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Chapter

OMIS 2010 Chapter Notes -Descriptive Statistics, Level Of Measurement, Frequency Distribution


Department
Operations Management and Information System
Course Code
OMIS 2010
Professor
Alan Marshall

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Chapter 1: What is Statistics?
-Way to get info from data
-Descriptive statistics: deals with methods of organizing, summarizing and presenting data in convenient
and informative way (ie. graphing techniques)
-Use numerical techniques to summarize data; average
-Average is a measure of central location
-Range is measure of variability
-Inferential Statistics: methods used to draw conclusions/inferences about characteristics of population
based on sample data
-Exit polls: random sample of votes exit the polling booth and asked for whom they voted; sample proportion
of voters supported the candidates is computed
Key Statistical Concepts
-Population: group of all items of interest; very large; does not necessarily refer to group of people
-Parameter: descriptive measure of a population; mean # of soft drinks consumer by all students at the
university or proportion of 5 million who voted for Bush
-Sample: set of data drawn from the studied population
-Statistic: descriptive measure of a sample; used to make inferences about parameters
-Statistical Inference: process of making an estimate/prediction/decision about a population based on
sample data; measure of reliability
-Confidence level: proportion of times that an estimating procedure will be correct
-Significance level: measures how frequently conclusion will be wrong
-Hw pg 39, 47, 57-62, 69-72, 195, 197, 202, 204, 91-94, 100-104, 110-112, 118-125
Chapter 2: Graphical Descriptive Techniques 1
Types of Data and Information
-Variable: some characteristic of a population or sample (ie. prices of stocks varying daily)
-Values: possible observations of the variable; integers between 0 - 100 of statistics exam(100 marks)
-Data: observed values of a variable; midterm test marks of 10 students; datum refers to mark of one student
Interval data: real numbers; heights, weights, incomes, distances (quantitative or numerical)
Nominal data: values of nominal data are categories; responses to questions about marital
status (qualitative/categorical); only calculations based on frequencies or percentages of
occurrence are valid

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Ordinal data: order of values has meaning; order of values of latter indicate higher rating;
calculations based on ordering process are valid
Interval/differences between values of interval data are consistent and meaningful
Calculation for Types of Data
Interval Data: all calculations permitted; set of interval data described by using the average
Nominal Data: calculations based on codes used to store this type of data are meaningless; compute
percentages of occurrences of each category
Ordinal Data: only permissible calculations should involve ranking process
Describing Set of Nominal Data
-Frequency distribution: presenting the categories and their counts; relative frequency distribution lsits
categories and proportions with which each occurs
-Bar graph shows frequencies, pie chart shows relative frequencies
-Bars in bar graph arrange in ascending/descending ordinal values; pie chart wedges arrange clockwise in
ascending/descending order for ordinal data
Describing relationship between 2 nominal variables
-Univariate: techniques applied to single sets of data
-Bivariate: methods that depict relationship between variables
-Cross-classification table: describes relationship between 2 nominal variables; lists frequency of each
combination of values of the 2 variables
-If two variables are unrelated, patterns exhibited in bar charts should approx.. be the same
Comparing 2 or more nominal data sets
-Consider the three occupations (newspaper example) as defining 3 populations; if differences exist
between columns of frequency distributions (or between bar charts), then differences exist among the
three populations
Chapter 3: Graphical Descriptive Techniques 2
Graphing techniques to describe set of interval data
-Create frequency distribution for interval data by counting number of observations that fall into each of a
series of intervals; called classes, covering range of observations
-Intervals should be equal; graphing and interpretation made easier
-Histogram: created by drawing rectangles whose bases are intervals and heights are frequencies
Determining number of class intervals
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