business statistics
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A term too broad to define, Statistics is an important subject studied by almost all commerce graduates and undergraduates across the globe.
We at Homework Guru offer best Business Statistics Homework Help available online.
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What is StatisticsI need help!
Applications in Business and Economics Data Data Sources Descriptive Statistics Statistical Inference Computers and Statistical Analysis
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Application Areas of Statistics
Accounting– Auditing– Costing
Statistics– Financial trends– Forecasting
Management– Describe employees– Quality improvement
Marketing– Consumer preferences– Marketing mix effects
Applications in Business and Economics
• Accounting
Economics
Public accounting firms use statisticalsampling procedures when conductingaudits for their clients.
Economists use statistical informationin making forecasts about the future ofthe economy or some aspect of it.
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Applications in Business and Economics
A variety of statistical quality control charts are used to monitorthe output of a production process.
Production
Electronic point-of-sale scanners atretail checkout counters are used tocollect data for a variety of marketingresearch applications.
Marketing
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Applications in Business and Economics
Financial advisors use price-earnings ratios anddividend yields to guide their investmentrecommendations.
Statistics
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Why Collect Data?
Obtain input to a research studyMeasure performanceAssist in formulating decision alternativesSatisfy curiosity
– Knowledge for the sake of knowledge
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Data and Data Sets
• Data are the facts and figures collected, summarized, analyzed, and interpreted.
The data collected in a particular study are referred to as the data set.
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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.
Elements, Variables, and Observations
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Stock Annual Earn/Exchange Sales($M) Share($)
Data, Data Sets, Elements, Variables, and Observations
Company
Dataram EnergySouth Keystone LandCare Psychemedics
AMEX 73.10 0.86 OTC 74.00 1.67 NYSE 365.70 0.86 NYSE 111.40 0.33 AMEX 17.60 0.13
VariablesElement Names
Data Set
Observation
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Scales of Measurement
The scale indicates the data summarization and statistical analyses that are most appropriate.
The scale determines the amount of information contained in the data.
Scales of measurement include:Nominal
Ordinal
Interval
Ratio
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Scales of Measurement• Nominal
A nonnumeric label or numeric code may be used.
Data are labels or names used to identify an attribute of the element.
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Example: Students of a university are classified by the school in which they are enrolled using a nonnumeric label such as Business, Humanities, Education, and so on. Alternatively, a numeric code could be used for the school variable (e.g. 1 denotes Business, 2 denotes Humanities, 3 denotes Education, and so on).
Scales of Measurement Nominal
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Scales of Measurement• Ordinal
A nonnumeric label or numeric code may be used.
The data have the properties of nominal data and the order or rank of the data is meaningful.
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Scales of Measurement• Ordinal
Example: Students of a university are classified by their class standing using a nonnumeric label such as Freshman, Sophomore, Junior, or Senior. Alternatively, a numeric code could be used for the class standing variable (e.g. 1 denotes Freshman, 2 denotes Sophomore, and so on).
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Scales of Measurement
Interval data are always numeric.
The data have the properties of ordinal data, and the interval between observations is expressed in terms of a fixed unit of measure.
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Scales of Measurement
Example: Melissa has an SAT score of 1205, while Kevin has an SAT score of 1090. Melissa scored 115 points more than Kevin.
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Scales of Measurement
The data have all the properties of interval data and the ratio of two values is meaningful.
Variables such as distance, height, weight, and time use the ratio scale.
This scale must contain a zero value that indicates that nothing exists for the variable at the zero point.
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Scales of Measurement
Example: Melissa’s college record shows 36 credit hours earned, while Kevin’s record shows 72 credit hours earned. Kevin has twice as many credit hours earned as Melissa.
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Types of DataData
Numerical(Quantitative)
Categorical(Qualitative)
Discrete Continuous
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Data can be further classified as being qualitative or quantitative.
The statistical analysis that is appropriate depends on whether the data for the variable are qualitative or quantitative.
In general, there are more alternatives for statistical analysis when the data are quantitative.
Qualitative and Quantitative Data
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Qualitative Data Labels or names used to identify an attribute of each element
Often referred to as categorical data
Use either the nominal or ordinal scale of measurement
Can be either numeric or nonnumeric
Appropriate statistical analyses are rather limited
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Quantitative Data
Quantitative data indicate how many or how much: discrete, if measuring how many
continuous, if measuring how much
Quantitative data are always numeric.
Ordinary arithmetic operations are meaningful for quantitative data.
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Scales of Measurement
Qualitative Quantitative
Numerical NumericalNon-numerical
Data
Nominal
Ordinal
Nominal Ordinal Interval Ratio
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Cross-Sectional Data
Cross-sectional data are collected at the same or approximately the same point in time.
Example: data detailing the number of building permits issued in June 2003 in each of the counties of Ohio
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Time Series Data
Time series data are collected over several time periods.
