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Analytical Chemistry 2.1
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As currently taught in the United States, introductory courses in analytical chemistryemphasize quantitative (and sometimes qualitative) methods of analysis along with a heavydose of equilibrium chemistry. Analytical chemistry, however, is much more than a collection ofanalytical methods and an understanding of equilibrium chemistry; it is an approach to solvingchemical problems. Although equilibrium chemistry and analytical methods are important, theircoverage should not come at the expense of other equally important topics.

The introductory course in analytical chemistry is the ideal place in the undergraduate chemistry curriculum forexploring topics such as experimental design, sampling, calibration strategies, standardization,optimization, statistics, and the validation of experimental results. Analytical methods comeand go, but best practices for designing and validating analytical methods are universal. Becausechemistry is an experimental science it is essential that all chemistry students understand theimportance of making good measurements.

My goal in preparing this textbook is to find a more appropriate balance between theoryand practice, between “classical” and “modern” analytical methods, between analyzing samplesand collecting samples and preparing them for analysis, and between analytical methods anddata analysis. There is more material here than anyone can cover in one semester; it is myhope that the diversity of topics will meet the needs of different instructors, while, perhaps,suggesting some new topics to cover.

Subject:
Chemistry
Material Type:
Textbook
Provider:
DePauw University
Author:
David Harvey
Date Added:
06/20/2016
Behavior of Sample Mean (1 of 3)
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Apply the sampling distribution of the sample mean as summarized by the Central Limit Theorem (when appropriate). In particular, be able to identify unusual samples from a given population.

Subject:
Statistics and Probability
Material Type:
Module
Provider:
Carnegie Mellon University
Provider Set:
Open Learning Initiative
Date Added:
02/28/2018
Central limit theorem
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This video talka about what is easily one of the most fundamental and profound concepts in statistics and maybe in all of mathematics. And that's the central limit theorem.

Subject:
Statistics and Probability
Material Type:
Lesson
Provider:
Khan Academy
Author:
Salman Khan
Date Added:
02/28/2018
Introduction to Statistics
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Introduction to Statistics is a resource for learning and teaching introductory statistics. This work is in the public domain. Therefore, it can be copied and reproduced without limitation. However, we would appreciate a citation where possible. Please cite as: Online Statistics Education: A Multimedia Course of Study (http://onlinestatbook.com/). Project Leader: David M. Lane, Rice University. Instructor's manual, PowerPoint Slides, and additional questions are available.

Subject:
Statistics and Probability
Material Type:
Textbook
Author:
David Lane
Date Added:
04/27/2020
Introductory Business Statistics
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CC BY
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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:
04/27/2020
Introductory Statistics - Chapter 1: Sampling and Data
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Summary
Introductory Statistics follows scope and sequence requirements of a one-semester introduction to statistics course and is geared toward students majoring in fields other than math or engineering. The text assumes some knowledge of intermediate algebra and focuses on statistics application over theory. Introductory Statistics includes innovative practical applications that make the text relevant and accessible, as well as collaborative exercises, technology integration problems, and statistics labs.

Senior Contributing Authors
Barbara Illowsky, De Anza College
Susan Dean, De Anza College

Subject:
Mathematics
Material Type:
Lesson
Module
Unit of Study
Author:
Amanda Postle
Date Added:
08/07/2018
Learning Statistics with R: A tutorial for psychology students and other beginners
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CC BY-SA
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Learning Statistics with R covers the contents of an introductory statistics class, as typically taught to undergraduate psychology students, focusing on the use of the R statistical software. The book discusses how to get started in R as well as giving an introduction to data manipulation and writing scripts. From a statistical perspective, the book discusses descriptive statistics and graphing first, followed by chapters on probability theory, sampling and estimation, and null hypothesis testing. After introducing the theory, the book covers the analysis of contingency tables, t-tests, ANOVAs and regression. Bayesian statistics are covered at the end of the book.

Subject:
Psychology
Material Type:
Textbook
Provider:
University of New South Wales
Author:
Danielle Navarro
Date Added:
01/01/2018
Parameters vs. Statistics
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LEARNING OBJECTIVE: Identify and distinguish between a parameter and a statistic.

LEARNING OBJECTIVE: Explain the concepts of sampling variability and sampling distribution.

Subject:
Statistics and Probability
Material Type:
Module
Provider:
Carnegie Mellon University
Provider Set:
Open Learning Initiative
Date Added:
02/28/2018
Research Methods in Social Psychology
Unrestricted Use
CC BY
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Social psychologists are interested in the ways that other people affect thought, emotion, and behavior. To explore these concepts requires special research methods. Following a brief overview of traditional research designs, this module introduces how complex experimental designs, field experiments, naturalistic observation, experience sampling techniques, survey research, subtle and nonconscious techniques such as priming, and archival research and the use of big data may each be adapted to address social psychological questions. This module also discusses the importance of obtaining a representative sample along with some ethical considerations that social psychologists face.

Subject:
Psychology
Material Type:
Module
Provider:
Diener Education Fund
Provider Set:
Noba
Author:
Rajiv Jhangiani
Date Added:
02/28/2018
Sampling
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1). Identify the sampling method used in a study and discuss its implications and potential limitations.
2). Critically evaluate the reliability and validity of results published in mainstream media.
3). Summarize and describe the distribution of a categorical variable in context.

Subject:
Statistics and Probability
Material Type:
Module
Provider:
Carnegie Mellon University
Provider Set:
Open Learning Initiative
Date Added:
02/28/2018
Statistical Thinking for the 21st Century
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Statistical thinking is a way of understanding a complex world by describing it in relatively simple terms that nonetheless capture essential aspects of its structure, and that also provide us some idea of how uncertain we are about our knowledge. The foundations of statistical thinking come primarily from mathematics and statistics, but also from computer science, psychology, and other fields of study.

Subject:
Statistics and Probability
Material Type:
Textbook
Author:
Russel A. Poldrack
Date Added:
04/27/2020
Statistics Course Content
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Introductory statistics course developed through the Ohio Department of Higher Education OER Innovation Grant. The course is part of the Ohio Transfer Module and is also named TMM010. For more information about credit transfer between Ohio colleges and universities please visit: www.ohiohighered.org/transfer.Team LeadKameswarrao Casukhela                     Ohio State University – LimaContent ContributorsEmily Dennett                                       Central Ohio Technical CollegeSara Rollo                                            North Central State CollegeNicholas Shay                                      Central Ohio Technical CollegeChan Siriphokha                                   Clark State Community CollegeLibrarianJoy Gao                                                Ohio Wesleyan UniversityReview TeamAlice Taylor                                           University of Rio GrandeJim Cottrill                                             Ohio Dominican University

Subject:
Mathematics
Statistics and Probability
Material Type:
Full Course
Provider:
Ohio Open Ed Collaborative
Date Added:
04/17/2018
Statistics Course Content, Sampling Methods, Producing Data – Sampling Methods
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Producing Data – Sampling MethodsIn this module we will explore the different sampling methods to obtain representative samples from a population. We also learn about the relative advantages and disadvantages of each method. Learning Objectives:Reasons for samplingRandom Vs. Non-Random SamplesSampling Bias and VariabilityRandom Sampling Methods – Simple, Stratified, Systematic, Cluster and Multistage random samplesNon-Random Sampling Methods – Voluntary Response and Convenience samplingSample surveys, sampling errorsBest method of random samplingSampling distributions

Subject:
Statistics and Probability
Material Type:
Module
Provider:
Ohio Open Ed Collaborative
Date Added:
07/02/2018