Business Statistics in PracticeSeventh EditionAuthors: Bowerman / O’Connell / MurphreeMcGraw-Hill

Chapter 1

An Introduction to Business Statistics

Copyright © 2014 by The McGraw-Hill Companies, Inc. All rights reserved.McGraw-Hill/Irwin

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An Introduction to Business Statistics

1.1 Data1.2 Data Sources1.3 Populations and Samples1.4 Three Case Studies that Illustrate

Sampling and Statistical Inference1.5 Ratio, Interval, Ordinal, and Nominative

Scales of Measurement (Optional)

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1.1 Data

Data: facts and figures from which conclusions can be drawn

Data set: the data that are collected for a particular study◦Elements: may be people, objects, events, or

other entriesVariable: any characteristic of an element

LO1-1: Explain what a variable is.

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Data Continued

Measurement: A way to assign a value of a variable to the element

Quantitative: the possible measurements of the values of a variable are numbers that represent quantities

Qualitative: the possible measurements fall into several categories

LO1-2: Describe the difference between a quantitative variable and a qualitative variable.

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Cross-Sectional Data

Cross-sectional data: Data collected at the same or approximately the same point in time

Time series data: data collected over different time periods

LO1-3: Describe the difference between cross-sectional data and time series data.

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Time Series Data

LO1-4: Construct and interpret a time series (runs) plot.

Table 1.2 and Figure 1.1

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1.2 Data Sources

Existing sources: data already gathered by public or private sources◦ Internet

◦ Library

◦ Private data sources

Experimental and observational studies: data we collect ourselves for a specific purpose◦ Response variable: variable of interest

◦ Factors: other variables related to response variable

LO1-5: Describe the different types of data sources: existing data sources, experimental studies, and observational studies.

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Examples of Public Economic and Financial Data Sites

Table 1.3

LO1-5

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1.3 Populations and Samples

Population The set of all elements about which we wish to draw conclusions (people, objects or events)

Census An examination of the entire population of measurements

Sample A selected subset of the units of a population

LO1-6: Describe the difference between a population and a sample.

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Descriptive Statistics and Statistical Inference

Descriptive statistics: the science of describing the important aspects of a set of measurements

Statistical inference: the science of using a sample of measurements to make generalizations about the important aspects of a population of measurements

LO1-7: Distinguish between descriptive statistics and statistical inference.

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1.4 Three Case Studies That Illustrate Sampling and Statistical Inference

1. Estimating Cell Phone Costs

2. The Marketing Research Case: Rating a New Bottle Design

3. The Car Mileage Case: Estimating Mileage

LO1-8: Explain the importance of random sampling.

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Estimating Cell Phone Costs

Considering using a company to manage their cellular resources

Random sample of 100 employees on 500-minute plan

Many overages and underage

LO1-8

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The Cell Phone Case: The Data

LO1-8

Table 1.4

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The Marketing Research Case: Rating a New Bottle DesignStudying to see if changes should be made in

the bottle design for a popular soft drinkUsing “mall intercept method”Sample size of 60

LO1-8

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The Marketing Research Case: The Form and the Data

LO1-8

Figure 1.2 and Table 1.5

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Terms

Process: a sequence of operations that takes inputs and turns them into outputs

Finite population: a population of limited size

Infinite population: a population of unlimited size

LO1-8

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The Care Mileage Case: Estimating Mileage

Study of tax credit offered by the federal government for improving fuel economy

Automaker has introduced a new model and wishes to demonstrate it qualifies for the tax credit

Sample of 50 cars

LO1-8

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The Care Mileage Case: The Data

LO1-8

Table 1.6 and Figure 1.3

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1.5 Ratio, Interval, Ordinal, and Nominative Scales of Measurement (Optional)

Quantitative variables◦ Ratio variable: a quantitative variable measured on a scale

such that ratios of its value are meaningful and there is an inherently defined zero value

◦ Interval variable: a quantitative variable where ratios are not meaningful and there is no defined zero

Qualitative variables (categorical)◦ Ordinal variable: a qualitative variable for which there is

a meaningful ranking of the categories

◦ Nominative variable: a qualitative variable for which there is no meaningful ranking of the categories

LO1-9: Identify ratio, interval, ordinal, and nominative scales of measurement (optional).