Chapter 1Introductionto Statistics with Excel.ppt

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1、 2002 Thomson / South-Western,Slide 1-1,Chapter 1Introduction to Statistics with Excel, 2002 Thomson / South-Western,Slide 1-2,Learning Objectives,Define statistics Become aware of a wide range of applications of statistics in business Differentiate between descriptive and inferential statistics Cla

2、ssify numbers by level of data and understand why doing so is important Become aware of the statistical analysis capabilities of Excel, 2002 Thomson / South-Western,Slide 1-3,What is Statistics?,Science of gathering, analyzing, interpreting, and presenting data Branch of mathematics Course of study

3、Facts and figures A death Measurement taken on a sample Type of distribution being used to analyze data, 2002 Thomson / South-Western,Slide 1-4,Population Versus Sample,Population the whole a collection of persons, objects, or items under study Census gathering data from the entire population Sample

4、 a portion of the whole a subset of the population, 2002 Thomson / South-Western,Slide 1-5,Population, 2002 Thomson / South-Western,Slide 1-6,Population and Census Data, 2002 Thomson / South-Western,Slide 1-7,Sample and Sample Data, 2002 Thomson / South-Western,Slide 1-8,Descriptive vs. Inferential

5、Statistics,Descriptive Statistics using data gathered on a group to describe or reach conclusions about that same group onlyInferential Statistics using sample data to reach conclusions about the population from which the sample was taken, 2002 Thomson / South-Western,Slide 1-9,Parameter vs. Statist

6、ic,Parameter descriptive measure of the population Usually represented by Greek lettersStatistic descriptive measure of a sample Usually represented by Roman letters, 2002 Thomson / South-Western,Slide 1-10,Symbols for Population Parameters, 2002 Thomson / South-Western,Slide 1-11,Symbols for Sample

7、 Statistics, 2002 Thomson / South-Western,Slide 1-12,Process of Inferential Statistics, 2002 Thomson / South-Western,Slide 1-13,Levels of Data Measurement,Nominal - Lowest level of measurement Ordinal Interval Ratio - Highest level of measurement, 2002 Thomson / South-Western,Slide 1-14,Nominal Leve

8、l Data,Numbers are used to classify or categorize Example: 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, 2002 Thomson / South-Western,Slide

9、1-15,Ordinal Level Data,Numbers are used to indicate rank or order Relative magnitude of numbers is meaningful Differences between numbers are not comparableExample: Taste test ranking of three brands of soft drink Example: Position within an organization 1 for President 2 for Vice President 3 for P

10、lant Manager 4 for Department Supervisor 5 for Employee, 2002 Thomson / South-Western,Slide 1-16,Example of Ordinal Measurement, 2002 Thomson / South-Western,Slide 1-17,Ordinal Data,Faculty and staff should receive preferential treatment for parking space., 2002 Thomson / South-Western,Slide 1-18,In

11、terval 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 zeroExamples: Fahrenheit Temperature, Calen

12、dar Time, Monetary Units, 2002 Thomson / South-Western,Slide 1-19,Ratio 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 funct

13、ion is zeroExamples: Height, Weight, and VolumeMonetary Variables, such as Revenues, and ExpensesFinancial ratios, such as P/E Ratio, Inventory Turnover, 2002 Thomson / South-Western,Slide 1-20,Usage Potential of Various Levels of Data,Nominal,Ordinal,Interval,Ratio, 2002 Thomson / South-Western,Sli

14、de 1-21,Data Level, Operations, and Statistical Methods, 2002 Thomson / South-Western,Slide 1-22,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

15、interval or ratio level that measures on a naturally occurring numeric scale., 2002 Thomson / South-Western,Slide 1-23,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

16、Data is numeric data that can take on values at every point over a given interval. Continuous data result from a measuring process., 2002 Thomson / South-Western,Slide 1-24,Summary of Data Classifications,Data,nal,Ordinal,Interl,Ratio,Qualitative (Categorical),Quantitative,Nonnumeric,Numeric,Discrete,Numeric,Discrete or Continuous,Data,Nominal,Ordinal,Interval,Ratio,Quantitative,Qualitative,Numeric,Numeric,Discrete,Nonnumeric,Discrete or Continuous,