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Statistics for Business and Economics

Chapter 1

Statistics, Data, &Statistical Thinking


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


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


1.1

The Science of Statistics


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

© 1984-1994 T/Maker Co.

© 1984-1994 T/Maker Co.


What Is Statistics?

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


1.2

Types of Statistical Applications in Business


Application Areas

• Economics– Forecasting– Demographics

• Sports– Individual & Team

Performance

• Engineering– Construction– Materials

• Business– Consumer Preferences– Financial Trends


Statistics: Two Processes

Describing sets of data

and

Drawing conclusions (making estimates, decisions, predictions, etc. about sets of data based on sampling)


Statistical Methods

StatisticalMethods

DescriptiveStatistics

InferentialStatistics


Descriptive Statistics

1. Involves• Collecting Data

• Presenting Data• Characterizing Data

1. Purpose• Describe Data

X = 30.5 S2 = 113

0

25

50

Q1 Q2 Q3 Q4

$


1. Involves• Estimation

• Hypothesis Testing

1. Purpose• Make decisions about

population characteristics

Inferential Statistics

Population?


1.3

Fundamental Elementsof Statistics


Fundamental Elements1. 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


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


Four Elements of Descriptive Statistical Problems

1. The population or sample of interest2. One or more variables (characteristics of the

population or sample units) that are to be investigated

3. Tables, graphs, or numerical summary tools4. Identification of patterns in the data


Five Elements of Inferential Statistical Problems

1. The population of interest2. One or more variables (characteristics of the

population units) that are to be investigated3. The sample of population units4. The inference about the population based on

information contained in the sample5. A measure of reliability for the inference


1.4

Processes


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


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.


1.5

Types of Data


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.


Types of Data

Types ofData

QuantitativeData

QualitativeData


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.

3

52

71

4

8

943

120 1221


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).

$$ Credit


1.6

Collecting Data


Obtaining Data

1. Data from a published source2. Data from a designed experiment3. Data from a survey4. Data collected observationally


Obtaining DataPublished source:

book, journal, newspaper, Web siteDesigned experiment:

researcher exerts strict control over unitsSurvey:

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


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.


Random SampleEvery sample of size n has an equal chance of selection.


1.7

The Role of Statistics in Managerial Decision Making


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.


Nonrandom Sample ErrorsSelection 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.


Real-World Problem


StatisticalComputer Packages

1. Typical Software• SPSS• MINITAB

• Excel

1. Need Statistical Understanding• Assumptions• Limitations


Key IdeasTypes of Statistical Applications

Descriptive1. Identify population and sample (collection of experimental units)2. Identify variable(s)3. Collect data4. Describe data


Key IdeasTypes of Statistical Applications

Inferential1. Identify population (collection of all experimental units)2. Identify variable(s)3. Collect sample data (subset of population)4. Inference about population based on sample5. Measure of reliability for inference


Key Ideas

Types of Data

1. Quantitative (numerical in nature)2. Qualitative (categorical in nature)


Key Ideas

Data-Collection Methods

1. Observational2. Published source3. Survey4. Designed experiment


Key Ideas

Problems with Nonrandom Samples

1. Selection bias2. Nonresponse bias3. Measurement error

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