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.

Handling statistical data is an essential part of psychological research. However, many people find the idea of using statistics, and especially statistical software packages, extremely daunting. This unit takes a step-by-step approach to statistics softw

This site teaches Statistics and Probability to 7th graders through a series of 475 questions and interactive activities aligned to 10 Common Core mathematics skills.

This site teaches Statistics and Probability to 8th graders through a series of 303 questions and interactive activities aligned to 5 Common Core mathematics skills.

This site is dedicated to making high value health data more accessible to entrepreneurs, researchers, and policy makers in the hopes of better health outcomes for all.

Homework 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.

Homework 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.

Homework 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.

Lecture 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.

Homework 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.

Spreadsheets Across the Curriculum module. Students analyze the percentage of change in the diversity of the US compared to New York City over 20 years.

This course is an introduction to data cleaning, analysis and visualization. We will teach the basics of data analysis through concrete examples. You will learn how to take raw data, extract meaningful information, use statistical tools, and make visualizations.
This was offered as a non-credit course during the Independent Activities Period (IAP), which is a special 4-week term at MIT that runs from the first week of January until the end of the month.

How to do science: a guide to researching human physiology has been written for students of the life sciences who are actively engaged in the scientific process. A lot of support is available for students learning scientific facts, but we found that it was harder to find resources to support students to become scientists.

This ebook introduces you to what it means to be a scientist. You will learn about the scientific method and how to do many tasks of a scientist, your roles and responsibilities as a scientist as well as possible career paths, and how to use your skills as a science graduate to get a leg up in the job market.

This text is published by the La Trobe eBureau.

This book is intended for those who want to learn the basics of SPSS. It can be used as a text in a class or by those working independently. Each chapter has instructions that guide you through a series of problems, as well as graphics showing you what your screen should look like at various steps in the process. There are also exercises at the end of each chapter for further practice and more exercises and teaching modules are on the Teaching Resources page of the Social Science Research and Instructional Center (SSRIC) website.

This model-eliciting activity (MEA) challenges students to develop a model for predicting the characteristics of a person who has committed a crime. Students work with real data on shoe length, height, and gender to develop the model. Students write a report to the crime victim that identifies a suspect and justifies their decision. The activity sets the stage for students to learn about regression models, and reinforces their understanding of central tendency and variability. It is suggested that this activity be used prior to a formal introduction to linear relationships.

Introductory statistics courses prepare students to think statistically but cover relatively few statistical methods. Building on the basic statistical thinking emphasized in an introductory course, a second course in statistics at the undergraduate level can explore a large number of statistical methods. This text covers more advanced graphical summaries, One-Way ANOVA with pair-wise comparisons, Two-Way ANOVA, Chi-square testing, and simple and multiple linear regression models. Models with interactions are discussed in the Two-Way ANOVA and multiple linear regression setting with categorical explanatory variables. Randomization-based inferences are used to introduce new parametric distributions and to enhance understanding of what evidence against the null hypothesis “looks like”. Throughout, the use of the statistical software R via Rstudio is emphasized with all useful code and data sets provided within the text. This is Version 3.0 of the book.

This course is intended to assist undergraduates with learning the basics of programming in general and programming MATLAB® in particular.

This course provides graduate students in the sciences with an intensive introduction to applied statistics. Topics include descriptive statistics, probability, non-parametric methods, estimation methods, hypothesis testing, correlation and linear regression, simulation, and robustness considerations. Calculations will be done using handheld calculators and the Minitab Statistical Computer Software.