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Averages: Still Flawed
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This learning video continues the theme of an early BLOSSOMS lesson, Flaws of Averages, using new examples—including how all the children from Lake Wobegon can be above average, as well as the Friendship Paradox. As mentioned in the original module, averages are often worthwhile representations of a set of data by a single descriptive number. The objective of this module, once again, is to simply point out a few pitfalls that could arise if one is not attentive to details when calculating and interpreting averages. Most students at any level in high school can understand the concept of the flaws of averages presented here. The essential prerequisite knowledge for this video lesson is the ability to calculate an average from a set of numbers. Materials needed include: pen and paper for the students; a blackboard or equivalent; and coins (one per student) or something similar that students can repeatedly use to create a random event with equal chances of the two outcomes (e.g. flipping a fair coin). The coins or something similar are recommended for one of the classroom activities, which will demonstrate the idea of regression toward the mean. Another activity will have the students create groups to show how the average number of friends of friends is greater than or equal to the average number of friends in a group, which is known as The Friendship Paradox. The lesson is designed for a typical 50-minute class session.

Subject:
Mathematics
Material Type:
Lecture
Provider:
MIT
Provider Set:
MIT Blossoms
Author:
Dan Livengood, Rhonda Jordan
Date Added:
02/13/2015
Big Data Analytics: IOT BASED RECOMMENDATION SYSTEM FOR TOURISM
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The IOT services are for customer convenience, control in online booking IOT services such as radio station, smart coffee makers, dim lights and energy programmed lights. Our System will able to recommend the valid customer opinion by analyzing UAE, UK and Oman hotel aspects like services, value, cleanliness and location from customers’ reviews. it include the Big Analytics, Hadoop, HDFS, Sentiment Analytics, Emotion Analytics, ANOVA in Map-Reduce.

Subject:
Computer Science
Material Type:
Module
Author:
Sharjeel Imtiaz
Date Added:
04/11/2019
Big Data Analytics: IOT Recomendation system for Tourism
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This project will recommend a big data analytics tool for the customers, ministry and hotels in Oman to adapt new hotel services after considering together hotel services with customer opinions. The IOT services are for customer convenience, control in online booking IOT services such as radio station, smart coffee makers, dim lights and energy programmed lights.The big data analytics will analyze the hotel information , rating and reviews of UK , Dubai to recomend aspect like services especially IOT services. The coverage of Analysis in R: Big data Analytics with Hadoop/HDFS Sentiment AnalysisEmotion Analysis Machine Learning K-mean , Regression and Neural NetworkAnova version to analyze Big data of 90k reviews 

Subject:
Information Science
Material Type:
Module
Author:
sharjeel imtiaz
Date Added:
04/11/2019
Biostatistics Methods 2
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The aim of this course is to provide fundamental statistical concepts and tools relevant to the practice of summarizing, analyzing, and visualizing data. This course will build your knowledge of the fundamental principles of biostatistical inference. The course will focus on linear regression and generalized linear regression models. We will use a variety of examples and exercises from scientific, medical, and public health research.

Material Type:
Activity/Lab
Full Course
Homework/Assignment
Interactive
Lecture Notes
Lesson Plan
Simulation
Syllabus
Provider:
University of Massachusetts
Provider Set:
Individual Authors
Author:
Jeff Goldsmith
Nicholas G Reich
Date Added:
04/07/2014
Econometrics Textbook
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Econometrics is the study of estimation and inference for economic models using economic data. Econometric theory concerns the study and development of tools and methods for applied econometric applications. Applied econometrics concerns the application of these tools to economic data.

Subject:
Business and Communication
Economics
Material Type:
Textbook
Provider:
University of Wisconsin
Author:
Bruce Hansen
Date Added:
01/01/2016
Identifying a Theft Suspect
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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.

Subject:
Mathematics
Material Type:
Activity/Lab
Assessment
Provider:
Science Education Resource Center (SERC) at Carleton College
Provider Set:
Measuring Study Effectiveness
Pedagogy in Action
Date Added:
08/28/2012
Introductory Business Statistics
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The book "Introductory Business Statistics" by Thomas K. Tiemann explores the basic ideas behind statistics, such as populations, samples, the difference between data and information, and most importantly sampling distributions. The author covers topics including descriptive statistics and frequency distributions, normal and t-distributions, hypothesis testing, t-tests, f-tests, analysis of variance, non-parametric tests, and regression basics. Using real-world examples throughout the text, the author hopes to help students understand how statistics works, not just how to "get the right number."

Subject:
Statistics and Probability
Material Type:
Textbook
Provider:
BCcampus
Provider Set:
BCcampus Open Textbooks
Author:
Thomas K. Tiemann
Date Added:
12/02/2019
Introductory Business Statistics with Interactive Spreadsheets - 1st Canadian Edition
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CC BY
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"Introductory Business Statistics with Interactive Spreadsheets - 1st Canadian Edition" is an adaptation of Thomas K. Tiemann's book, "Introductory Business Statistics". In addition to covering basics such as populations, samples, the difference between data and information, and sampling distributions, descriptive statistics and frequency distributions, normal and t-distributions, hypothesis testing, t-tests, f-tests, analysis of variance, non-parametric tests, and regression basics, the following information has been added: the chi-square test and categorical variables, null and alternative hypotheses for the test of independence, simple linear regression model, least squares method, coefficient of determination, confidence interval for the average of the dependent variable, and prediction interval for a specific value of the dependent variable. This new edition also allows readers to learn the basic and most commonly applied statistical techniques in business in an interactive way -- when using the web version -- through interactive Excel spreadsheets. All information has been revised to reflect Canadian content.

