Видео похожее на "Mathematical Statistics I - Lecture 1 - UCCS MathOnline", с 1 по 23 из (примерно) 90
Introduction to Probability and Statistics 131A. Lecture 1. Probability
UCI Math 131A: Introduction to Probability and Statistics (Summer 2013)
Lec 01. Introduction to Probability and Statistics: Probability
View the complete course: http://ocw.uci.edu/courses/math_131a_introduction_to_probability_and_statistics.html
Instructor: Michael C. Cranston, Ph.D.

License: Creative Commons CC-BY-SA
Terms of Use: http://ocw.uci.edu/info
More courses at http://ocw.uci.edu

Description: UCI Math 131A is an introductory course covering basic principles of probability and statistical inference. Axiomatic definition of probability, random variables, probability distributions, expectation.

Recorded on June 24, 2013

Required attribution: Cranston, Michael C. Math 131A (UCI OpenCourseWare: University of California, Irvine), http://ocw.uci.edu/courses/math_131a_introduction_to_probability_and_statistics.html. [Access date]. License: Creative Commons Attribution-ShareAlike 3.0 United States License. (http://creativecommons.org/licenses/by-sa/3.0/deed.en_US)
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DAY1/14 Probability & Statistics with Prof David Spiegelhalter
This video forms part of a mathematics course on Probability & Statistics by Prof David Spiegelhalter held at AIMS South Africa in 2012.

Please visit video-courses.aims.ac.za to download the supporting booklet.
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The Truth about the #1 Job in America featuring Bryan Bowman
What do actuaries do and why does this profession consistently rank as one of the best careers? Bryan Bowman discusses a day in the life of an actuary. Bryan works for Milliman FRM, an independent consulting firm with offices located around the globe. He holds a degree in Actuarial Science from the University of Illinois Urbana-Champaign. This event is part of the STEM Speaker series.
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For the Love of Physics (May 16, 2011)
On May 16, 2011, Professor of Physics Emeritus Walter Lewin returned to MIT lecture hall 26-100 for a physics talk and book signing, complete with some of his most famous physics demonstrations to celebrate the publication of his new book: For The Love Of Physics: From the End of the Rainbow to the Edge Of Time - A Journey Through the Wonders of Physics, written with Warren Goldstein.

View the complete course at: http://ocw.mit.edu/8-01F99

License: Creative Commons BY-NC-SA
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What does an actuary do? Learn from the experts.

Studying mathematics, statistics and business can lead to certification as an actuary. Today's actuaries help make critical business decisions in a surprising variety of areas.

Learn more from from the experts, David E. Delahanty, ASA, and Nicole Delahanty, FSA, CIMA, who spoke at the Ask the Actuaries seminar on November 27, 2012.
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1. Introduction and Probability Review
MIT 6.262 Discrete Stochastic Processes, Spring 2011
View the complete course: http://ocw.mit.edu/6-262S11
Instructor: Robert Gallager

License: Creative Commons BY-NC-SA
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Actuarial Studies at University - Andrew Lee
Matrix Graduate Andrew Lee explains all about Actuarial Studies at the Matrix HSC & Careers Open Day. Andrew scored an ATAR of 98.00 and graduated from Concord High School in 2008.

In case you missed it:
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Zelda's Lullaby 10 Hours - Zelda Ocarina of Time
Zelda's Lullaby/Theme. From The Legend of Zelda Ocarina of Time and Majora's Mask.
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Mathematics Gives You Wings
October 23, 2010 - Professor Margot Gerritsen illustrates how mathematics and computer modeling influence the design of modern airplanes, yachts, trucks and cars. This lecture is offered as part of the Classes Without Quizzes series at Stanford's 2010 Reunion Homecoming.

Margot Gerritsen, PhD, is an Associate Professor of Energy Resources Engineering, with expertise in mathematical and computational modeling of energy and fluid flow processes. She teaches courses in energy and the environment, computational mathematics and computing at Stanford University.

