Introductory Statistics Pdf

[Leola]

Iam hoping you can give me some suggestions. I am teaching in a very diverse (made of minority groups) college and the students are mostly Psychology majors. Most students are fresh from high school but some of them are older returning students above 40. Most of the students have motivational problems and aversion to math. But I am still looking for a book that covers the basic curriculum: from descriptive to sampling and testing all the way to ANOVA, and all in the context of experimental methods. The department requires me to use SPSS in class, but I like the idea of building the analysis in a spreadsheet such as excel.

p.s. the other teachers use a book that I don't like because of the extensive reliance on computational formulae. I find using these computational formulas - rather than the more intuitive and computationally intensive formula that is consistent with the rational and basic algorithm- unintuitive, unnecessary and confusing. This is the book I refer to Essentials of Statistics for the Behavioral Sciences, 7th Edition Frederick J Gravetter State University of New York, Brockport Larry B. Wallnau State University of New York, Brockport ISBN-10: 049581220XThank you for reading!

Statistics, by Freedman, Pisani, & Purves, originated from a popular and successful course taught at U.C. Berkeley. I have used it as an intro stats text for undergraduates, have borrowed some of its ideas when teaching graduate stats courses, and have given away many copies to colleagues and clients. There are many reasons for its popularity:

Its narrative and its problems are driven by real case studies and actual data of obvious importance, rather than the made-up drivel found in so many texts. These are truly interesting and memorable, including the Salk polio vaccine trials, the 1936 Literary Digest poll debacle, the Berkeley graduate student discrimination lawsuit (hinging on Simpson's Paradox), Fisher's criticism of Mendel's pea results, and much more.

It has extensive problems at three levels: at the end of each chapter subsection (of which there are hundreds), at the end of each chapter (over 30), and at the ends of major groups of chapters (about 4, I recall). These problems require minimal or no mathematics: they focus on potential misunderstandings that the authors, in their extensive experience, have found to arise among students.

It uses (almost) no mathematical formulas. Quantitative relationships are usually expressed graphically and in words. (They are so clearly conveyed that when I first read this book, as a math graduate student entirely ignorant of statistics, I was able to reproduce all the underlying mathematical theory with no trouble.)

It covers most of the traditional material, including the Binomial and Normal distributions, confidence intervals, z tests, t tests, chi squared tests, regression, and the minimum amount of probability and combinatorics needed to understand these.

I believe the latter two are not critical: a good instructor can easily supply the ANOVA material and can teach as much or little computing as they might wish. Whether the omission of Bayesian statistics is important will depend on the instructor's tastes and aims.

Finally, I should note that although the mathematical demands are as small as one could possibly imagine, my pre- and post-testing of students indicates that people who come to the book with a disposition and habit of thinking quantitatively still get much more out of it than those who do not. Most of my students performed badly on pretests of mathematical knowledge (90% got failing grades), but those who also performed badly on pretests of critical thinking (Shane Frederick's Cognitive Reflection Test) exhibited markedly less improvement during the semester than others did. The pre and post tests both included the full 40-item CAOS test of fundamental concepts any introductory college-level stats course ought to include. The students in this class have consistently exhibited twice as much improvement as that reported in the CAOS literature; the students with poor cognitive reflection scores improved only an average amount (or failed to complete the course). I haven't the data to assign causes to this extra improvement, but suspect the textbook deserves at least some of the credit.

I eventually found the book that came close to what I was looking for: Learning Statistics with R: A tutorial for psychology students and other beginners by Danielle Navarro. It is freely available online (legally) and you can also order a print version for about US $30 (see the book page for details).

R implementations embedded in text as topics are introduced. R has built-in functions for most of the methodsexplained in the book. Where R doesn't have a built-in, the authorhas written her own function for it and made it available on CRANunder her lsr library, so your learning is quite complete. Ipersonally found this to be the biggest plus point of this book.

