1. Chapter One: An Introduction to Business
Statistics
Statistics Applications in Business and Economics
Basic Vocabulary Terms
Populations and Samples
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2. Applications in
Business and Economics
Accounting
Public accounting firms use statistical sampling procedures when conducting
audits for their clients.
Finance
Financial analysts use a variety of statistical information, including priceearnings ratios and dividend yields, to guide their investment
recommendations.
Marketing
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Electronic point-of-sale scanners at retail checkout counters are being used to
collect data for a variety of marketing research applications.
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3. Production
A variety of statistical quality control charts are used to monitor
the output of a production process.
Economics
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Economists use statistical information in making forecasts about
the future of the economy or some aspect of it.
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4. Basic Vocabulary Terms
Statistics is the art and science of collecting, analyzing,
presenting and interpreting data
Data are the facts and figures that are collected, summarized,
analyzed, and interpreted.
Data can be further classified as being qualitative or quantitative.
The statistical analysis that is appropriate depends on whether
the data for the variable are qualitative or quantitative.
In general, there are more alternatives for statistical analysis
when the data are quantitative.
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5. Qualitative Data
Qualitative data are labels or names used to identify an attribute
of each element.
Qualitative data use either the nominal or ordinal scale of
measurement.
Qualitative data can be either numeric or nonnumeric.
The statistical analysis for qualitative data are rather limited.
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6. Quantitative Data
Quantitative data indicate either how many or how much.
– Quantitative data that measure how many are discrete.
– Quantitative data that measure how much are continuous
because there is no separation between the possible values
for the data.
Quantitative data are always numeric.
Ordinary arithmetic operations are meaningful only with
quantitative data.
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7. Quantitative and Qualitative Data
A qualitative variable is a variable with qualitative data
A quantitative variable is a variable with quantitative data.
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8. Additional Terms
The elements are the entities on which data are collected.
The set of measurements collected for a particular element is
called an observation.
A variable is a characteristic of interest for the elements.
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10. Short Exercise
In the previous example, determine which
variables are qualitative and which are
quantitative.
Ans: Stock exchange is qualitative. Annual Sales and Earn/Shares is quantitative.
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11. Populations and Samples
The population is the set of all elements of interest in a particular
study.
A sample is a subset of the population.
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13. Descriptive Statistics and Statistical Inference
Descriptive Statistics is tabular, graphical, and
numerical methods used to summarize data.
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14. Example: Hudson Auto Repair
Descriptive Statistics
Graphical Summary (Histogram)
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Frequency
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10
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6
4
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50
60
70
80
90
100
110
Parts
Cost ($)
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15. Numerical Descriptive Statistics
The most common numerical descriptive statistic is the average (or
mean).
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Hudson’s average cost of parts, based on the 50 tune-ups
studied, is $79 (found by summing the 50 cost values and then
dividing by 50).
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16. Statistical Inference is the process of using information obtained
from analyzing a sample to make estimates about characteristics
of the entire population.
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17. Example: Hudson Auto Repair
Process of Statistical Inference
2. A sample of 50
engine tune-ups
is examined.
4. The value of the
sample average is used
to make an estimate of
the population average.
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1. Population
consists of all
tune-ups. Average
cost of parts is
unknown.
3. The sample data
provide a sample
average cost of
$79 per tune-up.
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18. Random Sampling
A procedure for selecting a subset of the population units in such
a way that every unit in the population has an equal chance of
selection. Since the validity of all statistical results depend upon
the original sampling process, it is essential that this process is
“blind”. This implies that every element in the population is
equally likely to be selected for the sample without bias.
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