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**Unformatted text preview: **Business Statistics (BUSA 3101)
Dr. Lari H. Arjomand
[email protected]
Text Book
Statistical Techniques in Business & Economics
Lind, Marchal, and Wathen
15th Ed. 2012 Slide 1 Business Statistics
s For Lecture Notes, Slides, Quizzes, Projects, Syllabus, Office Hours, Exams & Due Dates, Statistical Links, Tutorials, Bulletin Board & Much More referee to my website at the following URL:
http://business.clayton.edu/arjomand Slide 2 Chapter 1 What is Statistics
s Applications in Business and Economics s Data s Data Sources s I need help! Descriptive Statistics
Statistical Inference s Computers and Statistical Analysis
s Slide 3 Application Areas of Statistics •
• Accounting
Auditing
Costing •
• •
• Finance
Financial trends
Forecasting Management
Describe employees
Quality improvement •
• Marketing
Consumer preferences
Marketing mix effects Slide 4 Applications in Business and Economics
s Accounting
Public accounting firms use statistical
sampling procedures when conducting
audits for their clients. s Economics Economists use statistical information
in making forecasts about the future of
the economy or some aspect of it. Slide 5 Applications in Business and Economics
s Marketing
Electronic pointofsale scanners at
retail checkout counters are used to
collect data for a variety of marketing
research applications. s Production A variety of statistical quality control charts are used to monitor
the output of a production process. Slide 6 Applications in Business and Economics Finance Financial advisors use priceearnings ratios and
dividend yields to guide their investment
recommendations. Slide 7 Why Collect Data? Obtain input to a research study
Measure performance
Assist in formulating decision alternatives
Satisfy curiosity
• Knowledge for the sake of knowledge Slide 8 Data and Data Sets Data are the facts and figures collected, summarized, analyzed, and interpreted.
s The data collected in a particular study are referred to as the data set. Slide 9 Elements, Variables, and Observations The elements are the entities on which data are collected. A variable is a characteristic of interest for the elements. The set of measurements collected for a particular element is called an observation. The total number of data values in a data set is the number of elements multiplied by the number of variables. Slide 10 Data, Data Sets, Elements, Variables, and Observations
Element Names Variables Observation Company Dataram EnergySouth Keystone LandCare Psychemedics Stock Annual Earn/
Exchange Sales($M) Share($) AMEX OTC NYSE NYSE AMEX 73.10 0.86 74.00 1.67
365.70 0.86 111.40 0.33 17.60 0.13
Data Set Slide 11 Scales of Measurement Scales of measurement include:
Scales of measurement include:
Nominal Interval Ordinal Ratio The scale determines the amount of information
The scale determines the amount of information contained in the data.
contained in the data. The scale indicates the data summarization and
The scale indicates the data summarization and statistical analyses that are most appropriate.
statistical analyses that are most appropriate. Slide 12 Scales of Measurement
s Nominal Data are labels or names used to identify an
Data are labels or names used to identify an attribute of the element.
attribute of the element. A nonnumeric label or numeric code may be used.
A nonnumeric label or numeric code may be used. Slide 13 Scales of Measurement
s Nominal Example: Example: Students of a university are classified by the Students of a university are classified by the school in which they are enrolled using a school in which they are enrolled using a nonnumeric label such as Business, Humanities, nonnumeric label such as Business, Humanities, Education, and so on. Education, and so on. Alternatively, a numeric code could be used for Alternatively, a numeric code could be used for the school variable (e.g. 1 denotes Business, the school variable (e.g. 1 denotes Business, 2 denotes Humanities, 3 denotes Education, and 2 denotes Humanities, 3 denotes Education, and so on). so on). Slide 14 Scales of Measurement
s Ordinal The data have the properties of nominal data and
The data have the properties of nominal data and the order or rank of the data is meaningful..
the order or rank of the data is meaningful A nonnumeric label or numeric code may be used.
A nonnumeric label or numeric code may be used. Slide 15 Scales of Measurement
s Ordinal Example:
Example: Students of a university are classified by their Students of a university are classified by their class standing using a nonnumeric label such as class standing using a nonnumeric label such as Freshman, Sophomore, Junior, or Senior. Freshman, Sophomore, Junior, or Senior. Alternatively, a numeric code could be used for Alternatively, a numeric code could be used for the class standing variable (e.g. 1 denotes the class standing variable (e.g. 1 denotes Freshman, 2 denotes Sophomore, and so on). Freshman, 2 denotes Sophomore, and so on). Slide 16 Scales of Measurement
s Interval The data have the properties of ordinal data, and
The data have the properties of ordinal data, and the interval between observations is expressed in
the interval between observations is expressed in terms of a fixed unit of measure.
terms of a fixed unit of measure. Interval data are always numeric..
