> @ ͦbjbjFF ,,Nlllllll4($($($h$<%i2%&"&&&& &&hhhhhhhjR=mhl/&&//hll&&si)4)4)4/vl&l&h)4/h)4)4jbllf&%f($/
d2h,i0idAn2fAndfllllAnlf&D))4*L,%&&&hhD#D4#____________________________________________________________________________________
Syllabus for
ECON 2300, BUSINESS STATISTICS
Fridays, 8 am 1:45 pm, BB372, Summer 2005
Professor: Penny Verhoeven email: pverhoev@kennesaw.edu
Office: BB324 Office Phone: (770) 4236537
Office Hours: M 910:30 am
W 23:30 pm
F 24 pm
____________________________________________________________________________________
Course Description from Current Catalog
ECON 2300. Business Statistics. 303.
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 decisionmaking process in a variety of business contexts.
4. Promote ethical conduct in research.
Course Material Available at profs website HYPERLINK "http://ksuweb.kennesaw.edu/~pverhoev" http://ksuweb.kennesaw.edu/~pverhoev :
this Syllabus
Unit One, Unit Two, and Unit Three packets, which contain: information to reduce notetaking 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, SouthWestern, 2003.
Required Ethics in Research Readings (will be distributed in class):
Ethical Guidelines for Statistical Practice, Committee on Professional Ethics of the American Statistical Association (ASA), 1999. URL: HYPERLINK "http://www.amstat.org/profession/index.cfm?fuseaction=ethicalstatistics" www.amstat.org/profession/index.cfm?fuseaction=ethicalstatistics.
Ethics case (for inclass group discussion) entitled Ethics of Data Quality. ASA Case Study #3. URL: HYPERLINK "http://www.tcnj.edu/~ethcstat" www.tcnj.edu/~ethcstat.
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: HYPERLINK "http://www.nap.edu/readingroom/books/obas/" www.nap.edu/readingroom/books/obas/): (1) Misconduct in Science; and (2) Responding to Violations of Ethical Standards, which contains the ethics case (for inclass group discussion) entitled A Career in the Balance.
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 BB292 (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 4243 of the KSU 20042005 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 nonacademic 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 shouldprior to approaching the professor should questions about that day's coverage ariseget 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 DropAdd 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, selfaddressed envelope with your third test, your graded test will be mailed to you.
COURSE OUTLINE
_______________________________________________________________________________________
Friday Topics and associated readings (from Anderson et al text unless otherwise specified),
Class Homework for ensuing week (homework is from the EXERCISES sections of the Anderson et al text unless otherwise indicated)
_______________________________________________________________________________________
June 3 Topics: Types of Data; Frequency, Relative Frequency, and Percent Frequency Distributions; Summary Measures; Probability Concepts; Discrete Probability Distributions
Readings: Chapter 1, Sections 2.12.2, Sections 3.13.3, Sections 4.14.4, Sections 5.15.3
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: 5ac, 12, 22, 25, 26. Ch. 4: 10ad, 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: Takehome 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: 9bc[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 (810 am). Test #1 (on objectives 111) begins at 11 am. Takehome 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 profs website
June 24 Topics: Sampling Methods; Point Estimates; Sampling Distribution of the Mean; Central Limit Theorem; Interval Estimation of a Population Mean; Students t distribution; Sampling Distribution of the Proportion; Interval Estimation of a Population Proportion; Determining an appropriate sample size
Readings: Sections 7.17.3, 7.47.7, 8.18.4, 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).
_____________________________________________________________________________________________________
Friday Topics and associated readings (from Anderson et al text unless otherwise specified),
Class Homework for ensuing week (homework is from the EXERCISES sections of the Anderson et al text unless otherwise indicated)
_______________________________________________________________________________________
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 35),
Sections 9.19.6, Case Problem 2 in Chapter 9, Ethics in Research Reading 3)
Homework*: Takehome 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 pvalue 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 (810 am). Test #2 (on objectives 5, 1221) 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 profs website
July 15 Topics: Chisquare test of independence; Covariance and Correlation Coefficient; Simple Linear Regression
Readings: Sections 11.3, 3.5, 12.112.5, 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 1af, 2af, and 3af.
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 1gj, 2gj, 3gj, and 4aj.
