Transcript Business Statistics: A Decision

Business Statistics:
A Decision-Making Approach
8th Edition
Chapter 1
The Where, Why, and How of
Data Collection
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Chapter Goals
After completing this chapter, you should be
able to:



Describe key data collection methods
Know key definitions:
Population vs. Sample
Primary vs. Secondary data types
Qualitative vs. Qualitative data
Time Series vs. Cross-Sectional data
Explain the difference between descriptive and
inferential procedures

Describe different sampling methods

Construct and interpret graphs
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Procedures of Statistics

Descriptive procedures


Collecting, presenting, and describing data
Inferential procedures

Drawing conclusions and/or making decisions
concerning a population based only on
sample data
Goal: Convert data into meaningful information!
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Descriptive Procedures

Collect data

e.g., Survey, Observation,
Experiments

Present data


e.g., Charts and graphs
Describe data
x

 e.g., Sample mean =
i
n
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Inferential Procedures

Making statements about a population by
examining sample results
Sample statistics
(known)
Population parameters
Inference
unknown, but can
be estimated from
sample evidence
Sample
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Population
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Techniques for Inferential Procedures
Drawing conclusions and/or making decisions
concerning a population based on sample results.

Estimation


e.g., Estimate the population mean
weight using the sample mean
weight
Hypothesis Testing

e.g., Use sample evidence to test
the claim that the population mean
weight is 120 pounds
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Procedures for Collecting Data
(MKTG 300 course and Video Clip #14: Samples and Surveys)
Data Collection Procedures
Experiments
Telephone
surveys
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Written
questionnaires
Direct observation and
personal interview
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Population vs. Sample
Population
a b
Sample
cd
b
ef gh i jk l m n
o p q rs t u v w
x y
z
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c
gi
o
n
r
u
y
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Populations and Samples


A population is the entire collection of things under
consideration and referred to as the frame

The sampling unit is each object or individual in the frame

A parameter is a summary measure computed to describe a
characteristic of the population
A sample is a subset of the population selected for
analysis

A statistic is a summary measure computed to describe a
characteristic of the sample drawn from the population
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Why Sample?

Less time consuming than a census

Less costly to administer than a census

It is possible to obtain statistical results of a
sufficiently high precision based on samples
Strive for representative samples to reflect the population
of interest accurately!
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Sampling Techniques
Sampling Techniques
Nonstatistical Sampling
Convenience
Statistical Sampling
Simple
Random
Systematic
Judgment
Stratified
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Cluster
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Nonstatistical Sampling

Convenience


Collected in the most convenient manner for the
researcher
Judgment

Based on judgments about who in the population
would be most likely to provide the needed
information
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Statistical Sampling

Items of the sample are chosen based on
known or calculable probabilities
Statistical Sampling
(Probability Sampling)
Simple Random
Stratified
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Systematic
Cluster
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Simple Random Sampling

Every possible sample of a given size has an
equal chance of being selected

Selection may be with replacement or without
replacement

The sample can be obtained using a table of
random numbers or computer random number
generator
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Stratified Random Sampling
(Please Watch Video Clip #14: Samples and Surveys)

Divide population into subgroups (called strata) according
to some common characteristic

e.g., gender, income level

Select a simple random sample from each subgroup

Combine samples from subgroups into one
Population
Divided
into 4
strata
Sample
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Systematic Random Sampling

Decide on sample size: n

Divide ordered (e.g., alphabetical) frame of N
individuals into groups of k individuals: k=N/n

Randomly select one individual from the 1st
group

Select every kth individual thereafter
N = 64
n=8
First Group
k=8
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Cluster Sampling


Divide population into several “clusters,” each
representative of the population (e.g., county)
Select a simple random sample of clusters

All items in the selected clusters can be used, or items can be
chosen from a cluster using another probability sampling
technique
Population
divided into
16 clusters.
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Randomly selected
clusters for sample
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Data Types
(Please see the Video Clip Introduction to Variables)
Data
Qualitative
(Categorical)
Quantitative
(Numerical)
Examples:



Marital Status
Political Party
Eye Color
(Defined categories)
Discrete
Examples:


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Number of Children
Defects per hour
(Counted items)
Continuous
Examples:


Weight
Voltage
(Measured
characteristics)
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Data Types

Time Series Data


Ordered data values observed over time
Cross Sectional Data

Data values observed at a single point in time
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Data Types
Sales (in $1000’s)
2003
2004
2005
2006
Atlanta
435
460
475
490
Boston
320
345
375
395
Cleveland
405
390
410
395
Denver
260
270
285
280
Time
Series
Data
Cross Sectional
Data
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Data Measurement Levels
(Please see the Video Clip: Scales of Measurement)
Measurements
e.g., temperature
Rankings
Ordered Categories
e.g., age range 25-34
Categorical Codes
e.g., ID Numbers, gender
Ratio/Interval Data
Ordinal Data
Nominal Data
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Highest Level
Complete Analysis
Higher Level
Mid-level Analysis
Lowest Level
Basic Analysis
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Steps to Categorizing Data
1. Identify each factor in the data set
2. Determine if the data are time-series or
cross-sectional
3. Determine which factors are quantitative and
which are qualitative
4. Determine the level of data measurement

e.g., nominal, ordinal
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Chapter Summary

Reviewed key data collection methods

Introduced key definitions:
Population vs. Sample
Primary vs. Secondary data types
Qualitative vs. Quantitative data
Time Series vs. Cross-Sectional data

Examined descriptive vs. inferential procedures

Described different sampling techniques

Reviewed data types and measurement levels
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