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  • Statistical Analysis
Remix
Analyzing Findings
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By the end of this section, you will be able to:Explain what a correlation coefficient tells us about the relationship between variablesRecognize that correlation does not indicate a cause-and-effect relationship between variablesDiscuss our tendency to look for relationships between variables that do not really existExplain random sampling and assignment of participants into experimental and control groupsDiscuss how experimenter or participant bias could affect the results of an experimentIdentify independent and dependent variables

Subject:
Social Science
Psychology
Material Type:
Module
Author:
Melinda Boland
Date Added:
01/12/2018
Data Quality: Out of Range Values
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Defines what "Out of Range Values" are and how to account for them in data collection and statistical analysis.

Subject:
Health, Medicine and Nursing
Material Type:
Assessment
Diagram/Illustration
Lecture
Lesson Plan
Unit of Study
Provider:
OER Africa
University of Michigan
Provider Set:
open.Michigan African Health OER Network
Author:
Beverly Musick
Date Added:
07/05/2012
Deciding on which statistics to use.
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This chart will help you identify which statistical analysis to used depending on your research question.How to use: When deciding which statistics to use, first you must ask what is your research question looking for (difference or association). Then, identify what type of variable are you dealing with (Nominal, Ordinal, or Interval / Ratio), then the number of independent variables (IV) or dependent variables (DV) depending on the nature of your hypothesis. Below is a chart to help you identify which analysis to use for your hypothesis testing. 

Subject:
Statistics and Probability
Material Type:
Module
Author:
Antoniette Aizon
Date Added:
04/11/2018
Geographic Information Analysis
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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.

Subject:
Computer Science
Information Science
Physical Geography
Material Type:
Full Course
Provider:
Penn State's College of Earth and Mineral Sciences (http
Penn State University
Provider Set:
// e-education.psu.edu/oer/)
Author:
David O'Sullivan
Date Added:
10/07/2019
Introduction to Statistics (MATH 146)
Unrestricted Use
CC BY
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The main goal of the course is to highlight the general assumptions and methods that underlie all statistical analysis. The purpose is to get a good understanding of the scope, and the limitations of these methods. We also want to learn as much as possible about the assumptions behind the most common methods, in order to evaluate if they apply with reasonable accuracy to a given situation. Our goal is not so much learning bread and butter techniques: these are pre-programmed in widely available and used software, so much so that a mechanical acquisition of these techniques could be quickly done "on the job". What is more challenging is the evaluation of what the results of a statistical procedure really mean, how reliable they are in given circumstances, and what their limitations are.Login: guest_oclPassword: ocl

Subject:
Statistics and Probability
Material Type:
Full Course
Homework/Assignment
Lecture Notes
Lesson Plan
Syllabus
Provider:
Washington State Board for Community & Technical Colleges
Provider Set:
Open Course Library
Date Added:
10/31/2011
Mini Car Design Challenge
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This engineering design challenge is a great hands-on activity that utilizes the engineering design process, 3D modeling, and 3D printing technology. The challenge can be completed individually or in groups of 2 to 3. Students will work to complete the following challenge: Using the design process, design, document, model, and produce a toy car with interchangeable parts.

Subject:
Engineering
Material Type:
Activity/Lab
Lesson Plan
Author:
Zach Potter
Date Added:
12/05/2018
Play it Safe!
Unrestricted Use
Public Domain
Rating

Students will learn how the U.S. Census Bureau helps emergency responders provide support during natural disasters. Then, the teacher will set up various stations around the room to encourage peer-to-peer learning in small groups. Students will rotate from station to station, completing tasks such as creating an emergency preparedness kit, determining the states with the highest risk for hurricanes, and reviewing a series of photos of houses to determine which are most likely to survive a natural disaster.

Subject:
Statistics and Probability
Material Type:
Activity/Lab
Provider:
U.S. Census Bureau
Provider Set:
Statistics in Schools
Date Added:
10/18/2019
Psychology
Unrestricted Use
CC BY
Rating

Psychology is designed to meet scope and sequence requirements for the single-semester introduction to psychology course. The book offers a comprehensive treatment of core concepts, grounded in both classic studies and current and emerging research. The text also includes coverage of the DSM-5 in examinations of psychological disorders. Psychology incorporates discussions that reflect the diversity within the discipline, as well as the diversity of cultures and communities across the globe.Senior Contributing AuthorsRose M. Spielman, Formerly of Quinnipiac UniversityContributing AuthorsKathryn Dumper, Bainbridge State CollegeWilliam Jenkins, Mercer UniversityArlene Lacombe, Saint Joseph's UniversityMarilyn Lovett, Livingstone CollegeMarion Perlmutter, University of Michigan

Subject:
Social Science
Psychology
Material Type:
Full Course
Provider:
Rice University
Provider Set:
OpenStax College
Date Added:
02/14/2014
Psychology, Psychological Research, Analyzing Findings
Conditional Remix & Share Permitted
CC BY-NC
Rating

By the end of this section, you will be able to:Explain what a correlation coefficient tells us about the relationship between variablesRecognize that correlation does not indicate a cause-and-effect relationship between variablesDiscuss our tendency to look for relationships between variables that do not really existExplain random sampling and assignment of participants into experimental and control groupsDiscuss how experimenter or participant bias could affect the results of an experimentIdentify independent and dependent variables

Subject:
Social Science
Psychology
Material Type:
Module
Provider:
Rice University
Provider Set:
OpenStax College
Date Added:
07/10/2017
Quantitative political analysis
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As taught Spring Semester 2011.

The objective of this module is to introduce students to the practice of quantitative data analysis in the social sciences. The lecture component of the module will explore a variety of the most commonly used statistical methods; in the laboratory component, students will learn to apply these techniques to the analysis of social science data. Through assignments, students will have the opportunity to develop and test their own hypotheses and explanations on major research data sets. The module should provide a sound grasp of the possibilities, methods, and dangers inherent in quantitative social and political research.