July 29 Test #3 (on objectives 2229) from 8:3010:30 am (time reserved for final exam)
LEARNING OBJECTIVES FOR ECON 2300PRIVATE
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) crosssectional data and time series data
(f) discrete variables and continuous variables
2. Identify what each of the following symbols represents, and recognize each symbols referent in a verbal passage:
(a) N (e) n (i) p
(b) ( (f)(x (j)(p
(c) ( (g) s
(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: HYPERLINK "http://www.bea.gov" www.bea.gov, HYPERLINK "http://www.bls.gov" www.bls.gov, HYPERLINK "http://www.census.gov" www.census.gov, HYPERLINK "http://www.sec.gov" www.sec.gov, HYPERLINK "http://www.stlouisfed.org" www.stlouisfed.org, HYPERLINK "http://unstats.un.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.
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 bellshaped (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 tdistributions.
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 estimatefor a mean or proportionhaving a given level of confidence and a given margin of error.
19. Testusing the pvalue approacha 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 pvalue p
(b) based on the pvalue, determine whether the null hypothesis would be rejected at a given significance level (
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 chisquare test of independence.
24. With respect to the covariance and the (Pearson productmoment) 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
state the model assumptions
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 Excels 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
U]abgi ,  Z [ {tihA5CJ\aJh5hh'h~q+h\@CJh~q+@CJh~q+h~q+5@CJh'@CJh@CJh\5@CJh~q+5@CJha)5@CJhrj5@CJh_5@CJhf5@CJhB:5@CJh5@CJh5@CJ&Ub = M [
"
$
0P*$^`Pa$^gdf1$
$
0*$a$
$
*$a$gd
$
*$a$̦
"
K
L
i
j
x
y
BI{md\QIQIQIQhI">CJaJh>sshI">CJaJhCJaJhI">5CJaJh>sshI">0J5CJaJ&jh>sshI">5CJUaJ jh>sshI">5CJUaJhiyQ5CJaJh>sshI">5CJaJhEShI">CJaJhXdd@CJh5@CJh@CJhfhf6CJ]aJhfhfCJaJhfhf5CJ\aJ"
K
L
&'*xx
&FgdI^gd ^ gdES$
0 *$^ `a$gd
$
0*$a$$
&F
0*$a$gdSE$
&F
0*$a$gdI">$
0 *$^ `a$gdI">h^hgdI">$
0P*$^`Pa$
$%&'057j{ƾzqh`XPHhES@CJhXdd@CJhYH@CJhX@CJhES5@CJh\5@CJhXdd5@CJhX5@CJhSE5@CJh>*@CJhXhX@CJhXhX5@CJh5@CJh@CJhEhI">CJaJhI">h>sshI">CJaJhI">CJaJhDNCJaJh:nCJaJhSECJaJ'()*6Vܲܪ袚naYQIh'CJaJh_CJaJhESCJaJhYHhX0JCJaJ#jhYHhXCJUaJjhYHhXCJUaJhYHhXCJaJh(\CJaJhXCJaJhE@CJh;Ph0J@CJ#j h;Ph@CJUhh@CJjh@CJUhES@CJhy&@CJh@CJ'EKMP 789~~shZhXddh>ss5@CJaJh\5@CJaJh>ss5@CJaJh(\CJaJhI^CJaJhESCJaJh'CJaJhESh_0JCJaJ#johESh_CJUaJjhESh_CJUaJh_@CJhESh_@CJaJhESh_CJaJhSEh_6CJaJh_CJaJAB./PVW
