This textbook is meant to introduce statistics to the general audience. It is also meant for the first college course in statistics irrespective of the student’s area of study. The audience is assumed to have no higher mathematics background than college algebra. The authors avoided broad explanations using varieties of examples to keep the length of the textbook short. The instructors may bring in test problems and/or homework assignments from other sources within the scope of study.
Only the materials that can be covered in a semester and that are vital in introducing the concepts of statistics are included. Materials such as Quality Control and Non-parametric Statistics are left out for the subsequent course(s) whenever applicable.
Due to computational advantage, the methods for grouped data have become less important and are not discussed extensively. Partial questions that have little value in the real world are mostly avoided. Complete questions are given to emphasize the concepts. More emphasis is given for the word problems.
The emphasis of this textbook is on concepts of statistics, and hence repetitive numerical and graphical descriptive methods are avoided. When the audience has the knowledge of key vocabularies in statistics and familiarity with the statistical concepts, they will be able to implement other methods without much difficulty.
The solutions of selected exercise problems are given in the Appendix. Students are advised to try the problems themselves before checking out the solutions.
CHAPTER 1 Introduction
1.1 Introduction
1.2 Population Parameters and Sample Statistics
1.3 Historical Background
1.4 Types of Data
1.5 Need of a Sample
1.6 Data Collection Methods
1.7 Sampling Methods
1.8 Technology in Statistical Analysis
1.9 R Programming
Exercises
CHAPTER 2 Descriptive Statistics
2.1 Organizing and Displaying Data
2.2 Frequency Table
2.3 Graphical Displays
2.4 Numerical Measures
2.5 Measures of Central Tendency
2.6 Measures of Variation
2.7 Measures of Position
Exercises
CHAPTER 3 Inferential Statistics
3.1 Definitions
3.2 Basic Approaches to Computing Probability
3.3 Relationships among Events
3.4 Conditional Probability
3.5 Counting
Exercises
CHAPTER 4 Discrete Probability Distributions
4.1 Definitions
4.2 Discrete Probability Distributions
4.3 Other Situations
4.4 Mean or Expected Value of a Discrete Random Variable
4.5 Variance and Standard Deviation of a Discrete Random Variable
Exercises
CHAPTER 5 Continuous Probability Distributions
5.1 Definitions
5.2 The Normal Distribution
5.3 Sampling Distributions
5.4 The Central Limit Theorem
5.5 Normal Approximation to the Binomial Distribution
Exercises
CHAPTER 6 Inferences about Population Parameters
6.1 Inferences about Population Proportion p
6.2 Inferences about Population Mean μ
6.3 Inferences about Population Variance σ2
6.4 Testing Statistical Hypotheses Regarding Population Mean μ
6.5 Testing Statistical Hypotheses Regarding Population Proportion p
6.6 Testing Statistical Hypotheses Regarding Population Variance σ2
Exercises
CHAPTER 7 Comparing Two Population Parameters
7.1 Comparing Two Population Proportions
7.2 Comparing Two Independent Population Means
7.3 Comparing Two Dependent Or Matched Population Means
7.4 Comparing Two Independent Population Variances
Exercises
CHAPTER 8 Chi-square Tests and Analysis of Variance
8.1 Chi-square Goodness-of-fit Test
8.2 Test for Homogeneity
8.3 Chi-square Test for Independence
8.4 Analysis of Variance
Exercises
CHAPTER 9 Association between Two Variables
9.1 Correlation Coefficient
9.2 Simple Linear Regression
Exercises
APPENDIX I
I.1 Solutions for Selected Exercises from Chapter 1
I.2 Solutions for Selected Exercises from Chapter 2
I.3 Solutions for Selected Exercises from Chapter 3
I.4 Solutions for Selected Exercises from Chapter 4
I.5 Solutions for Selected Exercises from Chapter 5
I.6 Solutions for Selected Exercises from Chapter 6
I.7 Solutions for Selected Exercises from Chapter 7
I.8 Solutions for Selected Exercises from Chapter 8
I.9 Solutions for Selected Exercises from Chapter 9
APPENDIX II
Table 1: Random Number Generating Table
Table 2: Standard Normal Cumulative Probability P(Z ≤ z)
Table 3: t-Distribution Percentiles
Table 4: Chi-square Distribution Percentiles
Table 5: F-Distribution Percentiles
Index
Mezbahur
Rahman
Professor Mezbahur Rahman has been teaching at Minnesota State University for the last twelve years. He has total nineteen years of teaching experience in statistics courses, starting from elementary statistics to graduate level theory and application courses. Professor Rahman earned his doctorate degree from the University of California, Riverside in Applied Statistics. His research areas are: Parametric and Nonparametric Inferential Statistics in the areas of Categorical Data Analysis, Data Transformations, Parameter Estimation, Kernel Density Estimation, and Goodness-of-fit tests. He has fifty plus research publications in national and international peer reviewed journals. He has given several presentations at national and international conferences.
Deepak
Sanjel
Dr. Deepak Sanjel has been working as a statistical consultant since 2001 and tenured Associate Professor in Statistics at Minnesota State University. He teaches a range of Mathematics and Statistics course to both graduate and undergraduate level students at MSU. In addition to his teaching and research, he actively serves as statistical consultant at various industries. Before coming to MSU, he earned a PhD degree in Statistics from the University of Western Ontario, Canada and worked as a postdoctoral research fellow at McMaster University, Hamilton, Canada
Han
Wu
Han Wu is an assistant professor of statistics at Minnesota State University. He has six years of full time college teaching experience. He teaches both graduate level and undergraduate level statistics courses. His research interests are in mathematical statistics and general methods. Before coming to Minnesota State University, He taught at Husson University and Austin Peay State University. He earned a PhD in Statistics from Iowa State University.