Conditions of Use
This book covers most of the points that should be introduced in the entry-level statistical course. The author has brought out descriptive analytics, I think it will be better if predictive and prescriptive analytics can be introduced in the... read more
This book covers most of the points that should be introduced in the entry-level statistical course. The author has brought out descriptive analytics, I think it will be better if predictive and prescriptive analytics can be introduced in the book. Also, it can be a bonus if big data is talked about in this book.
Some of the contents need to add more information and explanations.
Some of the additional contents can be added in each chapter, such as in the chapter of sampling and data structures, there should be mentioned the four different data, interval, ratio, nominal, and ordinal, which help the readers to understand how to take care different data structures, Therefore, some contents need to be considered.
The book is mainly based on the application of R programming, I think it will be better to give a better explanation before it starts to introduce the usage of applying R programming.
This book holds the consistency.
There are many examples that are applying the R programming, the logic is very important, It will be better if an additional vocabulary of the R language in each chapter, and the reader will be easier to know the coding that is referring to the different context that is associated with R programming.
The organization of the book is a little bit confused, I think the order of sections should be arranged in this book. For example, the author places descriptive statistics as the third chapter, I think it can be put in the later chapter because the different data type has not been introduced before the reader knows how to do the descriptive statistics with the different type of variable.
The book has provided the navigation for each chapter, figure, and table. I think it will be better if the terminologies can be provided at the end of the textbook, and it will be additional navigation for the reader who wants to direct to the pages with the relevant contents.
There are some grammar issues that need to be fixed in the book.
IT is not culturally insensitive or offensive at all.
I think the most important thing that I would like to recommend is to add some content and reorganize the chapters.
Table of Contents
- I Introduction to Statistics
- 1 Introduction
- 2 Sampling and Data Structures
- 3 Descriptive Statistics
- 4 Probability
- 5 Random Variables
- 6 The Normal Random Variable
- 7 The Sampling Distribution
- 8 Overview and Integration
- II Statistical Inference
- 9 Introduction to Statistical Inference
- 10 Point Estimation
- 11 Confidence Intervals
- 12 Testing Hypothesis
- 13 Comparing Two Samples
- 14 Linear Regression
- 15 A Bernoulli Response
- 16 Case Studies
About the Book
The target audience for this book is college students who are required to learn statistics, students with little background in mathematics and often no motivation to learn more. It is assumed that the students do have basic skills in using computers and have access to one. Moreover, it is assumed that the students are willing to actively follow the discussion in the text, to practice, and more importantly, to think.
Teaching statistics is a challenge. Teaching it to students who are required to learn the subject as part of their curriculum, is an art mastered by few. In the past I have tried to master this art and failed. In desperation, I wrote this book.
This book uses the basic structure of generic introduction to statistics course. However, in some ways I have chosen to diverge from the traditional approach. One divergence is the introduction of R as part of the learning process. Many have used statistical packages or spreadsheets as tools for teaching statistics. Others have used R in advanced courses. I am not aware of attempts to use R in introductory level courses. Indeed, mastering R requires much investment of time and energy that may be distracting and counterproductive for learning more fundamental issues. Yet, I believe that if one restricts the application of R to a limited number of commands, the benefits that R provides outweigh the difficulties that R engenders.
Another departure from the standard approach is the treatment of probability as part of the course. In this book I do not attempt to teach probability as a subject matter, but only specific elements of it which I feel are essential for understanding statistics. Hence, Kolmogorov’s Axioms are out as well as attempts to prove basic theorems and a Balls and Urns type of discussion. On the other hand, emphasis is given to the notion of a random variable and, in that context, the sample space.
About the Contributors
Benjamin Yakir, The Hebrew University of Jerusalem