Applied statistics: from bivariate through multivariate techniques free download

Version 27 Version 25 Version 24 Version 23 Version Version 26 Version 25 Version 24 Version 23 Version Need a previous version? Description Additional information. Why buy from StudentDiscounts. An official IBM partner with over 20 years of experience. Norton Shopping Guarantee.

Check out our independent reviews by real customers on the right side of our web site. Phone support is also available. Worried about buying the right version? No problem! If you buy the wrong one, you can return it with no penalty.

You will receive a credit to apply to purchasing the one you need. Your download comes super fast! Try us and see. Low prices to support the academic community A portion of our sales goes to support UNICEF — your purchase will help provide children around the world with health care, clean water, sound nutrition, education, protection from abuse and exploitation, and emergency assistance in times of crisis.

You may install the software on up to two 2 computers. New in Version 28 New statistical procedures such as metaanalysis to uncover deeper insights Procedure enhancements for Power Analysis and Ratio Statistics Addition of Relationship Maps for data visualization Everyday usability improvements including Statistics Workbook, search, and table side-pane editor SPSS Statistics Extensions give you a new way to access and work with open source and third-party programming extensions: SPSS Statistics Extensions Hub is a new interface to manage extensions.

It provides an online store-like experience. A redesigned experience while importing and exporting the most popular file types enables smarter data management.

Gain deeper predictive insights from large and complex datasets. Use the Temporal Causal Modeling TCM technique to uncover hidden causal relationships among large numbers of time series and automatically determine the best predictors. Integrate, explore and model location and time data, and capitalize on new data sources to solve new business problems The Spatio-Temporal Prediction STP technique can fit linear models for measurements taken over time at locations in 2D and 3D space.

Embed analytics into the enterprise to speed deployment and return on investment. Completely redesigned web reports offer more interactivity, functionality and web server support. Bulk load data for faster performance. A wider range of R programming options enables developers to use a full-featured, integrated R development environment within SPSS Statistics. The procedures within IBM SPSS Statistics Base will enable you to get a quick look at your data, formulate hypotheses for additional testing, and then carry out a number of statistical and analytic procedures to help clarify relationships between variables, create clusters, identify trends and make predictions.

Crosstabulations — Counts, percentages, residuals, marginals, tests of independence, test of linear association, measure of linear association, ordinal data measures, nominal by interval measures, measure of agreement, relative risk estimates for case control and cohort studies. Frequencies — Counts, percentages, valid and cumulative percentages; central tendency, dispersion, distribution and percentile values. Descriptives — Central tendency, dispersion, distribution and Z scores.

Descriptive ratio statistics — Coefficient of dispersion, coefficient of variation, price-related differential and average absolute deviance. Compare means — Choose whether to use harmonic or geometric means; test linearity; compare via independent sample statistics, paired sample statistics or one-sample t test.

ANOVA and ANCOVA — Conduct contrast, range and post hoc tests; analyze fixed-effects and random-effects measures; group descriptive statistics; choose your model based on four types of the sum-of-squares procedure; perform lack-of-fit tests; choose balanced or unbalanced design; and analyze covariance with up to 10 methods.

Correlation — Test for bivariate or partial correlation, or for distances indicating similarity or dissimilarity between measures.

Nonparametric tests — Chi-square, Binomial, Runs, one-sample, two independent samples, k-independent samples, two related samples, k-related samples. Explore — Confidence intervals for means; M-estimators; identification of outliers; plotting of findings. Procedures Included: General linear models GLM — Provides you with more flexibility to describe the relationship between a dependent variable and a set of independent variables. Unsupervised — create bins with equal counts Supervised — take the target variable into account to determine cutpoints.

This method is more accurate than unsupervised; however, it is also more computationally intensive. Hybrid approach — combines the unsupervised and supervised approaches. This method is particularly useful if you have a large number of distinct values.

The intuitive interface guides you every step of the way, and the new Scoring Wizard makes it easy to build models to score your data. After you run an analysis, the significance of the output is clearly explained. RFM Analysis: Score customers according to the recency, frequency and monetary value of their purchases. The program also converts coordinates in table format. Selecting a subset and exporting it as a new file is now also possible.

For the first time, you can now load additional layers into Geoda for visualization purposes. The analysis will still be done on the layer you load first. In this example, the map shows transit access from housing blocks, with the transit station locations as an additional layer. In contrast to programs that visualize raw data in maps, GeoDa focuses on exploring the results of statistical tests and models through linked maps and charts. You can now group the same variable across time periods in the new Time Editor to explore statistical patterns across space and time.

