Behavioral Objectives for Business Statistics
The student should be able…
1: Introduction and Data Collection
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To explain the difference between a sample and a population
To explain the difference between a statistic and a parameter
To explain the difference between descriptive and inferential statistics
To identify categorical and numerical random variables
To identify discrete and continuous data
To explain the difference between various sampling schemes (random, stratified, cluster,
etc)
7. To select a simple random sample
2: Presenting Data in Tables and Charts
8. To interpret frequency histograms, relative frequency histograms, scatterplots, bar
charts, line (time series) charts, and pie charts
9. To identify deceptive graphic techniques, such as truncating the vertical axis or changing
both the height and width of pictograms
3: Numerical Descriptive Measures
10. To compute and interpret measures of central tendency, variation and shape such as the
mean, median, mode, range, standard deviation, variance, z-score, coefficient of
variation, minimum, maximum, first quartile, third quartile and interquartile range
11. To state and apply the Empirical Rule for symmetric, unimodal distributions and
Chebyshev’s theorem for non-symmetric distributions
12. To discuss conditions under which the median is preferred to the mean as a measure of
central tendency, and vice versa
13. To describe the shape, center, and dispersion of a data set verbally, graphically, and/or
numerically
4: Basic Probability
14. To define experiment, sample point, sample space, events, union, intersection,
collectively exhaustive events, mutually exclusive events, independent events
15. To use contingency tables for joint probabilities
16. To compute and interpret conditional probability including the revision of prior
probabilities using Bayes’ theorem (or contingency tables)
17. To apply the addition and multiplication rules of probability
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5: Discrete Probability Distributions
18. To compute and interpret the expected value (mean), variance and standard deviation of
a discrete random variable defined on a finite sample space
19. To identify binomial (and poisson) random variables and characterize their properties
20. To compute binomial (and poisson) probabilities
21. To compute the expected value, variance and standard deviation of a binomial (and
poisson) random variable
22. To apply the binomial (and poisson) distribution to problems in business and economics
6: The Normal Distribution and Sampling Distributions
The Normal Distribution
23. To identify normal random variables and characterize their properties
24. To compute probabilities for normal random variables using tables
25. To compute values of normal random variables for given probabilities
26. To assess the assumption of normality
27. To apply the normal distribution to problems in business and economics
Sampling Distributions
28. To apply the concept of a sampling distribution
29. To compute the expected value and variance of a sample mean
30. To compute the sampling distribution for the mean of a sample from a normal population
31. To apply the Central Limit Theorem to derive the approximate sampling distribution for
the mean of a sufficiently large sample from a non-normal population
32. To apply the Central Limit Theorem to derive the approximate sampling distribution
(including the expected value and variance) for the proportion of successes in a
sufficiently large sample
7: Confidence Interval Estimation
33. To interpret a confidence interval for a parameter
34. To construct a confidence interval for the mean when 2 is known
35. To construct a confidence interval for the mean when 2 is unknown
36. To construct a confidence interval for the proportion
37. To determine sample size for estimating a mean or proportion with a given level of
confidence and bound on error of estimation
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8: Fundamentals of Hypothesis Testing
38. To define alternative hypothesis, level of significance, null hypothesis, observed
significance level (p-value), power of the test, rejection region, test statistic, Type I and
Type II error
39. To explain the rationale of hypothesis testing
40. To test hypotheses about means and proportions using the steps of Hypothesis Testing
with rejection regions and decision rules and sketch the power curve for a single mean
hypothesis test.
41. To test hypotheses using computed p-values rather than rejection regions
42. To interpret the conclusions from a hypothesis test
9: Simple Linear Regression
43. To plot and interpret bivariate data as a scattergram with statistical software
44. To write a bivariate model and estimate the unknown parameters of the model from a
data set using statistical software
45. To interpret the least squares estimates of the slope and intercept (if appropriate)
46. To state and understand the importance of the LINE regression assumptions within the
context of a specific problem
47. To estimate the standard deviation of the probability distribution of the random error term
48. To statistically evaluate the usefulness of the model by testing H0 : 1 =0
49. To obtain and interpret a confidence interval estimate for the slope
50. To obtain and interpret Pearson's correlation coefficient and the coefficient of
determination
OPTIONAL
51. To obtain and interpret in context a confidence interval for the mean value of the
dependent variable Y given a value of the independent variable X
52. To obtain and interpret in context a prediction interval for an individual value of the
dependent variable Y given a value of the dependent variable X
53. To obtain and analyze residuals for impact on the estimated model coefficients and
resulting predictions
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