# Search Results (59)

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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
02/28/2018 Conditions of Use:
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Describe a bivariate relationship's linearity, strength, and direction. In other words, plotting things that take two variables into consideration and trying to see whether there's a pattern with how they relate.

Subject:
Statistics and Probability
Material Type:
Lesson
Provider:
Author:
Salman Khan
02/28/2018 Conditions of Use:
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This is a brief conditional probability examplediscussing probabilities like P(A | B) using breakfast and lunch as examples.

Subject:
Statistics and Probability
Material Type:
Lesson
Provider:
Author:
Salman Khan
02/28/2018 Conditions of Use:
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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:
Author:
Salman Khan
02/28/2018 Conditions of Use:
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Understanding why correlation does not imply causality (even though many in the press and some researchers often imply otherwise).

Subject:
Statistics and Probability
Material Type:
Lesson
Provider:
Author:
Salman Khan
02/28/2018 Conditions of Use:
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This module contains both course worksheets in both pdf and MS Word format.

Subject:
Measurement and Data
Statistics and Probability
Material Type:
Module
Author:
Kameswarrao Casukhela
08/29/2018 Conditions of Use:
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This video defines the differences between discrete and continuous random variables, then works through examples of each.

Subject:
Statistics and Probability
Material Type:
Lesson
Provider:
02/28/2018 Conditions of Use:
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1). Summarize and describe the distribution of a categorical variable in context.
2). Generate and interpret several different graphical displays of the distribution of a quantitative variable (histogram, stemplot, boxplot).
3). Summarize and describe the distribution of a quantitative variable in context: a) describe the overall pattern, b) describe striking deviations from the pattern.
4). Relate measures of center and spread to the shape of the distribution, and choose the appropriate measures in different contexts.
5). Compare and contrast distributions (of quantitative data) from two or more groups, and produce a brief summary, interpreting your findings in context.
5). Apply the standard deviation rule to the special case of distributions having the "normal" shape.

Subject:
Statistics and Probability
Material Type:
Module
Provider:
Carnegie Mellon University
Provider Set:
Open Learning Initiative
02/28/2018 Conditions of Use:
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Compare distributions using the features of shape, center, spread, and outliers.

Subject:
Statistics and Probability
Material Type:
Lesson
Provider:
02/28/2018 Conditions of Use:
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A set of step by step walk through of a hypothesis testing

one proportion z-test using p-values
Chi-square goodness of fit using p-values
one mean with population standard deviation is known using p-values
one mean with population standard deviation is unknown, using p-value

Subject:
Statistics and Probability
Material Type:
Module
Author:
Laura Ralston
03/02/2018 Conditions of Use:
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Short video which describes the difference between a sample and a population.

Subject:
Statistics and Probability
Material Type:
Lesson
Provider:
Author:
Salman Khan
02/28/2018 Conditions of Use:
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Sal shows examples of intersection and union of sets and introduces some set notation.

Subject:
Statistics and Probability
Material Type:
Lesson
Provider:
Author:
Salman Khan
02/28/2018 Conditions of Use:
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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
02/28/2018 Conditions of Use:
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Examples finding probabilities from probability distributions for continuous random variables.

Subject:
Statistics and Probability
Material Type:
Lesson
Provider:
Author:
Salman Khan
02/28/2018 Conditions of Use:
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Explain how a density function is used to find probabilities involving continuous random variables.

Subject:
Statistics and Probability
Material Type:
Module
Provider:
Carnegie Mellon University
Provider Set:
Open Learning Initiative
02/28/2018 Conditions of Use:
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Learning Objective: Apply probability rules in order to find the likelihood of an event.

Subject:
Statistics and Probability
Material Type:
Module
Provider:
Carnegie Mellon University
Provider Set:
Open Learning Initiative
02/28/2018 Conditions of Use:
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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
02/28/2018 Conditions of Use:
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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:
Provider Set:
Causeweb.org
Author:
Trent D. Buskirk and Linda J. Young, University of Nebraska-Lincoln and University of Nebraska-Lincoln
02/28/2018 Conditions of Use:
No Strings Attached
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A variable is any characteristics, number, or quantity that can be measured or counted. A variable may also be called a data item. Age, sex, business income and expenses, country of birth, capital expenditure, class grades, eye colour and vehicle type are examples of variables. It is called a variable because the value may vary between data units in a population, and may change in value over time. There are different ways variables can be described according to the ways they can be studied, measured, and presented.

Subject:
Statistics and Probability
Material Type:
Diagram/Illustration
Provider:
Australian Bureau of Statistics
02/28/2018 Rating

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