The document is an introduction to the field of statistics, detailing its role in business decision-making and data analysis. It covers fundamental concepts, types of statistical applications, methods for collecting and analyzing data, and the distinction between descriptive and inferential statistics. Key aspects include understanding populations and samples, types of data, and potential biases in sampling.

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Statistics for Business and
Economics
Chapter 1
Statistics, Data, &
Statistical Thinking

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Contents
1. The Science of Statistics
2. Types of Statistical Applications in Business
3. Fundamental Elements of Statistics
4. Processes
5. Types of Data
6. Collecting Data
7. The Role of Statistics in Managerial Decision
Making

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Learning Objectives
1. Introduce the field of statistics
2. Demonstrate how statistics applies to business
3. Establish the link between statistics and data
4. Identify the different types of data and data-
collection methods
5. Differentiate between population and sample data
6. Differentiate between descriptive and inferential
statistics

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1.1
The Science of Statistics

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What Is Statistics?
Why?1. Collecting Data
e.g., Survey
1. Presenting Data
e.g., Charts & Tables
1. Characterizing Data
e.g., Average
Data
Analysis
Decision-
Making
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© 1984-1994 T/Maker Co.

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What Is Statistics?
Statistics is the science of data. It involves
collecting, classifying, summarizing, organizing,
analyzing, and interpreting numerical
information.

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1.2
Types of Statistical Applications in
Business

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Application Areas
• Economics
– Forecasting
– Demographics
• Sports
– Individual & Team
Performance
• Engineering
– Construction
– Materials
• Business
– Consumer Preferences
– Financial Trends

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Statistics: Two Processes
Describing sets of data
and
Drawing conclusions (making estimates,
decisions, predictions, etc. about sets of data
based on sampling)

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Statistical Methods
Statistical
Methods
Descriptive
Statistics
Inferential
Statistics

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Descriptive Statistics
1. Involves
• Collecting Data
• Presenting Data
• Characterizing Data
1. Purpose
• Describe Data
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1. Involves
• Estimation
• Hypothesis
Testing
1. Purpose
• Make decisions about
population characteristics
Inferential Statistics
Population?

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1.3
Fundamental Elements
of Statistics

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Fundamental Elements
1. Experimental unit
• Object upon which we collect data
1. Population
• All items of interest
1. Variable
• Characteristic of an individual
experimental unit
1. Sample
• Subset of the units of a population
• PP in PPopulation
& PParameter
• SS in SSample
& SStatistic

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Fundamental Elements
1. Statistical Inference
• Estimate or prediction or generalization about a
population based on information contained in a
sample
1. Measure of Reliability
• Statement (usually qualified) about the degree
of uncertainty associated with a statistical
inference

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Four Elements of Descriptive
Statistical Problems
1. The population or sample of interest
2. One or more variables (characteristics of the
population or sample units) that are to be
investigated
3. Tables, graphs, or numerical summary tools
4. Identification of patterns in the data

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Five Elements of Inferential
Statistical Problems
1. The population of interest
2. One or more variables (characteristics of the
population units) that are to be investigated
3. The sample of population units
4. The inference about the population based on
information contained in the sample
5. A measure of reliability for the inference

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1.4
Processes

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Process
A process is a series of actions or operations that
transforms inputs to outputs. A process produces or
generates output over time.

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Process
A process whose operations or actions are unknown or
unspecified is called a black box.
Any set of output (object or numbers) produced by a
process is called a sample.

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1.5
Types of Data

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Types of Data
Quantitative data are measurements that are recorded
on a naturally occurring numerical scale.
Qualitative data are measurements that cannot be
measured on a natural numerical scale; they can only be
classified into one of a group of categories.

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Types of Data
Types of
Data
Quantitative
Data
Qualitative
Data

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Quantitative Data
Measured on a numeric
scale.
• Number of defective
items in a lot.
• Salaries of CEOs of
oil companies.
• Ages of employees at
a company.
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Qualitative Data
Classified into categories.
• College major of each
student in a class.
• Gender of each employee
at a company.
• Method of payment
(cash, check, credit card).
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1.6
Collecting Data

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Obtaining Data
1. Data from a published source
2. Data from a designed experiment
3. Data from a survey
4. Data collected observationally

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Obtaining Data
Published source:
book, journal, newspaper, Web site
Designed experiment:
researcher exerts strict control over units
Survey:
a group of people are surveyed and their
responses are recorded
Observation study:
units are observed in natural setting and
variables of interest are recorded

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Samples
A representative sample exhibits characteristics
typical of those possessed by the population of
interest.
A random sample of n experimental units is a
sample selected from the population in such a way
that every different sample of size n has an equal
chance of selection.

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Random Sample
Every sample of size n has an equal chance of
selection.

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1.7
The Role of Statistics in
Managerial Decision Making

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Statistical Thinking
Statistical thinking involves applying rational
thought and the science of statistics to critically
assess data and inferences. Fundamental to the
thought process is that variation exists in
populations and process data.
A random sample of n experimental units is a
sample selected from the population in such a way
that every different sample of size n has an equal
chance of selection.

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Nonrandom Sample Errors
Selection bias results when a subset of the
experimental units in the population is excluded so
that these units have no chance of being selected for
the sample.
Nonresponse bias results when the researchers
conducting a survey or study are unable to obtain data
on all experimental units selected for the sample.
Measurement error refers to inaccuracies in the
values of the data recorded. In surveys, the error may
be due to ambiguous or leading questions and the
interviewer’s effect on the respondent.

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Real-World Problem

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Statistical
Computer Packages
1. Typical Software
• SPSS
• MINITAB
• Excel
1. Need Statistical
Understanding
• Assumptions
• Limitations

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Key Ideas
Types of Statistical Applications
Descriptive
1. Identify population and sample (collection
of experimental units)
2. Identify variable(s)
3. Collect data
4. Describe data

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Key Ideas
Types of Statistical Applications
Inferential
1. Identify population (collection of all
experimental units)
2. Identify variable(s)
3. Collect sample data (subset of population)
4. Inference about population based on sample
5. Measure of reliability for inference

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Key Ideas
Types of Data
1. Quantitative (numerical in nature)
2. Qualitative (categorical in nature)

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Key Ideas
Data-Collection Methods
1. Observational
2. Published source
3. Survey
4. Designed experiment

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Key Ideas
Problems with Nonrandom Samples
1. Selection bias
2. Nonresponse bias
3. Measurement error