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Syllabus for
ECON 2300, BUSINESS STATISTICS
Fridays, 8 am – 1:45 pm, BB-372, Summer 2005
Professor: Penny Verhoeven
e-mail: [email protected]
Office: BB-324
Office Phone: (770) 423-6537
Office Hours: M 9-10:30 am
W 2-3:30 pm
F 2-4 pm
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Course Description from Current Catalog
ECON 2300. Business Statistics. 3-0-3.
Prerequisite: MATH 1101.
An introduction to descriptive and inferential statistics with an emphasis on business applications.
Topics covered include data summarization, probability distributions, sampling methods, confidence
intervals, hypothesis testing, online data sources, and ethics in research. Small case studies are used
to illustrate statistical applications within business settings.
Course Objectives
1. Convey methods for organizing, summarizing, and presenting data;
2. Convey methods for drawing conclusions about entire sets (populations) of data when only portions
(samples) of those data sets are examined, as well as the rationale behind those methods; and
3. Illustrate the role of data analysis in supporting the decision-making process in a variety of business
contexts.
4. Promote ethical conduct in research.
Course Material Available at prof’s website http://ksuweb.kennesaw.edu/~pverhoev :
 this Syllabus
 Unit One, Unit Two, and Unit Three packets, which contain: information to reduce note-taking in
class, answers to all homework problems, practice problems for tests; and formula sheets
 Online Data Sources: Search Guidelines
 Optional (for interested parties): Excel Instructions for performing various analyses using Excel.
Required Text: Essentials of Statistics for Business and Economics (3rd ed.), by D.R. Anderson, D.J.
Sweeney, and T.A. Williams, South-Western, 2003.
Required Ethics in Research Readings (will be distributed in class):
1) “Ethical Guidelines for Statistical Practice,” Committee on Professional Ethics of the American
Statistical Association (ASA), 1999. URL:
www.amstat.org/profession/index.cfm?fuseaction=ethicalstatistics.
2) Ethics case (for in-class group discussion) entitled “Ethics of Data Quality.” ASA Case Study #3.
URL: www.tcnj.edu/~ethcstat.
3) The following sections of On Being a Scientist: Responsible Conduct in Research, National
Academy of Sciences (NAS), Washington, D.C.: National Academy Press, 1995 (URL:
www.nap.edu/readingroom/books/obas/): (1) “Misconduct in Science;” and (2) “Responding to
Violations of Ethical Standards,” which contains the ethics case (for in-class group discussion)
entitled “A Career in the Balance.”
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Other material required: calculator
Last day to withdraw without academic penalty: Wednesday, June 29. Current KSU policy regarding
withdrawals is in effect.
Tutoring: available in BB-292 (once determined, a tutoring schedule will be posted outside the door); also,
you can post questions about homework problems on WebCT at the ECON 2300/Common site and
tutors will respond to your questions
Determination of Grade in Course
 There will be three tests, weighted evenly.
 Relationship between average test score and letter grade: [90,100) = A, [80,90) = B, [70,80) = C,
[60,70) = D, below 60 = F.
 Only under compelling circumstances may a test be taken at other than the scheduled time.
Receiving a Grade of Incomplete: Current KSU policies for receiving (and subsequently removing) a
grade of incomplete are in effect. As stated on pages 42-43 of the KSU 2004-2005 Undergraduate
Catalog, an incomplete “…will be awarded only when the student has done satisfactory work up to the
last two weeks of the semester, but for non-academic reasons beyond his/her control is unable to meet the
full requirements of the course.”
Class Attendance and Homework
To maximize your learning, it is essential that you attend every class and complete all of your homework
assignments. If you must miss a class, you should--prior to approaching the professor should questions
about that day's coverage arise--get the notes from a classmate, study the notes and associated text
material, and work on (i.e., at least attempt) the homework problems assigned that day.
Notice regarding class enrollment: Students must exclusively attend the course section in which they are
officially enrolled. Your grade will be based on your performance in that course section. It is your
responsibility to check your registration form and confirm that you are attending the course and section in
which you are enrolled. There will be no adjustments made to class rolls once the Drop-Add period ends.
Academic Integrity
All work on the tests is to be your independent work.