Then explore results as views change over time with the Time Player. If your spatial data are projected. For instance, first select if you want to compare means of selected vs. A scatter plot matrix allows you to explore multiple bivariate correlations at once.

In this example, the regression slopes for selected, unselected and all police precincts in San Francisco are shown to explore relationships between four types of crime. GeoDa has long supported uni-and bivariate local tests of spatial autocorrelation like local Moran. GeoDa now has lots of new techniques to identify clusters with spatial constraints, including skater, redcap, max-p, k-means, k-medians, k-medoids, and spectral clustering.

Download Project Details. I have not completed the course yet finished till EDA and these are my thought about the course. This course is quite beginner-friendly , easy to follow a This course is quite beginner-friendly , easy to follow and the material also a good helps in easily understand. I took this course and started learning as i am beginner for the Data Science field. And after completed just few lessons and modules of this course i felt t And after completed just few lessons and modules of this course i felt that it is designed very beautifully for beginners.

Every concepts described from basic level and easily understood. Recommending all to definitely go with this course. You will Learn a lot and will get the essential knowledge. This course is meant for people looking to learn Machine Learning.

We will start out to understand the pre-requisites, the underlying intuition behind several machine learning models and then go on to solve case studies using Machine Learning concepts.

This is a self paced course, which you can take any time at your convenience over the 6 months after your purchase. If you can put between 8 to 10 hours a week, you should be able to finish the course in 6 to 8 weeks. That is, do probability and inference topics for a SRS, then do probability and inference for a stratified sample and each time taking your probability and inference ideas further so that they are constantly being built upon, from day one!

Navigation as a PDF document is simple since all chapters and subsection within the table of contents are hyperlinked to the respective section. Graphs and tables are clean and clearly referenced, although they are not hyperlinked in the sections. The only visual issues occurs in some graphs, such as on page , which have maps of the U. The text would not be found to be culturally insensitive in any way, as a large part of the investigations and questions are introspective of cultures and opinions.

For example, income variations in two cities, ethnic distribution across the country, or synthesis of data from Africa. The book has a great logical order, with concise thoughts and sections. While section are concise they are not limited in rigor or depth as exemplified by a great section on the "power" of a hypothesis test and numerous case studies to introduce topics.

The reading of the book will challenge students but at the same time not leave them behind. Overall I like it a lot. The best statistics OER I have seen yet. More depth in graphs: histograms especially. The most accurate open-source textbook in statistics I have found. Though I might define p-values and interpret confidence intervals slightly differently. I did not see much explanation on what it means to fail to reject Ho.

I would consider this "omission" as almost inaccurate. Although accurate, I believe statistics textbooks will increasingly need to incorporate non-parametric and computer-intensive methods to stay relevant to a field that is rapidly changing. Also, as fewer people do manual computations, interpretation of computer software output becomes increasingly important.

Quite clear. The text, though dense, is easy to read. More color, diagrams, photos? Great job overall. However, the introduction to hypothesis testing is a bit awkward this is not unusual. Create a clear way to explain this multi-faceted topic and the world will beat a path to your door.

No problems, but again, the text is a bit dense. More color, diagrams, etc.? Overall it was not offensive to me, but I am a college-educated white guy. Examples of how statistics can address gender bias were appreciated. Overall, this is the best open-source statistics text I have reviewed. Most contain glaring conceptual and pedagogical errors, and are painful to read don't get me started on percentiles or confidence intervals.

Also, a reminder for reviewers to save their work as they complete this review would be helpful. The coverage of this text conforms to a solid standard very classical semester long introductory statistics course that begins with descriptive statistics, basic probability, and moves through the topics in frequentist inference including basic Comprehensiveness rating: 3 see less. The coverage of this text conforms to a solid standard very classical semester long introductory statistics course that begins with descriptive statistics, basic probability, and moves through the topics in frequentist inference including basic hypothesis tests of means, categories, linear and multiple regression.

The regression treatment of categorical predictors is limited to dummy coding though not identified as such with two levels in keeping with the introductory nature of the text.

There is a bit of coverage on logistic regression appropriate for categorical specifically, dichotomous outcome variables that usually is not part of a basic introduction. Within each appears an adequate discussion of underlying assumptions and a representative array of applications.

Some of the more advanced topics are treated as 'special topics' within the sections e. Some more modern concepts, such as various effect size measures, are not covered well or at all for example, eta squared in ANOVA. However, classical measures of effect such as confidence intervals and R squared appear when appropriate though they are not explicitly identified as measures of effect. Technical accuracy is a strength for this text especially with respect to underlying theory and impacts of assumptions.