Interval data are always numeric Slide 17 Scales of Measurement
s Interval Example:
Example: Melissa has an SAT score of 1205, while Kevin Melissa has an SAT score of 1205, while Kevin has an SAT score of 1090. Melissa scored 115 has an SAT score of 1090. Melissa scored 115 points more than Kevin. points more than Kevin. Slide 18 Scales of Measurement
s Ratio The data have all the properties of interval data
The data have all the properties of interval data and the ratio of two values is meaningful..
and the ratio of two values is meaningful Variables such as distance, height, weight, and time
Variables such as distance, height, weight, and time use the ratio scale.
use the ratio scale. This scale must contain a zero value that indicates
This scale must contain a zero value that indicates that nothing exists for the variable at the zero point.
that nothing exists for the variable at the zero point. Slide 19 Scales of Measurement
s Ratio Example:
Example: Melissa’s college record shows 36 credit hours Melissa’s college record shows 36 credit hours earned, while Kevin’s record shows 72 credit earned, while Kevin’s record shows 72 credit hours earned. Kevin has twice as many credit hours earned. Kevin has twice as many credit hours earned as Melissa. hours earned as Melissa. Slide 20 Types of Data
Data Numerical
(Quantitative) Discrete Categorical
(Qualitative) Continuous Slide 21 Qualitative and Quantitative Data Data can be further classified as being qualitative
Data can be further classified as being qualitative or quantitative..
or quantitative The statistical analysis that is appropriate depends
The statistical analysis that is appropriate depends on whether the data for the variable are qualitative
on whether the data for the variable are qualitative or quantitative.
or quantitative. In general, there are more alternatives for statistical
In general, there are more alternatives for statistical analysis when the data are quantitative.
analysis when the data are quantitative. Slide 22 Qualitative Data Labels or names used to identify an attribute of each
Labels or names used to identify an attribute of each element
element Often referred to as categorical data
Often referred to as categorical data Use either the nominal or ordinal scale of
Use either the nominal or ordinal scale of measurement
measurement Can be either numeric or nonnumeric
Can be either numeric or nonnumeric Appropriate statistical analyses are rather limited
Appropriate statistical analyses are rather limited Slide 23 Quantitative Data Quantitative data indicate how many or how much:
Quantitative data indicate how many or how much: discrete,, if measuring how many
discrete if measuring how many continuous,, if measuring how much
continuous if measuring how much Quantitative data are always numeric..
Quantitative data are always numeric Ordinary arithmetic operations are meaningful for
Ordinary arithmetic operations are meaningful for quantitative data.
quantitative data. Slide 24 Scales of Measurement
Data
Qualitative Numerical
Numerical Nominal
Nominal Ordinal
Ordinal Quantitative Nonnumerical
Nonnumerical Nominal
Nominal Ordinal
Ordinal Numerical
Numerical Interval
Interval Ratio
Ratio Slide 25 CrossSectional Data Crosssectional data are collected at the same or
Crosssectional data are collected at the same or approximately the same point in time.
approximately the same point in time. Example:: data detailing the number of building
Example data detailing the number of building permits issued in June 2003 in each of the counties
permits issued in June 2003 in each of the counties of Ohio
of Ohio Slide 26 Time Series Data Time series data are collected over several time
Time series data are collected over several time periods.
periods. Example:: data detailing the number of building
Example data detailing the number of building permits issued in Lucas County, Ohio in each of
permits issued in Lucas County, Ohio in each of the last 36 months
the last 36 months Slide 27 Data Sources
Data
Sources
Primary Experiment Survey Secondary Observation Published
(& On-Line) Slide 28 Data Sources
s Existing Sources Within a firm – almost any department
Business database services – Dow Jones & Co.
Government agencies U.S. Department of Labor
Industry associations – Travel Industry Association of America
Specialinterest organizations – Graduate Management Admission Council
Internet – more and more firms Slide 29 Data Sources (Continued)
s Statistical Studies
In experimental studies the variables of interest
In experimental studies the variables of interest
are first identified. Then one or more factors are
are first identified. Then one or more factors are
controlled so that data can be obtained about how
controlled so that data can be obtained about how
the factors influence the variables.
the factors influence the variables. In observational (nonexperimental) studies no
In observational (nonexperimental) studies no attempt is made to control or influence the
attempt is made to control or influence the variables of interest.
variables of interest.
a survey is a
good example Slide 30 Data Acquisition Considerations
Time Requirement • Searching for information can be time consuming.
