About the book
The book covers the topics usually included in undergraduate Statistics courses: graphical and numerical description of data, probability, discrete and continuous variables and distributions, sampling and sampling distribution, estimation, hypothesis testing, simple regression, ANOVA, and maximum likelihood method. It is the philosophy that is unusual: we try to prove most facts, without sacrificing intuition. As a result, the reader can see the true motivation behind statistical constructions and the logical links between them.
The electronic (pdf) copy of the book is extremely easy to use on
electronic devices because it contains almost 400 hyperlinks. You can start reading the book from the middle. Whenever knowledge of the previous material is required, you will see a link which will take you to the relevant place in the text. After reading it, just press the back button in your pdf reader and continue reading where you took off.
- The main statistical phenomena and variables are modeled and illustrated in Excel. This allows the student to see how data is generated in real time. We think the conventional practice to provide ready datasets partially hides the random nature of data and the difficulty of data collection.
- The definition of the correlation coefficient is explained using the Euclidean geometry. Specifically, the correlation coefficient is the cosine of the angle between certain vectors. Its interpretation as a cosine makes obvious most of its properties.
- The normal distribution is defined in two steps. First, we define a standard normal variable. Then, a (general) normal variable is defined as a linear transformation of the standard normal. This way is easier to understand and apply than the traditional way of defining the normal variable using its density. Under the traditional approach, the students do not remember the Gaussian density and cannot extract the mean and variance of the normal distribution from its density.
- Analysis of Variance (ANOVA) is a complex and outdated topic. It is not worth studying a bunch of definitions and formulas just to be able to interpret one table. We think ANOVA should not be given at all or should be given with most proofs. Giving it with proofs reveals that ANOVA is just an application of regression analysis and allows the students to learn manipulations with summation signs (including double summations).
- The whole book is written with the view that the reader is preparing to take Econometrics. In particular, the book contains a chapter called "Bridge to econometrics".
- Usually Statistics texts are provided with statistical tables. In addition to taking dozens of pages, the tables are inconvenient to use. In a separate chapter we describe Excel and Mathematica statistical functions that allow one to do without statistical tables.
Where to buy
Hint. The paperback copy is available also on Amazon.com, BarnesandNoble.com, Ingram and probably in other places. The price on Lulu.com, to which the above button takes you, is the lowest. Besides, if you register on Lulu.com, you can avail yourself of further discounts.
Hint. The pdf copy is available only on Lulu.com. If you want to save $5.99, you can download the previous pdf version (called Companion for “Statistics for Business and Economics” by Paul Newbold, William L. Carlson and Betty Thorne) for free from RePEc.org. Since 2010, when it was uploaded to RePEc, it has been downloaded more than 500 times. The main differences between the versions are: 1) the previous 12 chapters have been compressed to 10, 2) four new chapters have been added, and 3) there are no hyperlinks in the previous version.
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At last, an essential textbook for the fans of statistics and for the beginning econometricians has been published. I wish I had such book a long time ago when I was an undergraduate student. In fact, this should be available in every library. This book is way deeper in providing a solid background and much clearer than any other statistics textbooks I have ever read in my life. Normally, many authors publish mathematical statistics from theoretical perspectives skipping the application of it in social sciences. Another line of authors do publish only Business Statistics with full of examples, but with no motivating theoretical questions in mind where these tools come from. In contrary to them, Professor Mynbayev's recent book does both. He had put a lot of efforts to help students understand the intuition of every statistical concept and its relation to econometrics and other social sciences. Hence, I strongly recommend this book to anyone beginning to learn statistics and econometrics.