The basics of classical inferential statistics changes little over time and this text covers that ground exceptionally well. More modern approaches to statistical methods, however, will need to include concepts of important to the current replicability crisis in research: measures of effect, extensive applications of power analyses, and Bayesian alternatives.

The task of reworking statistical training in response to this crisis will be daunting for any text author not just this one. One of the strengths of this text is the use of motivated examples underlying each major technique.

These examples and techniques are very carefully described with quality graphical and visual aids to support learning. This defect is not present here: this text embraces an 'embodied' view of learning which prioritizes example applications first and then explanation of technique.

The consistency of this text is quite good. Notation, language, and approach are maintained throughout the chapters. It is difficult for a topic that in inherently cumulative to excel at modularity in the manner that is usually understanding. Each topic builds on the one before it in any statistical methods course. This text does indicate that some topics can be omitted by identifying them as 'special topics'.

The structure and organization of this text corresponds to a very classic treatment of the topic. It begins with the basics of descriptive statistics, probability, hypothesis test concepts, tests of numerical variables, categorical, and ends with regression. I have seen other texts begin with correlation and regression prior to tests of means, etc. This is the third edition and benefits from feedback from prior versions.

I found no negative issues with regard to interface elements. It is a pdf download rather than strictly online so the format is more classical textbook as would be experienced in a print version.

It is clear that the largest audience is assumed to be from the United States as most examples draw from regions in the U. The language seems to be free of bias. This text is an excellent choice for an introductory statistics course that has a broad group of students from multiple disciplines. The basic theory is well covered and motivated by diverse examples from different fields. This diversity in discipline comes at the cost of specificity of techniques that appear in some fields such as the importance of measures of effect in psychology.

This book covers topics in a traditional curriculum of an introductory statistics course: probabilities, distributions, sampling distribution, hypothesis tests for means and proportions, linear regression, multiple regression and logistic While the traditional curriculum does not cover multiple regression and logistic regression in an introductory statistics course, this book offers the information in these two areas.

The book started with several examples and case study to introduce types of variables, sampling designs and experimental designs chapter 1. It would be nice if the authors can start with the big picture of how people perform statistical analysis for a data set.

Chapter 2 covers the knowledge of probabilities including the definition of probability, Law of Large Numbers, probability rules, conditional probability and independence and linear combinations of random variables. However, the linear combination of random variables is too much math focused and may not be good for students at the introductory level. Chapter 3 covers random variables and distributions including normal, geometry and binomial distributions.

Chapter cover the inferences for means and proportions and the Chi-square test. Chapter 7 and 8 cover the linear , multiple and logistic regression. The book used plenty of examples and included a lot of tips to understand basic concepts such as probabilities, p-values and significant levels etc.

The book provides an effective index. The drawback of this book is that it does not cover how to use any computer software or even a graphing calculator to perform the calculations for inferences.

All of the calculations covered in this book were performed by hand using the formulas. As the trend of analysis, students will be confronted with the needs to use computer software or a graphing calculator to perform the analyses.

Calculations by hand are not realistic. However, when introducing the basic concepts of null and alternative hypotheses and the p-value, the book used different definitions than other textbooks. Students can easily get confused and think the p-value is in favor of the alternative hypothesis. The content is up-to-date. Especially, this book covers Bayesian probabilities, false negative and false positive calculations.

The text also provides enough context for students to understand the terminologies and definitions, especially this textbook provides plenty of tips for each concept and that is very helpful for students to understand the materials. The organization for each chapter is also consistent.

Each chapter contains short sections and each section contains small subsections. The text is easily reorganized and re-sequenced. The later chapters chapter are self-contained and can be re-ordered.

The later chapters chapters are built upon the knowledge from the former chapters chapters The later chapters on inferences and regression chapters are built upon the former chapters chapters But there are instances where similar topics are not arranged very well: 1 when introducing the sampling distribution in chapter 4, the authors should introduce both the sampling distribution of mean and the sampling distribution of proportion in the same chapter.

The authors spend many pages on the sampling distribution of mean in chapter 4, but only a few sentences on the sampling distribution of proportion in chapter 6; 2 the authors introduced independence after talking about the conditional probability. The order of introducing independence and conditional probability should be switched. The approach of introducing the inferences of proportions and the Chi-square test in the same chapter is novel.

The students can easily see the connections between the two types of tests. The graphs and tables in the text are well designed and accurate.

These graphs and tables help the readers to understand the materials well, especially most of the graphs are colored figures. Some examples are related to United States. Most of the examples are general and not culturally related. The text offered quite a lot of examples in the medical research field and that is probably related to the background of the authors.

