tatistics is a required course for undergraduate college students in a number of majors. Students in the
following disciplines are often required to take a course in beginning statistics: allied health careers, biology,
business, computer science, criminal justice, decision science, engineering, education, geography, geology,
information science, nursing, nutrition, medicine, pharmacy, psychology, and public administration. This
outline is intended to assist these students in the understanding of Statistics. The outline may be used as a
supplement to textbooks used in these courses or a text for the course itself.
The author has taught such courses for over 25 years and understands the difficulty students encounter with
statistics. I have included examples from a wide variety of current areas of application in order to motivate an
interest in learning statistics. As we leave the twentieth century and enter the twenty-first century, an
understanding of statistics is essential in understanding new technology, world affairs, and the ever-expanding
volume of knowledge. Statistical concepts are encountered in television and radio broadcasting, as well as in
magazines and newspapers. Modern newspapers, such as USA Today, are full of statistical information. The
sports section is filled with descriptive statistics concerning players and teams performance. The money section
of USA Today contains descriptive statistics concerning stocks and mutual funds. The life section of USA Today
often contains summaries of research studies in medicine. An understanding of statistics is helpful in evaluating
these research summaries.
The nature of the beginning statistics course has changed drastically in the past 30 or so years. This change
is due to the technical advances in computing. Prior to the 1960s statistical computing was usually performed
on mechanical calculators. These were large cumbersome computing devices (compared to today’s hand-held
calculators) that performed arithmetic by moving mechanical parts. Computers and computer software were no
comparison to today’s computers and software. The number of statistical packages available today numbers in
the hundreds. The burden of statistical computing has been reduced to simply entering your data into a data file
and then giving the correct command to perform the statistical method of interest.
One of the most widely used statistical packages in academia as well as industrial settings is the package
called Minitab (Minitab Inc., 3081 Enterprise Drive, State College, PA 16801-3008). I wish to thank Minitab
Inc. for granting me permission to include Minitab output, including graphics, throughout the text. Most
modern Statistics textbooks include computer software as part of the text. I have chosen to include Minitab
because it is widely used and is very friendly. Once a student learns the various data file structures needed to
use Minitab, and the structure of the commands and subcommands, this knowledge is readily transferable to
other statistical software.
The outline contains all the topics, and more, covered in a beginning statistics course. The only
mathematical prerequisite needed for the material found in the outline is arithmetic and some basic algebra. I
wish to thank my wife, Lana, for her understanding during the preparation of the book. I wish to thank my
friend Stanley Wileman for all the computer help he has given me during the preparation of the book. I wish to
thank Dr. Edwin C. Hackleman of Delta Software, Inc. for his timely assistance as compositor of the final
camera-ready manuscript. Finally, I wish to thank the staff at McGraw-Hill for their cooperation and
helpfulness.
LARRY J. STEPHENS
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Contents
Chapter 1 INTRODUCTION ................................................................................. 1
Statistics. Descriptive Statistics. Inferential Statistics: Population and
Sample. Variable, Observation. and Data Set. Quantitative Variable:
Discrete and Continuous Variable. Qualitative Variable. Nominal,
Ordinal, Interval, and Ratio Levels of Measurement. Summation Notation.
Computers and Statistics.
Chapter 2 ORGANIZING DATA .......................................................................... 14
Raw Data. Frequency Distribution for Qualitative Data. Relative
Frequency of a Category. Percentage. Bar Graph. Pie Chart.
Frequency Distribution for Quantitative Data. Class Limits, Class
Boundaries, Class Marks, and Class Width. Single-Valued Classes.
Histograms. Cumulative Frequency Distributions. Cumulative Relative
Frequency Distributions. Ogives. Stem-and-Leaf Displays.
Chapter 3 DESCRIPTIVE MEASURES .............................................................. 40
Measures of Central Tendency. Mean, Median, and Mode for Ungrouped
Data. Measures of Dispersion. Range, Variance, and Standard Deviation
for Ungrouped Data. Measures of Central Tendency and Dispersion for
Grouped Data. Chebyshev’s Theorem. Empirical Rule. Coefficient of
Variation. Z Scores. Measures of Position: Percentiles, Deciles, and
Quartiles. Interquartile Range. Box-and-Whisker Plot.
