This task involves two aspects of statistical reasoning: providing a probabilistic model for the situation at hand, and defining a way to collect data to determine whether or not the observed data is reasonably likely to occur under the chosen model. When guessing between two choices, there is no reason to suspect that one outcome is more likely than the other. Thus, a model that assumes the two outcomes to be equally likely (such as flipping a coin) is appropriate.

In this module, students are asked to look at how long it takes for planets and moons to complete their orbits, and how fast they are going.

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Students will participate in an online scavenger hunt based on a story that a geographer named Gina, who loves to travel, has escaped to an undisclosed location. It is their mission to bring her back to the school. Students must follow a series of clues about the location including landmarks, weather, and population—and use a U.S. Census Bureau data tool called State Facts for Students to answer questions that lead them one step closer to finding Gina.

We’ve talked a lot in this series about how often you see data and statistics in the news and on social media - which is ALL THE TIME! But how do you know who and what you can trust? Today, we’re going to talk about how we, as consumers, can spot flawed studies, sensationalized articles, and just plain poor reporting. And this isn’t to say that all science articles you read on facebook or in magazines are wrong, but that it's valuable to read those catchy headlines with some skepticism.

This is a task from the Illustrative Mathematics website that is one part of a complete illustration of the standard to which it is aligned. Each task has at least one solution and some commentary that addresses important aspects of the task and its potential use.

In this introductory Excel tutorial (Activity I) students use Excel to explore the geodynamics model equation for ocean depth around a sea-floor spreading center. For students with no prior Excel experience.

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In this introductory Excel tutorial (Activity I) students use Excel to explore the geodynamics model equation for ocean depth around a sea-floor spreading center. For students with no prior Excel experience.

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Students import ocean bathymetry data from text files, they then use Excel to graph these observations along with model prediction to assess the model's ability to simulated the observed topographic features of the North Atlantic.

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Students import ocean bathymetry data from text files, they then use Excel to graph these observations along with model prediction to assess the model's ability to simulated the observed topographic features of the North Atlantic.

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In this exercise students work with light, temperature, and phytoplankton biomass proxy (chlorophyll a concentration) data to;

Become more skilled in reading and interpreting semi log graphs, temperature profiles, and time series plots.
Practice unit conversions.
Gain an understanding of k, the attenuation coefficient for nondirectional light.
See how the depth of the photic zone and the surface mixed layer varies seasonally at temperate latitudes and how this relates to seasonal phytoplankton productivity dynamics.

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An activity/lab where students determine the changes in 100-year flood determinations for 2 streams over time.

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Within this unit, I will take a three level design that is planned to make these courses more relevant to students and promote questions that interrogate the authority of statistics that students will encounter throughout the course and in their lives.

The skill of interrogating statistics is crucial for all adults in our society to become thinking consumers and users of data. In addition, it is important to deconstruct data to see implicit ideas of domination and subjugation that travel through numbers that can appear nuetral. Statistics shares a creation story with the field of Eugenics. Francis Galton, a mathematician who contributed many of the major ideas to statistics was also one of the originators of eugenics. The influence of eugenic thinking in statistics drives a notion of superiority, fitness and ranking alongside measurements. Milton Reynolds describes this in Shifting Frames:,” The term “eugenics” refers to a scientifically based, ideological movement dedicated to the reiification of race. It is the wellspring of scientific theories used to construct taxonomies of difference within the human family and to legitimize the subjugation of different groups.”.1 Statistics often does the work of justifying this subjugation through its “innocent” and authoritative work as a logical system. These embedded assumptions of superiority are validated by the seeming neutrality of mathematical calculations. The “taxonomies of difference” he describes are invalid and biased assumptions about difference that dominate our interpretations of data, however they appear as factual products legitimized by math.

This activity helps students develop a better understanding and stronger reasoning skills about the Central Limit Theorem and normal distributions.

Key words: Sample, Normal Distribution, Model, Distribution, Variability, Central Limit Theorem (CLT)

This activity allows students to plot arrival times for direct and head waves in a simple refraction system (2 or 3 layers, assuming horizontal interfaces). Students use provided MATLAB functions to investigate the effects of changing layer thicknesses and velocities on arrival times and crossover distances.

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An in-class activity for connecting earthquake magnitude, shaking, and intensity.

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When collecting data to make observations about the world it usually just isn't possible to collect ALL THE DATA. So instead of asking every single person about student loan debt for instance we take a sample of the population, and then use the shape of our samples to make inferences about the true underlying distribution our data. It turns out we can learn a lot about how something occurs, even if we don't know the underlying process that causes it. Today, we’ll also introduce the normal (or bell) curve and talk about how we can learn some really useful things from a sample's shape - like if an exam was particularly difficult, how often old faithful erupts, or if there are two types of runners that participate in marathons!