The text provides enough examples, exercises and tips for the readers to understand the materials. It also offered enough graphs and tables to facilatate the reading. This text provides decent coverage of probability, inference, descriptive statistics, bivariate statistics, as well as introductory coverage of the bivariate and multiple linear regression model and logistics regression.

Although there are some Although there are some materials on experimental and observational data, this is, first and foremost, a book on mathematical and applied statistics. Professors looking for in-depth coverage of research methods and data collection techniques will have to look elsewhere. The coverage of probability and statistics is, for the most part, sound. Most essential materials for an introductory probability and statistics course are covered.

The authors do a terrific job in chapter 1 introducing key ideas about data collection, sampling, and rudimentary data analysis. Chapters on statistical inference are especially strong, and the discussion of outliers and leverage in the regression chapters should prove useful to students who work with small n data sets. Teachers might quibble with a particular omission here or there e. In other cases I found the omissions curious. As well, the authors define probability but this is not connected as directly as it could be to the 3 fundamental axioms that comprise the mathematical definition of probability.

The authors limit their discussion on categorical data analysis to the chi square statistic, which centers on inference rather than on the substantive magnitude of the bivariate relationship.

I wish they included measures of association for categorical data analysis that are used in sociology and political science, such as gamma, tau b and tau c, and Somers d. Finally, I think the book needs to add material on the desirable properties of statistical estimators i. Appendix A contains solutions to the end of chapter exercises. The index is decent, but there is no glossary of terms or summary of formula, which is disappointing.

There are some things that should probably be included in subsequent revisions. All of the chapters contain a number of useful tips on best practices and common misunderstandings in statistical analysis.

There are also a number of exercises embedded in the text immediately after key ideas and concepts are presented. I suspect these will prove quite helpful to students. Overall, the book is heavy on using ordinary language and common sense illustrations to get across the main ideas.

They draw examples from sources e. There are no proofs that might appeal to the more mathematically inclined. There are lots of great exercises at the end of each chapter that professors can use to reinforce the concepts and calculations appearing in the chapter.

I also appreciated that the authors use examples from the hard sciences, life sciences, and social sciences. This will increase the appeal of the text. A teacher can sample the germane chapters and incorporate them without difficulty in any research methods class. Things flow together so well that the book can be used as is. The book presents all the topics in an appropriate sequence.

The color graphics come through clearly and the embedded links work as they should. It might be asking too much to use it as a standalone text, but it could work very well as a supplement to a more detailed treatment or in conjunction with some really good slides on the various topics. The text has a thorough introduction to data exploration, probability, statistical distributions, and the foundations of inference, but less complete discussions of specific methods, including one- and two-sample inference, contingency tables, It includes too much theory for our undergraduate service courses, but not enough practical details for our graduate-level service courses.

For example, it is claimed that the Poisson distribution is suitable only for rare events p. For example, there is a strong emphasis on assessing the normality assumption, even though most of the covered methods work well for non-normal data with reasonable sample sizes. Normal approximations are presented as the tool of choice for working with binomial data, even though exact methods are efficiently implemented in modern computer packages.

The section on model selection, covering just backward elimination and forward selection, seems especially old-fashioned. Some topics seem to be introduced repeatedly, e. The authors are sloppy in their use of hat notation when discussing regression models, expressing the fitted value as a function of the parameters, instead of the estimated parameters pp. For example, I can imagine using pieces of Chapters 2 Probability and 3 Distributions of random variables to motivate methods that I discuss in service courses.

One-way analysis of variance is introduced as a special topic, with no mention that it is a generalization of the equal-variances t-test to more than two groups. The final chapter 8 gives superficial treatments of two huge topics, multiple linear regression and logistic regression, with insufficient detail to guide serious users of these methods. It is as if the authors ran out of gas after the first seven chapters and decided to use the final chapter as a catchall for some important, uncovered topics.

The availability of data sets and functions at a website www. OpenIntro Statistics covers a first course in statistics, providing a rigorous introduction to applied statistics that is clear, concise, and accessible. We hope readers will take away three ideas from this book in addition to forming a foundation of statistical thinking and methods. But, when you understand the strengths and weaknesses of these tools, you can use them to learn about the world.

David M. Diez is a Quantitative Analyst at Google where he works with massive data sets and performs statistical analyses in areas such as user behavior and forecasting. Christopher D. Content Accuracy rating: 5 I see essentially no errors in this book. Clarity rating: 4 The writing in this book is above average. Consistency rating: 5 The book reads cleanly throughout.

Modularity rating: 5 This book is highly modular. Interface rating: 3 I found no problems with the book itself. Grammatical Errors rating: 5 I did not find any grammatical errors that impeded meaning.