Chapter 4 PROBABILITY ..................................................................................... 63
Experiment, Outcomes, and Sample Space. Tree Diagrams and the
Counting Rule. Events, Simple Events, and Compound Events. Probability.
Classical, Relative Frequency and Subjective Probability Definitions.
Marginal and Conditional Probabilities. Mutually Exclusive Events.
Dependent and Independent Events. Complementary Events.
Multiplication Rule for the Intersection of Events. Addition Rule for the
Union of Events. Bayes’ Theorem. Permutations and Combinations. Using
Permutations and Combinations to Solve Probability Problems.
Chapter 5 DISCRETE RANDOM VARIABLES ................................................ 89
Random Variable. Discrete Random Variable. Continuous Random
Variable. Probability Distribution. Mean of a Discrete Random Variable.
Standard Deviation of a Discrete Random Variable. Binomial Random
Variable. Binomial Probability Formula. Tables of the Binomial
Distribution. Mean and Standard Deviation of a Binomial Random
Variable. Poisson Random Variable. Poisson Probability Formula.
Hypergeome tric Random Variable. Hypergeometric Probability Formula.
V
vi CONTENTS
Chapter 6 CONTINUOUS RANDOM VARIABLES AND THEIR
PROBABILITY DISTRIBUTIONS .................................................... 113
Uniform Probability Distribution. Mean and Standard Deviation for the
Uniform Probability Distribution. Normal Probability Distribution.
Standard Normal Distribution. Standardizing a Normal Distribution.
Applications of the Normal Distribution. Determining the z and x Values
When an Area under the Normal Curve is Known. Normal Approximation
to the Binomial Distribution. Exponential Probability Distribution.
Probabilities for the Exponential Probability Distribution.
Chapter 7 SAMPLING DISTRIBUTIONS .......................................................... 140
Simple Random Sampling. Using Random Number Tables. Using the
Computer to Obtain a Simple Random Sample. Systematic Random
Sampling. Cluster Sampling. Stratified Sampling. Sampling Distribution
of the Sampling Mean. Sampling Error. Mean and Standard Deviation of the
Sample Mean. Shape of the Sampling Distribution of the Sample Mean
and the Central Limit Theorem. Applications of the Sampling Distribution
of the Sample Mean. Sampling Distribution of the Sample Proportion.
Mean and Standard Deviation of the Sample Proportion. Shape of the
Sampling Distribution of the Sample Proportion and the Central Limit
Theorem. Applications of the Sampling Distribution of the Sample Proportion.
Chapter 8 ESTIMATION AND SAMPLE SIZE DETERMINATION:
ONE POPULATION ........................................................................... 166
Point Estimate. Interval Estimate. Confidence Interval for the Population
Mean: Large Samples. Maximum Error of Estimate for the Population
Mean. The t Distribution. Confidence Interval for the Population Mean:
Small Samples. Confidence Interval for the Population Proportion: Large
Samples. Determining the Sample Size for the Estimation of the
Population Mean. Determining the Sample Size for the Estimation of the
Population Proportion.
Chapter 9 TESTS OF HYPOTHESIS: ONE POPULATION ............................ 185
Null Hypothesis and Alternative Hypothesis. Test Statistic, Critical
Values, Rejection and Nonrejection Regions.Type I and Type I1 Errors.
Hypothesis Tests about a Population Mean: Large Samples. Calculating
Type I1 Errors. P Values. Hypothesis Tests about a Population Mean:
Small Samples. Hypothesis Tests about a Population Proportion: Large
Samples.
المادة المعروضة اعلاه هي مدخل الى المحاضرة المرفوعة بواسطة استاذ(ة) المادة . وقد تبدو لك غير متكاملة . حيث يضع استاذ المادة في بعض الاحيان فقط الجزء الاول من المحاضرة من اجل الاطلاع على ما ستقوم بتحميله لاحقا . في نظام التعليم الالكتروني نوفر هذه الخدمة لكي نبقيك على اطلاع حول محتوى الملف الذي ستقوم بتحميله .
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