Example: data detailing the number of building permits issued in Lucas County, Ohio in each of the last 36 months
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DataSources
Primary Secondary
Experiment Published(& On-Line)
Survey Observation
Data Sources
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Data Sources• Existing Sources
Within a firm – almost any departmentBusiness database services – Dow Jones & Co.Government agencies - U.S. Department of LaborIndustry associations – Travel Industry Association of AmericaSpecial-interest organizations – Graduate Management Admission CouncilInternet – more and more firms
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• Statistical Studies
Data Sources (Continued)
In experimental studies the variables of interestare first identified. Then one or more factors arecontrolled so that data can be obtained about howthe factors influence the variables.
In observational (non-experimental) studies no attempt is made to control or influence the variables of interest. a survey is a
good example
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Data Acquisition Considerations
Time Requirement
Cost of Acquisition
Data Errors
• Searching for information can be time consuming.• Information may no longer be useful by the time it
is available.
• Organizations often charge for information even when it is not their primary business activity.
• Using any data that happens to be available or that were acquired with little care can lead to poor and misleading information.
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What Is Statistics?
• Collecting data– e.g., Survey
• Presenting data– e.g., Charts & tables
• Characterizing data– e.g., Average
Data Analysis
Decision-Making
Why?
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Statistical MethodsStatisticalMethods
DescriptiveStatistics
InferentialStatistics
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Descriptive Statistics
• 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.
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Example: Hudson Auto Repair
The manager of Hudson Autowould like to have a betterunderstanding of the costof parts used in the enginetune-ups performed in theshop. She examines 50customer invoices for tune-ups. The costs of
parts,rounded to the nearest dollar, are listed on the
nextslide.
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91 78 93 57 75 52 99 80 97 6271 69 72 89 66 75 79 75 72 76104 74 62 68 97 105 77 65 80 10985 97 88 68 83 68 71 69 67 7462 82 98 101 79 105 79 69 62 73
Example: Hudson Auto Repair
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Tabular Summary: Frequency and Percent Frequency
50-59 60-69 70-79 80-89 90-99 100-109
2 13 16 7 7 5 50
4 26 32 14 14 10 100
(2/50)100
Parts Cost ($)
Parts Frequency
PercentFrequency
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Graphical Summary: Histogram
2468
1012141618
PartsCost ($)
Freq
uenc
y
50-59 60-69 70-79 80-89 90-99 100-110
Tune-up Parts Cost
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Numerical Descriptive Statistics
Hudson’s average cost of parts, based on the 50 tune-ups studied, is $79 (found by summing the 50 cost values and then dividing by 50).
The most common numerical descriptive statistic is the average (or mean).
Inferential Statistics• Involves
– Estimation– Hypothesis
testing
• Purpose– Make decisions about
population characteristics
Population?
Inferential Statistics: These are statistical methods used to find out something about population based on a sample.
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Statistical Inference
Population
Sample
Statistical inference
Census
Sample survey
- the set of all elements of interest in a particular study
- a subset of the population
- the process of using data obtained from a sample to make estimates and test hypotheses about the characteristics of a population
- collecting data for a population
- collecting data for a sample
Process of Statistical Inference1. Population consists of alltune-ups. Averagecost of parts isunknown.
2. A sample of 50engine tune-ups is examined.
3. The sample data provide a sampleaverage parts costof $79 per tune-up.
4. The sample averageis used to estimate the population average.
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Statistical Analysis Using Microsoft Excel
Computer software is typically used to conduct the analysis. Frequently the data that is to be analyzed resides in a spreadsheet. Modern spreadsheet packages are capable of data management, analysis, and presentation. MS Excel is the most widely available spreadsheet software in business organizations.
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Statistical Analysis Using Microsoft Excel 3 tasks might be
needed:• Enter Data• Enter Functions and Formulas• Apply Tools
A
1Parts Cost
2 913 714 1045 856 627 788 69
D EMean =AVERAGE(A2:A71)
Median =MEDIAN(A2:A71)Mode =MODE(A2:A71)
Range =MAX(A2:A71)-MIN(A2:A71)
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Excel Worksheet (showing data)
Note: Rows 10-51 are not shown.
Statistical Analysis Using Microsoft Excel
A B C D
1 Customer Invoice #Parts
Cost ($)Labor
Cost ($)2 Sam Abrams 20994 91 1853 Mary Gagnon 21003 71 2054 Ted Dunn 21010 104 1925 ABC Appliances 21094 85 1786 Harry Morgan 21116 62 2427 Sara Morehead 21155 78 1488 Vista Travel, Inc. 21172 69 1659 John Williams 21198 74 190
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Excel Formula Worksheet
Note: Columns A-B and rows 10-51 are not shown.
Statistical Analysis Using Microsoft Excel
C D E F G
1Parts
Cost ($)Labor
Cost ($)2 91 185 Average Parts Cost =AVERAGE(C2:C51)3 71 2054 104 1925 85 1786 62 2427 78 1488 69 1659 74 190
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Excel Value Worksheet
Note: Columns A-B and rows 10-51 are not shown.
Statistical Analysis Using Microsoft Excel
C D E F G
1Parts
Cost ($)Labor
Cost ($)2 91 185 Average Parts Cost 793 71 2054 104 1925 85 1786 62 2427 78 1488 69 1659 74 190
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