Scientists should be able to provide support for the absence of a meaningful effect. Currently, researchers often incorrectly conclude an effect is absent based a nonsignificant result. A widely recommended approach within a frequentist framework is to test for equivalence. In equivalence tests, such as the two one-sided tests (TOST) procedure discussed in this article, an upper and lower equivalence bound is specified based on the smallest effect size of interest. The TOST procedure can be used to statistically reject the presence of effects large enough to be considered worthwhile. This practical primer with accompanying spreadsheet and R package enables psychologists to easily perform equivalence tests (and power analyses) by setting equivalence bounds based on standardized effect sizes and provides recommendations to prespecify equivalence bounds. Extending your statistical tool kit with equivalence tests is an easy way to improve your statistical and theoretical inferences.

This is a text-based STEM Inquiry, focusing on the mathematical standard of making inferences and justifying conclusions while evaluating reports based on data. The unit culminates in students presenting their findings comparing local to national data regarding the relationships between educational attainment and financial earnings.

This is a comprehensive math textbook for Grade 12. It can be downloaded, read on-line on a mobile phone, computer or iPad. Every chapter has links to on-line video lessons and explanations. Summary presentations at the end of each chapter offer an overview of the content covered, with key points highlighted for easy revision. Topics covered are: language of mathematics, logarithms, sequences and series, finance, factorising cubic polynomials, functions and graphs, differential calculus, linear programming, geometry, trigonometry, statistics, combinations and permutations. This book is based upon the original Free High School Science Text series.

This book discusses what is currently known about software engineering, based on an analysis of all the publicly available data. This aim is not as ambitious as it sounds, because there is not a great deal of data publicly available.

The intent is to provide material that is useful to professional developers working in industry; until recently researchers in software engineering have been more interested in vanity work, promoted by ego and bluster.

The material is organized in two parts, the first covering software engineering and the second the statistics likely to be needed for the analysis of software engineering data.

The evolution of ideas is often ignored in the teaching of statistics. It is important to show students how definitions and formulas evolve. This activity describes a fairly straightforward activity of how measures of association can evolve.

Midterm Exam Review for the course "CS 217 – Probability and Statistics for Computer Science" delivered at the City College of New York in Spring 2019 by Evan Agovino as part of the Tech-in-Residence Corps program.

Practice Final Exam for the course "CS 217 – Probability and Statistics for Computer Science" delivered at the City College of New York in Spring 2019 by Evan Agovino as part of the Tech-in-Residence Corps program.

As mentioned in the syllabus, we are using MS Excel for statistical analysis in our OER stats course. We have, therefore, created three templates, one for each of the three units that the course is divided into, in order to help students make necessary calculations. Creating templates was a necessary steps since students are not expected to know how to use Excel functions for statistics and these templates are to guide students into getting relevant stats for their data, such as average, median, histograms, etc. Each template is divided into separate spreadsheets according to the topics. These templates help cut down the tedious work of evaluating statics and help students focus more on the concepts and analyse the results.

This OER is an online tutorial for students learning the software R. The OER is anintroductory guide to exploratory data analysis in R. It is aimed primarily at graduate students in the biomedical sciences, but could be more broadly applicable.

The materials are an interactive tutorial that guides students through some basic analysis, asking them to input code to answer questions about conducting such analysis in R. Hints for the correct code are provided and a short quiz tests them on what they have learnt in the tutorial.

This guide is written using the ‘learnr’ package in the R software environment. The underlying R Markdown source code for this OER material is provided for download, with the intention that lecturers can modify it according to specific requirements. Students can use these materials as a standalone learning resource and use these materials for further learning.

These resources were created to compliment our undergraduate statistics lab manual, Applied Data Analysis in Psychology: Exploring Diversity with Statistics, published by Kendall Hunt publishing company. Like our lab manual, these JASP walk-through guides meaningfully and purposefully integrate and highlight diversity research to teach students how to analyze data in an open-source statistical program. The data sets utilized in these guides are from open-access databases (e.g., Pew Research Center, PLoS One, ICPSR, and more). Guides with step-by-step instructions, including annotated images and examples of how to report findings in APA format, are included for the following statistical tests: independent samples t test, paired samples t test, one-way ANOVA, two factor ANOVA, chi-square test, Pearson correlation, simple regression, and multiple regression.

In this activity, students use real water chemistry data and descriptive statistics in Excel to examine primary productivity in an urban estuary of the Salish Sea. They will consider how actual data do or do not support expected annual trends.

In this activity, students use real water chemistry data and descriptive statistics in Excel to examine primary productivity in an urban estuary of the Salish Sea. They will consider how actual data do or do not support expected annual trends.

This learning video presents an introduction to the Flaws of Averages using three exciting examples: the ''crossing of the river'' example, the ''cookie'' example, and the ''dance class'' example. Averages are often worthwhile representations of a set of data by a single descriptive number. The objective of this module, however, is to simply point out a few pitfalls that could arise if one is not attentive to details when calculating and interpreting averages. The essential prerequisite knowledge for this video lesson is the ability to calculate an average from a set of numbers. During this video lesson, students will learn about three flaws of averages: (1) The average is not always a good description of the actual situation, (2) The function of the average is not always the same as the average of the function, and (3) The average depends on your perspective. To convey these concepts, the students are presented with the three real world examples mentioned above.

This is a textbook for an Introduction to Statistics class. I use it at a community college. The approach in this book is to develop an intuitive understanding of sampling distributions and inference in the first chapter so these can be used in the remainder of the book.

Spreadsheets Across the Curriculum module. Students examine the number of large earthquakes (magnitude 7 and above) per year for 1970-1999 and 1940-1999. QL: descriptors of a frequency distribution.

(Note: this resource was added to OER Commons as part of a batch upload of over 2,200 records. If you notice an issue with the quality of the metadata, please let us know by using the 'report' button and we will flag it for consideration.)

Spreadsheets Across the Curriculum module. Students examine the number of large earthquakes (magnitude 7 and above) per year for 1970-1999 and 1940-1999. QL: descriptors of a frequency distribution.

Introductory Statistics Course covering hypothesis testing, confidence interval, sampling, probability, counting techniques, correlation, linear regression, data collection and more.

This course will focus on understanding aspects of modern technology displaying exponential growth curves and the impact on global quality of life through a weekly updated class project integrating knowledge and providing practical tools for political and business decision-making concerning new aspects of bioengineering, personalized medicine, genetically modified organisms, and stem cells. Interplays of economic, ethical, ecological, and biophysical modeling will be explored through multi-disciplinary teams of students, and individual brief reports.

This course will assess the relationships among sequence, structure, and function in complex biological networks as well as progress in realistic modeling of quantitative, comprehensive, functional genomics analyses. Exercises will include algorithmic, statistical, database, and simulation approaches and practical applications to medicine, biotechnology, drug discovery, and genetic engineering. Future opportunities and current limitations will be critically addressed. In addition to the regular lecture sessions, supplementary sections are scheduled to address issues related to Perl, Mathematica and biology.

In this data rich world, we need to understand how things are organized on the Earth's surface. Those things are represented by spatial data and necessarily depend upon what surrounds them. Spatial statistics provide insights into explaining processes that create patterns in spatial data. In geographical information analysis, spatial statistics such as point pattern analysis, spatial autocorrelation, and spatial interpolation will analyze the spatial patterns, spatial processes, and spatial association that characterize spatial data. Understanding spatial analysis will help you realize what makes spatial data special and why spatial analysis reveals a truth about spatial data.