# Statistics Introduction

Edition: 1

Pages: 214

## \$26.44

ISBN 9798765724729

Details Electronic Delivery EBOOK 180 days

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. Only the materials that can be covered in a semester and that are vital in introducing the concepts of statistics are included. 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.

Preface
Acknowledgments

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

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 Test for Homogeneity

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

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.
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.
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

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. Only the materials that can be covered in a semester and that are vital in introducing the concepts of statistics are included. 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.

Preface
Acknowledgments

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

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 Test for Homogeneity

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

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.
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.
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