Every KSU student is responsible for upholding the provisions of the Student Code of Conduct, as
published in the Undergraduate and Graduate Catalogs. Section II of the Student Code of Conduct
addresses the University's policy on academic honesty, including provisions regarding plagiarism and
cheating, unauthorized access to University materials, misrepresentation/falsification of University
records or academic work, malicious removal, retention, or destruction of library materials,
malicious/intentional misuse of computer facilities and/or services, and misuse of student identification
cards. Incidents of alleged academic misconduct will be handled through the established procedures
of the University Judiciary Program, which includes either an "informal" resolution by a faculty
member, resulting in a grade adjustment, or a formal hearing procedure, which may subject a student
to the Code of Conduct's minimum one semester suspension requirement.
Posting of Grades: You may access your course grade via Owl Express at www.kennesaw.edu by 4 pm on
Saturday, July 30. By departmental policy, no grades will be given over the phone. Your graded third
test may be picked up during the Fall 2005 term; alternatively, if you submit a stamped, self-addressed
envelope with your third test, your graded test will be mailed to you.
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COURSE OUTLINE
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Friday
Class
Topics and associated readings (from Anderson et al text unless otherwise specified),
Homework for ensuing week (homework is from the EXERCISES sections of the Anderson
et al text unless otherwise indicated)
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June 3
Topics: Types of Data; Frequency, Relative Frequency, and Percent Frequency Distributions;
Summary Measures; Probability Concepts; Discrete Probability Distributions
Readings: Chapter 1, Sections 2.1-2.2, Sections 3.1-3.3, Sections 4.1-4.4, Sections 5.1-5.3
Homework: Ch.1: 5, 6, 14, 22. Ch. 2: 9 [in part c, construct the bar graph portraying the frequency
distribution; in part d, construct the pie chart portraying the percent frequency distribution], 18e [construct the
histogram portraying the percent frequency distribution], 19 a,b [also portray the frequency distribution as a
histogram], 20, 21b [also portray the relative frequency distribution as a histogram], 37 [in part c, construct
the bar graph portraying the frequency distribution]. Ch. 3: 5a-c, 12, 22, 25, 26. Ch. 4: 10a-d, 18,
19, 25, 28, 32, 34[assume that the blood type of one spouse is independent of that of the
other spouse], 35, 37. Ch. 5: 17, 21[treat the given probability distributions as estimates of the true
probability distributions, and thus treat your answers as estimates].
◄ In preparation for the next class, read Ethics in Research Readings 1) and 2).
June 10
Topics: Online Data Sources; Ethics in Research; the Binomial Distribution; Continuous
Probability Distributions; the Normal Distribution
Readings: Ethics in Research Readings 1) and 2), Section 5.4, p. 227 (first half), Section 6.2
Homework: Take-home problem on online data sources for Test #1 (due next class).
Ch. 5: 28, 31, 34, 59. Ch. 3: 32[apply the Empirical Rule], 33[apply the Empirical Rule], 34. Ch. 6:
9b-c[apply the Empirical Rule], 12, 13, 20, 22[in part b, change, in the question, “top 20%” to “top 10%”],
25, 37.
June 17
Review for Test #1 (8-10 am). Test #1 (on objectives 1-11) begins at 11 am. Take-home
Test #1 problem on online data sources due by 11 am.
◄ Bring to the next class a copy of the Unit Two packet, available at prof’s website
June 24
Topics: Sampling Methods; Point Estimates; Sampling Distribution of the Mean; Central
Limit Theorem; Interval Estimation of a Population Mean; Student’s t distribution; Sampling
Distribution of the Proportion; Interval Estimation of a Population Proportion; Determining
an appropriate sample size
Readings: Sections 7.1-7.3, 7.4-7.7, 8.1-8.4, Case Problem 3 in Chapter 8
Homework: Ch. 7: 3, 6, 13, 15, 16, 17. Ch. 8: 10, 11(x = 6.34 , s = 2.16),
12(x = 3.8 , s = 2.3), 17, 19(x = 6.5 , s = .5), 21, 27, 29, 36, 41, 51, 54, 56, 59a, 60.
◄ In preparation for the next class, read Ethics in Research Reading 3).