Subject:
Business and Communication
Statistics and Probability
Material Type:
Textbook
Provider:
BCcampus
Provider Set:
BCcampus Open Textbooks
Author:
Mohammad Mahbobi, Thompson Rivers University; Thomas K. Tiemann, Elon University
Date Added:
04/19/2016
Psychology
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CC BY
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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, Personality, Freud and the Psychodynamic Perspective
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By the end of this section, you will be able to:Describe the assumptions of the psychodynamic perspective on personality developmentDefine and describe the nature and function of the id, ego, and superegoDefine and describe the defense mechanismsDefine and describe the psychosexual stages of personality development

Subject:
Social Science
Psychology
Material Type:
Module
Provider:
Rice University
Provider Set:
OpenStax College
Date Added:
07/10/2017
Regression
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This applet from Statistical Java allows the user to generate bivariate data for analysis with simple linear regression. The page describes the equations used to generate the data and estimate the regression lines.

Subject:
Statistics and Probability
Material Type:
Activity/Lab
Provider:
Consortium for the Advancement of Undergraduate Statistics Education
Provider Set:
Causeweb.org
Author:
Anderson-Cook, C.
C.Anderson-Cook
Dorai-Raj, S.
Robinson, T.
S.Dorai-Raj
T.Robinson
Date Added:
02/16/2011
Star Library: Regression - Residuals - Why?
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As teachers of statistics, we know that residual plots and other diagnostics are important to deciding whether or not linear regression is appropriate for a set of data. Despite talking with our students about this, many students might believe that if the correlation coefficient is strong enough, these diagnostic checks are not important. The data set included in this activity was created to lure students into a situation that looks on the surface to be appropriate for the use of linear regression but is instead based (loosely) on a quadratic function.

Subject:
Statistics and Probability
Material Type:
Activity/Lab
Provider:
Consortium for the Advancement of Undergraduate Statistics Education
Provider Set:
Causeweb.org
Author:
Jacqueline B. Miller
Miller, Jacqueline B.
Date Added:
02/16/2011
Star Library: Regression on the Rebound
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This activity is an advanced version of the “Keep your eyes on the ball” activity by Bereska, et al. (1999). Students should gain experience with differentiating between independent and dependent variables, using linear regression to describe the relationship between these variables, and drawing inference about the parameters of the population regression line. Each group of students collects data on the rebound heights of a ball dropped multiple times from each of several different heights. By plotting the data, students quickly recognize the linear relationship. After obtaining the least squares estimate of the population regression line, students can set confidence intervals or test hypotheses on the parameters. Predictions of rebound length can be made for new values of the drop height as well. Data from different groups can be used to test for equality of the intercepts and slopes. By focusing on a particular drop height and multiple types of balls, one can also introduce the concept of analysis of variance.

Subject:
Statistics and Probability
Material Type:
Activity/Lab
Provider:
Consortium for the Advancement of Undergraduate Statistics Education
Provider Set:
Causeweb.org
Author:
Trent D. Buskirk and Linda J. Young, University of Nebraska-Lincoln and University of Nebraska-Lincoln
Date Added:
02/16/2011
Star Library: What is the Shelf Life?
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The Food and Drug Administration requires pharmaceutical companies to establish a shelf life for all new drug products through a stability analysis. This is done to ensure the quality of the drug taken by an individual is within established levels. The purpose of this out-of-class project or in-class example is to determine the shelf life of a new drug. This is done through using simple linear regression models and correctly interpreting confidence and prediction intervals. An Excel spreadsheet and SAS program are given to help perform the analysis.

Subject:
Statistics and Probability
Material Type:
Activity/Lab
Provider:
Consortium for the Advancement of Undergraduate Statistics Education
Provider Set:
Causeweb.org
Author:
Bilder, Christopher R.
Christopher R. Bilder
Date Added:
02/16/2011
When Should I Drink My Hot Chocolate?
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Educational Use
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Students act as food science engineers as they explore and apply their understanding of cooling rate and specific heat capacity by completing two separate, but interconnected, tasks. In Part 1, student groups conduct an experiment to explore the cooling rate of a cup of hot chocolate. They collect and graph data to create a mathematical model that represents the cooling rate, and use an exponential decay regression to determine how long a person should wait to drink the cup of hot chocolate at an optimal temperature. In Part 2, students investigate the specific heat capacity of the hot chocolate. They determine how much energy is needed to heat the hot chocolate to an optimal temperature after it has cooled to room temperature. Two activity-guiding worksheets are included.

Subject:
Mathematics
Algebra
Statistics and Probability
Physical Science
Chemistry
Material Type:
Activity/Lab
Provider:
TeachEngineering
Provider Set:
TeachEngineering
Author:
Brian Palacios
Date Added:
02/17/2017