Seismograms of the 2004 M9.0 Sumatra earthquake, as recorded on station WANC on Wrnagell volcano, Alaska. The red signal shows the raw data and the blue represented data that have been lowpass filtered. The red spikes near 2800 s are local microearthquakes triggered by the passing of the surface waves.

Provenance: Jackie Caplan-Auerbach, Western Washington University
Reuse: This item is offered under a Creative Commons Attribution-NonCommercial-ShareAlike license http://creativecommons.org/licenses/by-nc-sa/3.0/ You may reuse this item for non-commercial purposes as long as you provide attribution and offer any derivative works under a similar license.

In this exercise, written for an undergraduate seismology class, students use MATLAB to analyze waveforms from the 2004 Sumatra M9.0 earthquake, as they were recorded on three seismic stations in Alaska. Two of the stations are broadbands and one is a short period station. Students use MATLAB scripts (provided) to plot and filter the time series data and to calculate power spectra at the different stations. They also see that surface waves from the Sumatra earthquake triggered microseismicity at Wrangell volcano as they passed through the hydrothermal system, an observation first made by West et al. (2005).

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Click to watch Jackie Caplan-Auerbach discuss her activity or watch the full webinar.

In doing this exercise students learn how the type of instrument and the instrument response affect the appearance of a seismogram. They identify body and surface waves in broadband seismograms. After examining the data on their own, students read a scientific paper that describes how microearthquakes were triggered by the passing surface waves. Not only does this provide them with experience reading and interpreting a scientific paper, but it shows them the types of observations made by the authors when they first analyzed the same data presented in this study.

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An Introduction to Statistics

Short Description:
Significant Statistics: An Introduction to Statistics was adapted and original content added by John Morgan Russell. It is adapted from content published by OpenStax Introductory Statistics, OpenIntro Statistics, and Introductory Statistics for the Life and Biomedical Sciences. NewParaNote to instructors: This book is undergoing active peer review and copyediting. It may change. Please complete this form https://bit.ly/stat-interest to be notified of the status of the book.NewParaSignificant Statistics: An Introduction to Statistics is intended for the one-semester introduction to statistics course for students who are not mathematics or engineering majors. It focuses on the interpretation of statistical results, especially in real world settings, and assumes that students have an understanding of intermediate algebra. In addition to end of section practice and homework sets, examples of each topic are explained step-by-step throughout the text and followed by a 'Your Turn' problem that is designed as extra practice for students. NewParaInstructors reviewing, adopting, or adapting this textbook, please help us understand your use by filling out this form: https://bit.ly/stat-interest.

Long Description:
Significant Statistics: An Introduction to Statistics is intended for the one-semester introduction to statistics course for students who are not mathematics or engineering majors. It focuses on the interpretation of statistical results, especially in real world settings, and assumes that students have an understanding of intermediate algebra. In addition to end of section practice and homework sets, examples of each topic are explained step-by-step throughout the text and followed by a ‘Your Turn’ problem that is designed as extra practice for students.

Word Count: 275456

ISBN: 978-1-949373-37-0

(Note: This resource's metadata has been created automatically by reformatting and/or combining the information that the author initially provided as part of a bulk import process.)

An Introduction to Statistics

Short Description:
Significant Statistics: An Introduction to Statistics was adapted and original content added by John Morgan Russell. It is adapted from content published by OpenStax Introductory Statistics, OpenIntro Statistics, and Introductory Statistics for the Life and Biomedical Sciences. NewParaNote to instructors: This book is undergoing active peer review and copyediting. It may change. Please complete this form https://bit.ly/stat-interest to be notified of the status of the book.NewParaSignificant Statistics: An Introduction to Statistics is intended for the one-semester introduction to statistics course for students who are not mathematics or engineering majors. It focuses on the interpretation of statistical results, especially in real world settings, and assumes that students have an understanding of intermediate algebra. In addition to end of section practice and homework sets, examples of each topic are explained step-by-step throughout the text and followed by a 'Your Turn' problem that is designed as extra practice for students. NewParaInstructors reviewing, adopting, or adapting this textbook, please help us understand your use by filling out this form: https://bit.ly/stat-interest.

Long Description:
Significant Statistics: An Introduction to Statistics is intended for the one-semester introduction to statistics course for students who are not mathematics or engineering majors. It focuses on the interpretation of statistical results, especially in real world settings, and assumes that students have an understanding of intermediate algebra. In addition to end of section practice and homework sets, examples of each topic are explained step-by-step throughout the text and followed by a ‘Your Turn’ problem that is designed as extra practice for students.

Word Count: 198073

(Note: This resource's metadata has been created automatically by reformatting and/or combining the information that the author initially provided as part of a bulk import process.)

Students will determine whether the amount of air in a balloon changes the distance it will travel on a fishing line. They will collect data from multiple tests and then create a graph to visualize the variation.