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Friday
Class
Topics and associated readings (from Anderson et al text unless otherwise specified),
Homework for ensuing week (homework is from the EXERCISES sections of the Anderson
et al text unless otherwise indicated)
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July 1
Topics: Online Data Sources; Interval Estimation of the Difference between Two Population
Means; Interval Estimation of the Difference between Two Population Proportions; Testing
Hypotheses about a Population Mean; Ethics in Research; Testing Hypotheses about a
Population Proportion; Type I and Type II Errors
Readings: Sections 10.1, 11.1, Case Problem 1 in Chapter 10 (questions 3-5),
Sections 9.1-9.6, Case Problem 2 in Chapter 9, Ethics in Research Reading 3)
Homework*: Take-home problem on online data sources for Test #2 (due next class). Ch.
10: 5, 6[note: for Miami, x = 6.34 , s = 2.16; for LA, x = 6.72, s = 2.37], 7a,c. Ch. 11: 3, 8b, 31b.
Ch. 9: 5, 6, 7, 8, 16, 17, 19, 25, 30, 38[assume normally distributed expenditures by
Corning households], 41[assume normally distributed driving distances], 42[assume
normally distributed planting times], 48, 50, 59, 66, 68[is there evidence that the proportion is now
below .47?].
*note: For all hypothesis testing problems in this course: (1) test the given or implied null
hypothesis using the p-value approach to hypothesis testing, and (2) if the authors specify a
significance level , determine whether the null hypothesis would be rejected at that
significance level.
July 8
Review for Test #2 (8-10 am). Test #2 (on objectives 5, 12-21) begins at 11 am. Takehome Test #2 problem on online data sources due by 11 am.
◄ Bring to the next class a copy of the Unit Three packet, available at prof’s website
July 15
Topics: Chi-square test of independence; Covariance and Correlation Coefficient; Simple
Linear Regression
Readings: Sections 11.3, 3.5, 12.1-12.5, 12.8
Homework: Ch. 11: 21, 22, 40. Ch. 3: 50, 52, 69b. Refer to the Regression Homework in
the Unit Three packet and work problems 1a-f, 2a-f, and 3a-f.
July 22
Topics: Simple Linear Regression; Discussion of Multiple Linear Regression; Review for
Test #3
Readings: Section 12.6
Homework: Refer to the Regression Homework in the Unit Three packet and work
problems 1g-j, 2g-j, 3g-j, and 4a-j.
July 29
Test #3 (on objectives 22-29) from 8:30-10:30 am (time reserved for final exam)
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LEARNING OBJECTIVES FOR ECON 2300
At the end of this course, the student will be able to:
1.
Distinguish between, and both recognize and provide examples of:
(a) populations and samples
(b) parameters and statistics
(c) descriptive statistics and inferential statistics
(d) qualitative data and quantitative data
(e) cross-sectional data and time series data
(f) discrete variables and continuous variables
2.
Identify what each of the following symbols represents, and recognize each symbol’s referent in a
verbal passage:
(a) N
(e) n
(i) p
(b) 
(f)x
(j)p
(c) ²
(g) s²
(d) 
(h) s
3.
Summarize a set of quantitative data by:
(a) constructing a frequency distribution and portraying the frequency distribution in the form of a
histogram
(b) constructing a relative frequency distribution and portraying the relative frequency distribution in
the form of a histogram
(c) constructing a percent frequency distribution and portraying the percent frequency distribution in
the form of a histogram
(d) determining the mean, median, mode, variance, standard deviation, and coefficient of variation of
the data set
4.
Summarize a set of qualitative data by:
(a) constructing a frequency distribution and portraying the frequency distribution in the form of a bar
chart or Pareto diagram or pie chart
(b) constructing a relative frequency distribution and portraying the relative frequency distribution in
the form of a bar chart of Pareto diagram or pie chart
(c) constructing a percent frequency distribution and portraying the percent frequency distribution in
the form of a bar chart or Pareto diagram or pie chart
5.
Obtain requested data from the following websites: www.bea.gov, www.bls.gov, www.census.gov,
www.sec.gov, www.stlouisfed.org, http://unstats.un.org
6.
With respect to probability concepts:
(a) distinguish between the classical, relative frequency, and subjective methods of assigning
probabilities to experimental outcomes
(b) define what it means for two events to be independent, and identify whether or not two events are
independent
(c) define what it means for two events to be mutually exclusive
(d) apply basic probability identities (rules) to calculate probabilities
7.
Determine the expected value (mean), variance, and standard deviation of a discrete random variable
from its probability distribution.
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8.
