| 1.
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Organize raw data into a frequency distribution. (I, II)
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| 2.
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Display data in graphic form. (I, II)
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| 3.
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Describe and calculate the data's central tendency (either grouped or
ungrouped). (II)
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| 4.
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Describe and calculate the data's measures of dispersion. (II)
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| 5.
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Identify the classical types of index numbers. (III)
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| 6.
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Construct an index. (III)
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| 7.
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Identify problem data as being either discrete or continuous. (IV)
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| 8.
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Use the binomial theorem and the Poisson tables in the solution of
discrete probability problems. (IV)
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| 9.
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Use the normal probability distribution in probability applications.
(IV)
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| 10.
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Describe the differences among various types of sampling techniques,
their uses, and misuses. (V)
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| 11.
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Use statistical inference techniques and confidence levels for
decision making when testing hypotheses. (VI)
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| 12.
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Use regression and time series analyses for estimation and
forecasting. (VII)
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| 13.
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Explain classical decision theory components. (VIII)
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| 14.
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Construct a decision tree. (VIII)
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| 15.
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Describe Bayes' theorem. (IX)
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