The book is more comprehensive than Freedman and OpenIntro. Alongwith the basics, it covers topics like Shapiro-Wilk test, Wilcoxon test,Spearman correlation, trimmed means and a chapter on Bayesian statistics, to name a few.

I am suspicious of the books that are in their 7th edition. In my teaching experience, it means that the sections and problems were reshuffled so that the students would have to buy the latest edition to generate the cash flow for the publisher and royalties for the authors keep up with the course. Few serious, research level monographs have undergone a second edition by their authors, and any higher number is obviously an outlier. (Kendall's Library of Statistics is a notable exception, but I cannot really think of any other book that I know that would be in its third edition.)

In my very strong opinion, Excel is a good tool for statistical analysis only when used by a Ph.D. statistician. Teaching undergraduate statistics with it will likely have disastrous consequences, and teaches little statistics as compared to using a modern package like R or Stata. Just try to produce a standardized residual vs. leverage regression plot in Excel, and compare it to one-liners in these packages. Stat majors would need to know the theory, so they would need to build these plots from scratch, but still using a statistical package rather than copy/paste the formulae around in Excel. Non-major undergrads need to get the feel for data analysis, and Excel obscures it, at best.

Statistics Unplugged is a great book for introductory statistics. The author first introduces the logic of the statistical test and later gives the mathematical formula. This approach helps in digesting the new concepts. There are several examples throughout the book which are presented in the form of a problem required to be solved rather than a hypothetical statement and mathematical steps.

I would suggest that an intro stats course for psychology and other social science types should emphasize how not to go wrong too much. A survey of methods would also be a good thing for undergrads to get.

I have been a TA, observer, or student in a lot of courses involving quantitative methods for psychology, with SPSS as the main program. In all cases it has seemed to me that students have gravitated towards Field (2013), irrespective of whether the course coordinator has mentioned this book or not. In numerous cases students have ignored a recommended textbook and read Field's textbook instead.

I'm not properly competent to assess the rigour of the explanations in the book, and nor am I aware of any research on learning outcomes. However, I can say that this book is comprehensive, cheap (where I'm from anyway), and popular with students. The author's writing style relies a great deal on personal anecdotes, which will grate with some readers. However, I've found that at least as many students enjoy it. I seemed to run into a lot of typos and other issues in the early editions, but by the fourth edition most of these seem to be weeded out.

Statistics for College Students and Researchers: Grasping the Concepts, Paperback 2010, ISBN-13 : 978-1453604533 is perhaps the easiest and most comprehensive stat book for college students. "grasping the concepts and the logic of Statistics. Formulas do not lead to understanding, they actually prevent it. This book takes you through elementary, intermediate and advanced Statistics with only five simple formulas! Descriptive and inferential Statistics. t-test, one-way analysis of variance (ANOVA), two-way ANOVA, repeated measures, factorial designs unlimited, complex split-plot designs.". Unbelievable? Yes... I got the book, the description at amazon is correct. The author is a psychologist/medical anatomist, so examples come from these areas. Apparently these are his lectures. Concepts are developed through funny stories. Yes, five simple formulas all the way through ANOVA. ANOVA summary tables become redundant, although all example cases use them.

The Dana Center Mathematics Pathways (DCMP) Introductory Statistics: Analyzing Data with Purpose (ISAP) course is a college-level introductory statistics course organized around broad statistical concepts and intended to serve students pursuing careers in business, allied health, nursing, and the social and behavioral sciences.

Statistics differs from mathematics in several ways. Statistics is inherently a data-based discipline that requires students to recognize variability in data and to take it into account to make decisions in a way that acknowledges and quantifies uncertainty. This introductory statistics course is grounded in data, with engaging contexts bringing meaning to the work. Students are asked to learn from data and communicate with data, with a focus on the investigative process that leads to data-based conclusions.

In this course, statistical thinking is viewed as an investigative process that leads to data-based conclusions. This type of statistical thinking promotes student success in future courses, helps students gain skills for the workplace, and prepares them to become well-informed, engaged citizens.

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