Answer a probability question about a variable having a binomial distribution.
9.
Answer a probability question about a variable having a normal distribution.
10.
Identify four characteristics true of all normal distributions.
11.
Apply the Empirical Rule to answer questions about, or describe, a quantitative data set whose
distribution is bell-shaped (thus normal or approximately normal) and for which the mean and
standard deviation are known.
12.
Suggest four reasons why, to obtain information about populations, one might draw samples from the
populations rather than examining the populations in their entirety.
13.
Define what is meant by a simple random sample of size n, and draw such a sample from a given
population.
14.
With respect to sampling distributions:
(a) state the Central Limit Theorem
(b) graph one population distribution and three associated sampling distributions of the mean that
exemplify the Central Limit Theorem
15.
Define what is meant by a C% confidence interval for a population parameter.
16.
Identify four characteristics true of all t-distributions.
17.
Obtain an interval estimate for:
(a) the mean of a population (excluding situations where a small sample is drawn from a nonnormally distributed population)
(b) the proportion of a population falling in a certain category (excluding situations where a small
sample is drawn)
(c) the difference between the means of two populations (excluding situations where small samples
are drawn)
(d) the difference between the proportions of two populations falling in a certain category (excluding
situations where small samples are drawn)
18.
Determine how many entities should be sampled to obtain an interval estimate--for a mean or
proportion--having a given level of confidence and a given margin of error.
19.
Test--using the p-value approach--a hypothesis about:
(a) the mean of a population (excluding situations where a small sample is drawn from a nonnormally distributed population); or
(b) the proportion of a population falling in a certain category (excluding situations where
a small sample is drawn)
20.
In the context of hypothesis testing:
(a) distinguish between the significance level  and the p-value p
(b) based on the p-value, determine whether the null hypothesis would be rejected at a given
significance level 
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21.
Distinguish, in the context of hypothesis testing, between a Type I error and Type II error, and, for a
particular null hypothesis, suggest one negative consequence of making a Type I error and one
negative consequence of making a Type II error.
22.
Given a scenario about real (or potential) ethical misconduct in statistical practice:
(a) identify an ethical guideline of the American Statistical Association that is (or may be) being
violated, and explain how that guideline is (or may be) being violated
(b) provide pros and cons for two proposed courses of action by a party involved in the scenario.
23.
Perform a chi-square test of independence.
24.
With respect to the covariance and the (Pearson product-moment) correlation coefficient:
(a) describe what each measures
(b) calculate each measure given a sample of n (x,y) data points or a population of N (x,y) data points
(c) given a sample of (x,y) data points depicted in a scatter diagram, provide a reasonable estimate of
the sample correlation coefficient
(d) given a description of two variables, identify which type of correlation between the variables is
expected: positive, negative, or none.
25.
Describe three potential purposes of regression analysis, and differentiate between simple and
multiple regression analysis.
26.
With respect to the (classical normal) simple linear regression model:
(a) express (symbolically) the model
(b) describe the meaning of the residual term in the model
(c) state the model assumptions
(d) distinguish between the population regression equation and sample regression equation
27.
Describe, in the context of regression analysis, the least squares criterion for arriving at the “bestfitting” sample regression equation.
28.
Given an Excel printout containing a sample of (x,y) data points, a scatter diagram of the data points,
and the results of applying Excel’s REGRESSION tool:
(a) write down the sample regression equation
(b) graph (superimposed on the scatter diagram) the sample regression equation, and label two points
on that line
(c) locate (in the Excel output) and interpret (in the context of the problem) se, the standard error of
the estimate
(d) refer to the residual plot (in the Excel output) and make a judgment as to whether any (and, if so,
which) of the model assumptions appear to be
(e) verbalize what each of SST, SSR, and SSE represent in the context of the problem, and locate (in
the Excel output) and interpret (in the context of the problem) r2
(f) test for a linear relationship between the dependent and independent variables
(g) obtain a point estimate for the mean value of the dependent variable across all entities having a
specified value for the independent variable
(h) obtain a point estimate for the value of the dependent variable for a single entity having a
specified value for the independent variable
(i) interpret the sample regression coefficient (slope of the sample regression line) in the context of
the problem
(j) suggest two additional independent variables which might enable one to better predict or estimate
the value of the dependent variable.
29. Identify 4 precautions associated with regression analysis.