By Richard G. Lomax
This booklet offers accomplished assurance in order that it may be utilized in a unmarried- or two-course series in information. It presents higher flexibility since it includes many themes now not handled in different introductory texts. Its conceptual, intuitive process allows ideas to be simply acknowledged and relating to real-life examples. in the course of the textual content the writer demonstrates what percentage statistical recommendations might be relating to each other. not like different texts, this booklet contains the subsequent issues: * skewness and kurtosis measures; * inferences approximately established proportions and self sustaining skill with unequal variances; * homogeneity of variance checks; * structure of the information in ANOVA versions; * the ANOVA linear version; * a wide selection of a number of comparability tactics; * value assessments in a number of linear regression; and * large dialogue of assumptions and the way to accommodate assumption violations. a number of tables and figures aid illustrate innovations and current examples in the textual content. an in depth bibliography is integrated. a couple of pedagogical units are incorporated to extend the reader's conceptual realizing of records: bankruptcy outlines; record of key techniques for every bankruptcy; bankruptcy pursuits; a variety of practical examples; precis tables of statistical assumptions; large references; and finish of bankruptcy conceptual and computational difficulties. An instructor's handbook is obtainable containing solutions to all the difficulties, in addition to a set of statistical humor designed to be an academic relief. This publication is meant for introductory information classes for college kids in schooling and behavioral sciences.
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Additional resources for An Introduction to Statistical Concepts for Education and Behavioral Sciences
If we also asked what products they liked, we could then determine whether men like certain products more than women, or whether certain products are more likely to be liked by younger people. Surveys are popular and relatively easy to carry out. However, we have to remember that the results and stories based on them can vary considerably based on how questions are asked and how they vary (or not) over time. For example, the US Census has worked for literally decades on questions about the race of US citizens.
At Expedia again, for example, one Eureka story involved eliminating change/cancel fees from online hotel, cruise, and car rental reservations. Until 2009, Expedia and its competitors all charged up to $30 for a change or cancellation—above and beyond the penalties the hotel imposed. Expedia and other online bookers’ rates were typically much lower than booking directly with a hotel, and customers were willing to tolerate change/cancel fees. However, by 2009 it had become apparent that the fees had become a liability.
It’s a traditional environment for analytics, so let’s call it small data. It used to be the only option for analytics. Today, however, big companies, nonprofit organizations, and small start-ups are excited about big data—unstructured data in large volumes. It might come from online discussions on the Internet, footage from video cameras, or DNA analysis of a group of patients in a hospital. These types of data tend to be much larger—sometimes in the multipetabyte range. For example, Google processes about 24 petabytes of Internet data per day, and AT&T transfers about 30 petabytes per day of voice and data telecommunications.
An Introduction to Statistical Concepts for Education and Behavioral Sciences by Richard G. Lomax