) ^ `gd^
&FgdSE
&FgdC
&FgdC
$
0*$a$gdC
$
0*$a$$
0P*$^`Pa$gd
$
0*$a$gd>ss
&FgdI^ABJ./PRSVvy
)zvrjbjbjbjvYh5@CJhh,{5hh5hh hiyQ6hh6hChC5@CJh>ss5@CJh@CJh(@CJhh5@CJh@CJh@CJh(\@CJhC@CJh5@CJh\5@CJh>ss@CJhXddh>ss@CJaJ )D45^rkl}# % G Ļxpxh\SxKh@CJhC5@CJhlhl5@CJhl@CJh,{@CJhC@CJhkE@CJh5@CJh5@CJ
hXK5@hXKhCJaJ
hCJh56CJh56CJ
h5CJh5@CJ\h56@CJ\]h>*@CJh56@CJh@CJ)]^qrkl P!Q!!."$
0`*$^``a$gd1r
$
0*$a$gdere$
0a$gdbv
0$
0 *$^ `a$gd$ ^ a$gdXK$ ^ a$
$
0*$a$$
0 *$^ a$ P!Q!W!X!^!o!w!!!!!!!"""""""""""##żűżxpjdYQhLTCJaJh_hLTCJaJ
hLTCJ
h_CJh_@CJh>*@CJh@CJh=K(@CJh?q@CJh@CJh1r@CJhere@CJh1rhere@CJh1r5@CJhere5@CJh5@CJh=K(5@CJhbv5@CJhhbvhhCh@CJ."""5#7###~'(((() )[)r*$
0`*$^``a$gd$
0`*$^``a$gdLT
$
0*$a$gd$
0*$^a$gd$
0*$^a$$
0`*$^``a$gd'
$
0*$a$#4#5#6#8#@#^#p######Z$b$$$$$3%:%D%E%%%%%%j&'~''((((( (J(ƾζΰΰΰΰΰΰƪ矛wowfwhkE>*@CJh=K(@CJhkE@CJh@CJh@CJOJQJ^J
hCJhLThLThLTCJaJ
h_CJ
h@h^A@CJhLT@CJh_@CJh@CJh>*@CJh'@CJ
hLTCJh^ACJaJh"LhLTCJaJ&J(L(O(e(g((((((((((((()) ))N)X)Y)Z)[)r)t))))))))Z*v*ξƶƓxphbbbb
h@hCJaJhCxCJaJh"LhkECJaJ
h=K(CJh#hkE>*CJh@CJhkE@CJhkE>*@CJ
hkECJhkEh"L@CJh^A@CJhCx@CJhLT@CJhLTCJaJh"Lh"LCJaJhCx@CJaJh"Lh"L@CJaJ$r*x*+,.,`a.P/R/000
$
0*$a$$
0*$^a$gd$
0`*$^``a$gd"L
$
0*$a$gd"L```gd^`gd"Lgdx
$
0*$a$gdkEv*x******$+8+H+++++++,+,,.,2,5,6,7,<,?,b,,9_`ackw缶ȥȒyyqyhCx@CJh@CJh"L>*@CJh"L@CJh^Ahfmhhh"L>*hh>*hrcYh"LaJ
h"LaJh"LOJQJ^JaJh"L h=K(5h#h=K(5h=K(5\h5\hxhh=K(h"LhkE@CJaJ(...,.>.@.b.j.n....P/R/V/0000ļ~vjfb^bUh5@CJhs[hhh"LOJQJ^JaJh@CJh@CJOJQJ^J h"Lh"LCJOJQJ^JaJh$CJaJ
h$CJh j`h
hCJhCJaJhhCJaJh"Lh"LCJaJh"Lh"L>*CJaJh"Lh"L@CJaJh"Lh^A@CJaJ0090n000H1I1M1O1P1V1Y1n11&222222222222223 3&3'3(3)313ƾҵҭҥҵҍ҅zok_hxh>*CJaJh^AhCxhCJaJhCxh8"CJaJh!@CJhCx@CJh^A@CJh8"@CJh4@CJh@CJh>*@CJh@CJh=K(@CJhh@CJh1r@CJh1rh1r@CJh1r5@CJhbv5@CJ#0p00H1I1222'3(3 5b6d67gd8"gd,{$
0*$^a$gdx$
0`*$^``a$gd8"$
0`*$^``a$gd4$
0`*$^``a$^
$
0*$a$$
0`*$^``a$gd1r
132353n3y3}333333333333334
4Y44445 5555b6d6p6r66огypga[Wh8"
hZ@aJ
h8"aJhs@CJh jahxCJhx5CJ\
h8"@h8"@CJh$@CJ
h$CJ
hxCJhxhs[hxCJaJhs[hCJaJh$hCJaJ j`hhh=K(CJaJhxh8pCJaJhxhCJaJhxhx5>*CJaJ"67*7:7777777'8(8/8086898Z88888888888888888888889>9E9P9R9f9躲ÆzvvrnrnrhZ@hdihC hC>*h^Ah^A
hCaJ hCxh h8"hhC>*h hChh^A@CJh8"@CJhC@CJhC>*@CJ
h8"aJh8"CJaJh8"h8"CJaJh8"OJQJ^JaJh8" h8"5h#h8"5h8"5\+7'8(88888g9i99999a:d:$