• Information may no longer be useful by the time it
is available. Cost of Acquisition • Organizations often charge for information even when it is not their primary business activity. Data Errors • Using any data that happens to be available or that were acquired with little care can lead to poor and misleading information. Slide 31 What Is Statistics?
s s s Collecting data
• e.g., Survey
Presenting data
• e.g., Charts & tables
Characterizing data
• e.g., Average Data
Data
Analysis
Analysis Why? DecisionMaking Slide 32 Statistical Methods
Statistical
Methods Descriptive
Statistics Inferential
Statistics Slide 33 Descriptive Statistics
s Descriptive statistics are the tabular, graphical, and numerical methods used to summarize data. Descriptive Statistics:
These are statistical
methods used to
describe data that
have been collected.
have Slide 34 Example: Hudson Auto Repair
The manager of Hudson Auto
would like to have a better
understanding of the cost
of parts used in the engine
tuneups performed in the
shop. She examines 50
customer invoices for tuneups. The costs of parts,
rounded to the nearest dollar, are listed on the next
slide. Slide 35 Example: Hudson Auto Repair
s Sample of Parts Cost for 50 Tuneups 91
71
104
85
62 78
69
74
97
82 93
72
62
88
98 57
89
68
68
101 75
66
97
83
79 52
75
105
68
105 99
79
77
71
79 80
75
65
69
69 97
72
80
67
62 62
76
109
74
73 Slide 36 Tabular Summary: Frequency and Percent Frequency
Parts Cost ($) 5059 6069 7079 8089 9099 100109 Parts Frequency 2 13 16 7 7 5 50 Percent
Frequency 4 26
(2/50)100 32 14 14 10 100 Slide 37 Graphical Summary: Histogram
18 Tuneup Parts Cost 16 Frequency 14
12
10
8
6
4
2
50−59 60−69 70−79 80−89 90−99 100110 Parts
Cost ($) Slide 38 Numerical Descriptive Statistics The most common numerical descriptive statistic is the average (or mean). Hudson’s average cost of parts, based on the 50 tuneups studied, is $79 (found by summing the 50 cost values and then dividing by 50). Slide 39 Inferential Statistics
s s Involves
• Estimation
• Hypothesis testing Population?
Population? Purpose
• Make decisions about population characteristics Inferential Statistics: These are
statistical methods used to find out
something about population based
on a sample.
on Slide 40 Statistical Inference Population − the set of all elements of interest in a particular study Sample − a subset of the population
Statistical inference − the process of using data obtained from a sample to make estimates and test hypotheses about the characteristics of a population
Census − collecting data for a population
Sample survey − collecting data for a sample Slide 41 Process of Statistical Inference
1. Population consists of all
tuneups. Average
cost of parts is
unknown. 2. A sample of 50 4. The sample average 3. The sample data is used to estimate the population average. engine tuneups is examined. provide a sample
average parts cost
of $79 per tuneup. Slide 42 Statistical Analysis Using Microsoft Excel Statistical analysis typically involves working with large amounts of data. Computer software is typically used to conduct the analysis. Frequently the data that is to be analyzed resides in a spreadsheet. Modern spreadsheet packages are capable of data management, analysis, and presentation. MS Excel is the most widely available spreadsheet software in business organizations. Slide 43 Statistical Analysis Using Microsoft Excel
s 3 tasks might be needed:
• Enter Data
• Enter Functions and Formulas
• Apply Tools 1
2
3
4
5
6
7
8 D
Mean
Median
Mode
Range A
Parts
Cost
91
71
104
85
62
78
69 E
=AVERAGE(A2:A71)
=MEDIAN(A2:A71)
=MODE(A2:A71)
=MAX(A2:A71)-MIN(A2:A71) Slide 44 Statistical Analysis Using Microsoft Excel
s Excel Worksheet (showing data)
A 1
2
3
4
5
6
7
8
9 Customer
Sam Abrams
Mary Gagnon
Ted Dunn
ABC Appliances
Harry Morgan
Sara Morehead
Vista Travel, Inc.
John Williams B
Invoice #
20994
21003
21010
21094
21116
21155
21172
21198 C
Parts
Cost ($)
91
71
104
85
62
78
69
74 D
Labor
Cost ($)
185
205
192
178
242
148
165
190 Note: Rows 1051 are not shown. Slide 45 Statistical Analysis Using Microsoft Excel
s 1
2
3
4
5
6
7
8
9 Excel Formula Worksheet
C
D
E
Parts
Labor
Cost ($) Cost ($)
91
185
71
205
104
192
85
178
62
242
78
148
69
165
74
190 F G Average Parts Cost =AVERAGE(C2:C51) Note: Columns AB and rows 1051 are not shown. Slide 46 Statistical Analysis Using Microsoft Excel
s 1
2
3
4
5
6
7
8
9 Excel Value Worksheet
C
D
E
Parts
Labor
Cost ($) Cost ($)
91
185
71
205
104
192
85
178
62
242
78
148
69
165
74
190 F G Average Parts Cost 79 Note: Columns AB and rows 1051 are not shown. Slide 47 End of Chapter 1 Slide 48 ...

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