KEAN UNIVERSITY
- Applied Nonparametric Statistical Methods Solutions Manual Download
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Union, New Jersey
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Course Number:Math 4500
Semester Hours:Three(3)
Prerequisite:Math 3526 or equivalent
Applied Nonparametric Statistical Methods Solutions Manual Download
Course Description
The dichotomous data problem, the one-sample and two-samplelocation problems, distribution-free rank test for dispersion and the difference in two populations, the one-way layout, distribution-free tests, multiple comparisons and the two-way layout distribution-free tests. Computers will be utilized.
Prerequisite:Math 3526 or equivalent
I.Objectives
Upon completion of the course the student should be able to:
A.Understand principles of nonparametric statistics
B.Solve applied problems in nonparametric statistics
C.Interpret the results of a nonparametric statistical
analysis
D.Use software systems for solving problems in nonparametric statistics
II. Course Content
A. The Dichotomous Data problem
1. A Binomial test
2. An estimator for the probability of success
3. Confidence interval for the probability of
Applied Nonparametric Statistics Pdf
success
B.The one sample location problem
1. Wilcoxon’s signed rank test
2. Fischer’s sign test
3. Procedures based on signed rank and sign
statistics
4. Distribution-free test of symmetry
C. The two-sample location problem
1.Wilcoxon, Mann and Whitney rank sum test
2.Estimation and confidence interval based on
Wilcoxon’s rank sum test
3.Efficiencies of one sample and two-sample
location procedures
4.Distribution free rank sum test for dispersion with equal medians
5.Kolmogorov-Smirnov test for difference in two populations
6.Efficiencies of two-sample Dispersion procedures
D. The One-Way Layout
1. Kruskal-Wallis test for general alternatives
2. Distribution-free test for Umbrella alternatives
3. Distribution-free test for treatments versus a
Control
4. Multiple comparisons based on pair-wise ranking
5. Efficiencies of one-way Layout procedures
E.The Two-Way Layout
1.Distribution-free test for general alternatives in a randomized complete block
design
2.Distribution-free test for ordered alternatives in a randomized complete block
design
3. Multiple comparisons based on Friedman Rank sums test-general configuration
4. Contrast estimation based on one-sample median estimators
II.Methods of Instruction
A. Lectures and discussions
B. Software demonstrations
C. Discussions of computer output of nonparametric
Statistical analysis
IV.Methods of Evaluation
A. Examinations
B. Assignments
C. Projects
V.Suggested Text
Hollander, Myles and Wolfe, Douglas A., Nonparametric Statistical Methods, 2nd Ed., New
York, NY.: John Wiley & Sons, 1999.
Hollander, Myles and Wolfe, Douglas A., Nonparametric Statistical Methods, Solution manual
2nd Ed., New York, NY.: John Wiley & Sons, 1999.
VI.Bibliography
Brunner, Edgar; Domhof,Sebastian; and Langer,Frank, Nonparametric Analysis of Longitudinal
Data in Factorial Experiments, New York, N.Y.: John Wiley & Sons, 2001.
Chernick, Michael R., Bootstrap Methods: A Practitioner’s Guide, New York, N.Y.: John
Wiley & Sons Inc., 1999.
Cody, R. P. and Smith, J. K., Applied Statistics and the SAS Programming Language, 4th Ed.,
Upper Saddle River, New Jersey: Prentice-Hall, Inc., 1997.
Conover, W. J., Practical Nonparametric Statistics, 3rd Ed., New York, NY.: John Wiley &
Sons Inc., 1998.
Daniel, Wayne W., Applied Nonparametric Statistics, 2nd Ed., Boston, Mass.: PWS-KENT, 1990. Basic technical english jeremy comfort pdf.
Gibbons Jean Dickinson, Nonparametric Statistical Inference, New York, N.Y.: McGraw-Hill
Book Company, 1971.
Jennrich, Robert I., Computational Statistics: An Introduction, Englewood Cliffs, NJ.:
Prentice Hall, 1995.
Kuehl, Robert O., Statistical Principles of Research Design and Analysis, Belmont CA.:
Duxbury Press, 1994.
Mendenhall, W. and Sincich, T., Statistics for Engineering and Computer Sciences, 2nd Ed.,
San Francisco: Dellen and Macmillan publishing Co., 1988.
Montgomery, D. C., Introduction to Statistical Quality Control, 2nd Ed., New York: John
Wiley & Sons Inc., 1991.
Ott, L. R., An Introduction To Statistical Methods and Data Analysis, 5th Ed., Belmont,
California: Duxbury and Wadsworth Publishing Co., 2001.
Ryan, T. P., Statistical Methods For Quality Improvement,New York: John Wiley & Sons Inc.,
1989.
SAS InstituteSAS/QC Software: Reference, Cary, North Carolina: SAS Institute, 1989.
Applied Nonparametric Statistical Methods Solutions Manual Free
Tamhane, Ajit C. and Dunlop, Dorothy D., Statistics and Data Analysis, Upper Saddle River,
NJ: Prentice Hall,2000.
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What Does Nonparametric Method Mean?
Nonparametric method refers to a type of statistic that does not require that the population being analyzed meet certain assumptions, or parameters. Well-known statistical methods such as ANOVA, Pearson's correlation, t test, and others provide valid information about the data being analyzed only if the underlying population meets certain assumptions. One of the most common assumptions is that the population data have a 'normal distribution.'
Parametric statistics may also be applied to populations with other known distribution types, however. Nonparametric statistics do not require that the population data meet the assumptions required for parametric statistics. Nonparametric statistics, therefore, fall into a category of statistics sometimes referred to as distribution-free. Often nonparametric methods will be used when the population data has an unknown distribution, or when the sample size is small.
Nonparametric Method Explained
Parametric and nonparametric methods are often used on different types of data. Parametric statistics generally require interval or ratio data. An example of this type of data is age, income, height, and weight in which the values are continuous and the intervals between values have meaning.
In contrast, nonparametric statistics are typically used on data that nominal or ordinal. Nominal variables are variables for which the values have not quantitative value. Common nominal variables in social science research, for example, include sex, whose possible values are discrete categories, 'male' and 'female.' Other common nominal variables in social science research are race, marital status, educational level, and employment status (employed versus unemployed).
Ordinal variables are those in which the value suggests some order. An example of an ordinal variable would be if a survey respondent asked, 'On a scale of 1 to 5, with 1 being Extremely Dissatisfied and 5 being Extremely Satisfied, how would you rate your experience with the cable company?'
Although nonparametric statistics have the advantage of having to meet few assumptions, they are less powerful than parametric statistics. This means that they may not show a relationship between two variables when in fact one exists.
Common nonparametric tests include Chi Square, Wilcoxon rank-sum test, Kruskal-Wallis test, and Spearman's rank-order correlation.