0`*$^``a$gdZ@
$
0*$a$$
0`*$^``a$gddi$`a$gdrcYgdZ@gd^A1$gdC$
0`*$^``a$gd8"
$
0*$a$gdC
$
0*$a$gd8"f9g9h9i9q9w999999999992:8:D:E:U:_:a:c:d:k:l:s:t:::::zvrkrd]kVhdi5\hx5\hDN5\h5\hhdihC@CJhZ@hCCJaJhZ@hB{CJaJhZ@hZ@CJaJhZ@hCJaJhZ@h>*CJaJ
h@h^A@CJh@CJh>*@CJhdi@CJ
hrcY5aJh>sshrcY5aJhC5OJQJ^JaJ ::::::::::::::::%;<=====
===='=(=9A:A=AoApAޠxxmme]eUhMu@CJhx@CJhE@CJ jsh@CJhB:h@CJaJ j`hB:h@CJaJ jmh@CJh@CJjh@Uh@CJjh@CJUh5@CJhJ5@CJh5@CJhhfhChdih hdi\!d:::::::';(;l;;;;<:<k<l<<<
00*$^`0gdI9$
0*$^a$$
00*$^`0a$
$
0*$a$
$
H*$a$
$
H*$a$$
0a$gd00<=!=1=2=^==N>>@?B?m??@9A:ABBB$
0*$a$gdE$
00*$^`0a$gdx$
00*$^`0a$
0*$^gdI9$
00*$^`0a$
$
0*$a$pAqArAAAAAAAAAAAAAAAAAAABB
BB+B,BB8B9B;BO?OKOLOOOOO P
PPPPPPPPPPPPPP"Q'Q+Q4Q5Q9QCQOQQQRR6S8SSSSXTYT[TTTTUUUUU{VVWøh@CJH*h@CJhDN@CJhJ6@OJQJhh$9@OJQJhh@OJQJh
hJ6CJ
h$9CJ
hCJh@CJhf@CJhx@CJh@CJ:PPPQ5QQ(RR6SSSSTyj$
&F
0*$a$$
0*$^a$gdDN$
0*$^a$$
0*$a$$
00*$^`0a$
08*$^8`gdf
0*$1$^
0*$^
$
0*$a$$
&F
0*$a$TYTTUU*VV8WWSts&`#$
0h*$^h`gd!
08*$^8`gd!
08*$^8`
00*$^`0gd$
00*$^`0a$gd$
&F
0*$a$gdDNWWWrsuŦƦǦȦɦ˦̷̦ͦ³hh0JmHnHuhCx
hCx0JjhCx0JU
hCJhx@CJh!@CJUh@CJh@CJH*(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.
PAGE
PAGE 2
ɦʦ˦̦ͦ
0h*$^h`gd!&`#$h]h5....()()))()() 0
00P8$:p!/ =!"#`$%5....()()))()() 0
00P8$:p!/ =!"#`$%5....()()))()() 0
00P8$:pJ/ =!"#`$% DyK%http://ksuweb.kennesaw.edu/~pverhoevyKJhttp://ksuweb.kennesaw.edu/~pverhoevDyKAwww.amstat.org/profession/index.cfm?fuseaction=ethicalstatisticsyKhttp://www.amstat.org/profession/index.cfm?fuseaction=ethicalstatisticsDyKwww.tcnj.edu/~ethcstatyK<http://www.tcnj.edu/~ethcstatDyK$www.nap.edu/readingroom/books/obas/yKVhttp://www.nap.edu/readingroom/books/obas/TDphoenixDyKwww.bea.govyK(http://www.bea.gov/DyKwww.bls.govyK(http://www.bls.gov/DyKwww.census.govyK.http://www.census.gov/DyKwww.sec.govyK(http://www.sec.gov/DyKwww.stlouisfed.orgyK6http://www.stlouisfed.org/DyKhttp://unstats.un.orgyK.http://unstats.un.org/8@8Normal_HmH sH tH Z@Z Heading 1$$
0*$1$@&a$@CJhtH uR@R Heading 2$$
0*$@&a$5@CJH@H Heading 3$*$@&
0@CJR@R Heading 4$$
*$@&a$5@CJd@d Heading 5)$$
0P*$@&^`Pa$5@CJ\DA@DDefault Paragraph FontViVTable Normal :V44
la(k(No ListN+@NEndnote Text1$CJOJQJhtH uBOB HTML Body_HhmH sH tH HC@HBody Text Indent
^CJbR@"bBody Text Indent 2$
0*$^a$@CJ4@24Header
!.)@A.Page NumberjS@RjBody Text Indent 3#$
0`*$^``a$@CJ6U@a6 Hyperlink>*B*phFV@qFFollowedHyperlink>*B*ph%.N7nUb =M["KL&'* AB.
