© 2002 thomson / south-western slide 1-1 chapter 1 introduction to statistics with excel
TRANSCRIPT
© 2002 Thomson / South-Western Slide 1-1
Chapter 1
Introductionto Statistics with Excel
© 2002 Thomson / South-Western Slide 1-2
Learning Objectives Learning Objectives• Define statistics• Become aware of a wide range of
applications of statistics in business• Differentiate between descriptive and
inferential statistics• Classify numbers by level of data and
understand why doing so is important• Become aware of the statistical analysis
capabilities of Excel
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What is Statistics?What is Statistics?
• Science of gathering, analyzing, interpreting, and presenting data
• Branch of mathematics• Course of study• Facts and figures• A death• Measurement taken on a sample• Type of distribution being used to
analyze data
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Population Versus SamplePopulation Versus Sample
• Population — the whole– a collection of persons, objects, or items
under study• Census — gathering data from the
entire population• Sample — a portion of the whole
– a subset of the population
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PopulationPopulation
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Population and Census DataPopulation and Census Data
Identifier Color MPG
RD1 Red 12
RD2 Red 10RD3 Red 13
RD4 Red 10RD5 Red 13BL1 Blue 27BL2 Blue 24
GR1 Green
35GR2 Gree
n35
GY1 Gray 15GY2 Gray 18GY3 Gray 17
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Sample and Sample DataSample and Sample Data
Identifier Color MPG
RD2 Red 10
RD5 Red 13
GR1 Green
35
GY2 Gray 18
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Descriptive vs. Inferential StatisticsDescriptive vs. Inferential Statistics
• Descriptive Statistics — using data gathered on a group to describe or reach conclusions about that same group only
• Inferential Statistics — using sample data to reach conclusions about the population from which the sample was taken
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Parameter vs. StatisticParameter vs. Statistic
• Parameter — descriptive measure of the population– Usually represented by Greek letters
• Statistic — descriptive measure of a sample– Usually represented by Roman letters
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Symbols for Population ParametersSymbols for Population Parameters
denotes population parameter
2 denotes population variance
denotes population standard deviation
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Symbols for Sample StatisticsSymbols for Sample Statistics
x denotes sample mean2S denotes sample variance
S denotes sample standard deviation
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Process of Inferential StatisticsProcess of Inferential Statistics
Population
(parameter)
Sample
x
(statistic)
Calculate x
to estimate
Select a
random sample
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Levels of Data MeasurementLevels of Data Measurement
• Nominal - Lowest level of measurement• Ordinal• Interval• Ratio - Highest level of measurement
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Nominal Level DataNominal Level Data
• Numbers are used to classify or categorizeExample: Employment Classification
– 1 for Educator– 2 for Construction Worker– 3 for Manufacturing Worker
Example: Ethnicity– 1 for African-American– 2 for Anglo-American– 3 for Hispanic-American– 4 for Oriental-American
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Ordinal Level DataOrdinal Level Data
• Numbers are used to indicate rank or order– Relative magnitude of numbers is meaningful– Differences between numbers are not comparable
Example: Taste test ranking of three brands of soft drink
Example: Position within an organization– 1 for President– 2 for Vice President– 3 for Plant Manager– 4 for Department Supervisor– 5 for Employee
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Example of Ordinal MeasurementExample of Ordinal Measurement
f
i
n
is
h
1
2
3
4
5
6
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Ordinal DataOrdinal Data
Faculty and staff should receive preferential treatment for parking space.
1 2 3 4 5
StronglyAgree
Agree StronglyDisagree
DisagreeNeutral
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Interval Level DataInterval Level Data• Distances between consecutive integers are
equal– Relative magnitude of numbers is meaningful– Differences between numbers are
comparable– Location of origin, zero, is arbitrary– Vertical intercept of unit of measure
transform function is not zero
Examples: Fahrenheit Temperature, Calendar Time, Monetary Units
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Ratio Level DataRatio Level Data• Highest level of measurement
– Relative magnitude of numbers is meaningful– Differences between numbers are comparable– Location of origin, zero, is absolute (natural)– Vertical intercept of unit of measure transform
function is zero
Examples: Height, Weight, and VolumeMonetary Variables, such as Revenues, and ExpensesFinancial ratios, such as P/E Ratio, Inventory Turnover
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Usage Potential of VariousLevels of Data
Usage Potential of VariousLevels of Data
Nominal
Ordinal
Interval
Ratio
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Data Level, Operations, and Statistical Methods
Data Level, Operations, and Statistical Methods
Data Level
Nominal
Ordinal
Interval
Ratio
Meaningful Operations
Classifying and Counting
All of above plus Ranking
All of above plus Addition, Subtraction, Multiplication, and Division
All of the above
StatisticalMethods
Nonparametric
Nonparametric
Parametric
Parametric
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Qualitative vs Quantitative Data
Qualitative Data is data of the nominal or ordinal level that classifies by a label or category. The labels may be numeric or nonnumeric.
Quantitative Data is data of the interval or ratio level that measures on a naturally occurring numeric scale.
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Discrete and Continuous Data
Discrete Data is numeric data in which the values can come only from a list of specific values. Discrete data results from a counting process.
Continuos Data is numeric data that can take on values at every point over a given interval. Continuous data result from a measuring process.
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Summary of Data Classifications
Data
nal Ordinal
InterlRatio
Qualitative(Categorical)
Quantitative
Nonnumeric Numeric
Discrete
Numeric
Discrete or Continuous
Data
Nominal Ordinal Interval Ratio
QuantitativeQualitative
Numeric Numeric
Discrete
Nonnumeric
Discrete orContinuous