre
qu
en
cy
Histogram
02040
50 60 70 80 90 100
Marks(Computer)
Fre
qu
en
cy
Interpreting histograms
The mathematical emphasiscreates two groups, and a larger spread.
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Preliminary analysis.Original observations vs. histogram approach.Split each observation into two parts.There are several ways of doing that:
42.19 42.19
Stem Leaf 4219
Stem Leaf4 2
A stem and leaf display forExample 2.1 will use thismethod
Stem and Leaf Display
Observation:
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A stem and leaf display for Example 2.1 (See page 42 for ref)
Stem-and-Leaf Display for Bills: unit = 1.0 1|2 represents 12.0
52 0|0000000001111122222233333455555566666667788889999999 85 1|000001111233333334455555667889999 (23) 2|00001111123446667789999 92 3|001335589 83 4|12445589 75 5|33566 70 6|3458 66 7|022224556789 54 8|334457889999 42 9|00112222233344555999 22 10|001344446699 10 11|0124557889
The length of each linerepresents the _________ of the class defined by the stem.
Stem and Leaf DisplaySG Demo
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Ogives
Cumulative relative frequency
Bills
Cumulative relative frequency
Bills
Cumulative relative frequency for telephone billsCumulative Cum.Relative
Class Frequency frequency frquency0-15 71 71 71/200=.355
15-30 37 108 108/200=.54030-45 13 121 121/200=.60545-60 9 130 130/200=.65060-75 10 140 140/200=.70075-90 18 158 158/200=.79090-105 28 186 186/200=.930
105-200 14 200 200/200=1.000
Cumulative relative frequency for telephone billsCumulative Cum.Relative
Class Frequency frequency frquency0-15 71 71 71/200=.355
15-30 37 108 108/200=.54030-45 13 121 121/200=.60545-60 9 130 130/200=.65060-75 10 140 140/200=.70075-90 18 158 158/200=.79090-105 28 186 186/200=.930
105-200 14 200 200/200=1.000
15
.355
30
.540
45
.605
60
.650
75
.700
90
.790
105
.930
120
1.000
Ogives are cumulative relative frequency distributions.
Example 2.1 - continued
SG Demo: Freq Tab
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Frequency Tabulation for Bills
------------------------------------------------------------------------------------ Lower Upper Relative Cumulative Cum. Rel.Class Limit Limit Midpoint Frequency Frequency Frequency Frequency------------------------------------------------------------------------------------ at or below 0.0 8 0.0400 8 0.0400 1 0.0 15.0 7.5 63 0.3150 71 0.3550 2 15.0 30.0 22.5 37 0.1850 108 0.5400 3 30.0 45.0 37.5 13 0.0650 121 0.6050 4 45.0 60.0 52.5 9 0.0450 130 0.6500 5 60.0 75.0 67.5 10 0.0500 140 0.7000 6 75.0 90.0 82.5 18 0.0900 158 0.7900 7 90.0 105.0 97.5 28 0.1400 186 0.9300 8 105.0 120.0 112.5 14 0.0700 200 1.0000above 120.0 0 0.0000 200 1.0000------------------------------------------------------------------------------------Mean = 43.5876 Standard deviation = 38.9697
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2.4 Graphical Techniques for Nominal data
The only allowable calculation on nominal data is to count the frequency of each value of a variable.When the raw data can be naturally categorized in a meaningful manner, we can display frequencies by Bar charts – emphasize frequency of occurrences
of the different categories. Pie chart – emphasize the proportion of
occurrences of each category.
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Marketing25.3%
Finance20.6%
General management14.2%
Other11.1% Accounting
28.9%
(28.9 /100)(3600) = 1040
The Pie Chart Ex #2.4: The student placement office at a university wanted to determine the general areas of employment of last year school graduates.
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Rectangles represent each category.
The height of the rectangle represents the frequency.
The base of the rectangle is arbitrary
Bar Chart
0
10
20
30
40
50
60
70
80
1 2 3 4 5 More
Area
Fre
qu
en
cy
73
5236
64
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The Bar Chart
SG Demo: Desc-
Categ-Tab
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2.5 Describing the Relationship Between Two Variables
The relationship between two interval variables.
Example 2.7 A real estate agent wants to study the relationship
between house price and house size Twelve houses recently sold are sampled and the
size and price recorded Use graphical technique to describe the
relationship between size and price.
Size Price23 31524 22926 33527 261……………..……………..
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Solution The size (independent variable, X) affects
the price (dependent variable, Y) We use Excel to create a scatter diagram
2.5 Describing the Relationship Between Two Variables
0
100
200
300
400
0 10 20 30 40
Y
X
The greater the house siz
e,
the greater the price
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Typical Patterns of Scatter DiagramsPositive linear relationship Negative linear relationshipNo relationship
Negative nonlinear relationship
This is a weak linear relationship.A non linear relationship seems to fit the data better.
Nonlinear (concave) relationship
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Graphing the Relationship Between Two Nominal Variables
We create a contingency table.
This table lists the frequency for each combination of values of the two variables.
We can create a bar chart that represent the frequency of occurrence of each combination of values.
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Example 2.8 (Data: 2.8a)
To conduct an efficient advertisement campaign the relationship between occupation and newspapers readership is studied. The following table was created
Contingency table
Blue Collar White collar ProfessionalG&M 27 29 33Post 18 43 51Star 38 15 24Sun 37 21 18
Blue Collar White collar ProfessionalG&M 27 29 33Post 18 43 51Star 38 15 24Sun 37 21 18