/
P

VW
)]^qrklPQ.57 [ 9!>X?Y?@@A@@!AAABAlAA!B"BCCyC$DDDDDE5EE(FF6GGGGHYHHII*JJ8KK5LLVMMUNNNNNNNNN000000x0000000000000000000 0 0 0 000000 0 0 00000x0000 0 0 00
H0
0W0W0W0W0W0W0W0W0W0W0W00x0x0x00x0x0x00x00x00@0x000000000 0x000 0x0 0 0 0 000x0 000x0x0x0x0 00 0 0x0(0x0000x00(00,0,(0,(0,(0,x0,(0,x0,(0,00,(0,00,0.0.x0.x0.x0.x0.x0.x0.0.x0.x0.x0.x0.x0.x0.x0.0.0.x0.00.00.00.00.0.x0.x0.x0.x0.x0.x0.00.x0.00. 0.0.(0.0. 0.00. 0. 0.0.x0.x0.x0.x0.x0.x0.0.x0.x0.x0.0.x0.x0.x0.0.x0.x0.0.x0.x0.x0.x0.x0.x0.x0.00.00.00. 0.0 0. 0.0.0.00. 0.H0.x0.x 0.x0.x0.x 0. 0.0.0.0.x0.x0.0.x0.x0.x0.x0.x 0.x 0.0x000000x0x0x0x0x0000 0 @0@0@0@0@0@00ҝLN@00} ) #J(v*0136f9:pAyB NWͦ,/123568:;=>?ABDEHIMP"
)."r*07d:<BhGALPTͦ0479<@CFGJKLNOȚ.x'
q555555556
6,686<6e6x6{666NXXXXXXXXXX!!_Hlt90272647N@N)NoElOoElPoEQoEDRoESoEToEDUoEVoEWoEXoEYoEZoE[oED\oE]oE^oE_oED`oEaoEboE coED doE eoELfoEgoELhoEioEjoEkoELloEmoEnoEooELpoEqoELroEsoEtoELuoEvoE2CTM
M
Y
88t t X!!!c#c#''((D)D)M+++,,.N
!"#$%&'(<LZW
]
]
@@w w _!!!k#k#''((K)K)T+++,,.N
!"#$%&'(B*urn:schemasmicrosoftcom:office:smarttagscountryregion9!*urn:schemasmicrosoftcom:office:smarttagsState8$*urn:schemasmicrosoftcom:office:smarttagsCity9%*urn:schemasmicrosoftcom:office:smarttagsplace8)*urn:schemasmicrosoftcom:office:smarttagstime 01114163089HourMinute))))%$%$!)%$%%%$%%)))%$%$%$%$)))%$%$)00eEhEEEEEI IWMXMNNN
( ##$
$^$c$((q////////00?0G011+1,1b1n111R2^222q3}3344496<666m7s777 8%8<
<,<1<k=n===^>a>>>E@H@@@FAHApA{AAA}CC(D/DE!E9EBEEE,F1FGGGGI IIIII/J5JJJZ@CEESE
PLDNiyQLTXrcYI^fU_0aXddDeerediYijrjfms>ssxCx{,{XoJtJ6X.00Y:n!ih0?qmzl'8pYakEVWC8W#Mu4_]acYHsy&1rR!nA(dXK.kbvws[%w{ $f@ji_x ZB{ES
NNNB@!"$%+,KN@@@&@P@@*@X@@.@`@@6@p@@H@UnknownGz Times New Roman5Symbol3&z Arial?5 z Courier New;Wingdings"qh)̕ff(<B(B(!24dmNmN3QH(?O_______________________________________________________________________________Penny VerhoevenPenny Verhoevend
Oh+'0 0 HT
p
P__________________________________________________________________________________Penny VerhoevenennNormalePenny Verhoeven18nMicrosoft Word 10.0@zH6@x@<ja@fB՜.+,D՜.+,Php
0Kennesaw State Universityn(mN:
P_______________________________________________________________________________Title 8@_PID_HLINKSA</9http://unstats.un.org/($http://www.stlouisfed.org/t*http://www.sec.gov/$=http://www.census.gov/}+http://www.bls.gov/t9http://www.bea.gov/3e +http://www.nap.edu/readingroom/books/obas/TKhttp://www.tcnj.edu/~ethcstatlrHhttp://www.amstat.org/profession/index.cfm?fuseaction=ethicalstatistics%http://ksuweb.kennesaw.edu/~pverhoev
!"#$%&'()*+,./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVXYZ[\]^`abcdefghijklmnopqrstuvwxyz{}~Root Entry F!fData
W1Table_nWordDocumentSummaryInformation(DocumentSummaryInformation8CompObjj
FMicrosoft Word Document
MSWordDocWord.Document.89q