Module Codes: M14121 (20 credits)

Suitable for study at: Postgraduate Level

Dr Mark Pickup, School of Politics and International Relations

Dr Mark Pickup is a specialist in Comparative politics, with a particular interest in public opinion and democratic representation within North American and European countries. His research focuses on political information, public opinion, the media, election campaigns and electoral institutions.

Dr Pickup is also a Visiting Fellow in the Department of Politics at the University of Oxford, where he runs the Oxford Polling Observatory website

Subject:
Social Science
Material Type:
Student Guide
Provider:
University of Nottingham
Author:
Dr Mark Pickup
Date Added:
03/24/2017
Remix
Reliability & Validity in Selection Methods
Conditional Remix & Share Permitted
CC BY-NC
Rating

By the end of this section, you will be able to:Explain what a correlation coefficient tells us about the relationship between variablesRecognize that correlation does not indicate a cause-and-effect relationship between variablesDiscuss our tendency to look for relationships between variables that do not really existRecognize how correlations coefficients tell us about the relationships between variables specific to selection methods.

Subject:
Social Science
Psychology
Material Type:
Reading
Author:
Melanie Reed
Date Added:
09/18/2020
Statistical Analysis of Flexible Circuits
Read the Fine Print
Educational Use
Rating

Students are introduced to the technology of flexible circuits, some applications and the photolithography fabrication process. They are challenged to determine if the fabrication process results in a change in the circuit dimensions since, as circuits get smaller and smaller (nano-circuits), this could become very problematic. The lesson prepares students to conduct the associated activity in which they perform statistical analysis (using Excel® and GeoGebra) to determine if the circuit dimension sizes before and after fabrication are in fact statistically different. A PowerPoint® presentation and post-quiz are provided. This lesson and its associated activity are suitable for use during the last six weeks of the AP Statistics course; see the topics and timing note for details.

Subject:
Mathematics
Statistics and Probability
Material Type:
Lesson
Provider:
TeachEngineering
Provider Set:
TeachEngineering
Author:
Cunjiang Yu
Miguel R. Ramirez
Minwei Xu
Song Chen
Date Added:
02/17/2017
Statistical Analysis of Methods to Repair Cracked Steel
Read the Fine Print
Educational Use
Rating

Students apply pre-requisite statistics knowledge and concepts learned in an associated lesson to a real-world state-of-the-art research problem that asks them to quantitatively analyze the effectiveness of different cracked steel repair methods. As if they are civil engineers, students statistically analyze and compare 12 sets of experimental data from seven research centers around the world using measurements of central tendency, five-number summaries, box-and-whisker plots and bar graphs. The data consists of the results from carbon-fiber-reinforced polymer patched and unpatched cracked steel specimens tested under the same stress conditions. Based on their findings, students determine the most effective cracked steel repair method, create a report, and present their results, conclusions and recommended methods to the class as if they were presenting to the mayor and city council. This activity and its associated lesson are suitable for use during the last six weeks of the AP Statistics course; see the topics and timing note for details.

Subject:
Statistics and Probability
Material Type:
Activity/Lab
Provider:
TeachEngineering
Provider Set:
TeachEngineering
Author:
Botong Zheng
Miguel R. Ramirez
Mina Dawood
Date Added:
02/17/2017
Statistical Analysis of Temperature Sensors
Read the Fine Print
Educational Use
Rating

Working as if they are engineers aiming to analyze and then improve data collection devices for precision agriculture, students determine how accurate temperature sensors are by comparing them to each other. Teams record soil temperature data during a class period while making changes to the samples to mimic real-world crop conditions—such as the addition of water and heat and the removal of the heat. Groups analyze their collected data by finding the mean, median, mode, and standard deviation. Then, the class combines all the team data points in order to compare data collected from numerous devices and analyze the accuracy of their recording devices by finding the standard deviation of temperature readings at each minute. By averaging the standard deviations of each minute’s temperature reading, students determine the accuracy of their temperature sensors. Students present their findings and conclusions, including making recommendations for temperature sensor improvements.

Subject:
Statistics and Probability
Material Type:
Activity/Lab
Provider:
TeachEngineering
Provider Set:
Activities
Author:
Keith Lehman
Northern Cass
Trent Kosel
Date Added:
06/28/2017
StatsforMedics
Only Sharing Permitted
CC BY-NC-ND
Rating

I have designed and presented the content within StatsforMedics specifically for use by undergraduate medical students who are considering use of statistics for short-term research projects. However, this is with the understanding that students from allied health sciences may also benefit from engaging with the site and its sister site, Statistics CALs. Also, I am currently exploring use of selected content for outreach work in pre-university sectors.

Subject:
Statistics and Probability
Material Type:
Homework/Assignment
Provider:
University of Edinburgh
Author:
Margaret MacDougall
Date Added:
08/21/2018
Understanding Data
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Data is all around us. Everything from the fitness tracker on your wrist to researchers at your local university are creating mountains of data — big data. What does all this data mean? And how can it help us answer important questions such as: What are the leading causes of heart disease? Or what patterns are related to higher pay at your job? Looking at data can help us answer fun questions too — who’s likely to win the next Super Bowl? Leave the boring lectures behind and intuitively learn data analysis through interactive exercises that allow you to play with fascinating real-world datasets. By the end of this course, you’ll be comfortable applying the basics of statistical analysis and econometrics. There are no prerequisites and we encourage you to repeat the interactive lessons as often as you need.

Subject:
Economics
Material Type:
Full Course
Provider:
Marginal Revolution University
Author:
Thomas Stratmann
Date Added:
05/18/2017