Stanford University:

Stanford Alumni Association:

Department of Mathematics at Stanford:

Margot Gerritsen:

Stanford University Channel on YouTube:
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Lec 1 | MIT 18.06 Linear Algebra, Spring 2005
Lecture 1: The Geometry of Linear Equations.
View the complete course at: http://ocw.mit.edu/18-06S05

License: Creative Commons BY-NC-SA
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CT3 (Probability and Mathematical Statistics) April 2013 Exam Review
Brief overview of the on the UK Actuarial Profession's CT3 April 2013 paper.
Full worked solutions are available in our ASET.
See http://www.acted.co.uk/html/paper_aset.htm
Просмотров: 2439
Lecture 1 | Modern Physics: Statistical Mechanics
March 30, 2009 - Leonard Susskind discusses the study of statistical analysis as calculating the probability of things subject to the constraints of a conserved quantity. Susskind introduces energy, entropy, temperature, and phase states as they relate directly to statistical mechanics.

Stanford University:

Stanford Continuing Studies Program:

Stanford University Channel on YouTube:
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Lecture 22: Normal, uniform, and exponential distributions; misuse of statistics
Come like us at http://www.facebook.com/TonysTutorials

This subject is aimed at students with little or no programming experience. It aims to provide students with an understanding of the role computation can play in solving problems. It also aims to help students, regardless of their major, to feel justifiable


This video does not belong to me and I do not claim it as mine. It is a free download from the itunes store, I am just uploading for peoples convenience
Просмотров: 3328
Statistics 101: Understanding Correlation
Statistics 101: Understanding Correlation

In this video we discuss the basic concepts of another bivariate relationship; correlation. Previous videos examined covariance and in this lesson we tie the two concepts together. Correlation comes with certain caveats and we talk about those as well. Finally we walk through a simple example involving correlation and its interpretation. Enjoy! For my complete video library organized by playlist, please go to my video page here:

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How to Get Your PhD in Mathematics & Statistics (pt. 1)
Check out the full guide on WorldWideLearn.com at http://www.worldwidelearn.com/doctorate-degrees/doctoral-guide/science-math/phd-in-mathematics-statistics.html
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Complete video for Add Maths Statistics
In this video, you will learn how to get mode mark, mean, median, variances and standard deviation from a group data in SPM Add Maths for both paper 1 and paper 2. This video is very important especially when you forget how to use the formula for median and standard deviation. Anyways, If you would like to have more interaction with me, or ask me more question, or suggest some chapter u want to learn, please add the facebook page at "y=mx+c" or www.facebook.com/maths.video.
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o The mathematics of the collection, organization, and interpretation of numerical data, especially the analysis of population characteristics by inference from sampling
o The subject of statistics can be divided into descriptive statistics - describing data, and inferential Statistics - drawing conclusions from data (Source: dictionary.com)

Descriptive Statistics : To describe a phenomenon
o Summary and presentation of data

Inferential Statistics: To draw conclusions
o Making statements or predictions about the population based on statistical information

POPULATION: is the group of all objects or individuals of interest.
o All York Students
o Canadians
SAMPLE: is a subset of the population
o 40 York students chosen at random
o People interviewed for the latest election poll
o We refer to the individual components of a sample as "observations"

Very generally we can say that:
o Populations are described by PARAMETERS
o Samples are described by STATISTICS

For example:
Parameter: the average hair length of all domestic cats (reflects the true value for the population)
Statistic: the average hair length of cats in my sample (it's an estimate)

Statistical inference: is the process of drawing a conclusion about the population based on the sample (with certain levels of confidence and significance)

A variable is a characteristic of a population or sample.
o student grades, height, income, etc.
Variables have values
o student marks (0..100)
Data are the observed values of a variable.
o student marks: {67, 74, 71, 83, 93, 55, 48}

We have a phenomenon of interest and we would like to collect data to study it further
o We can directly collect the data: this is called PRIMARY DATA.
o We can use data collected by others (e.g. Statistics Canada; market research companies; etc.): this is called SECONDARY DATA
1. By observations
2. By experiment
3. By survey
The difference is generally in the amount of control exercised by the researcher and the strength of the inference that can be made

Sample Population
o From which population do we sample?
o Why is this important? What do we have to consider?
Sample Size
o How large should the sample be?
Sampling Method
o How should we pick the sample out of the population?

o The size of the population
The sample size will INCREASE with the population size

o The variation in the population
The sample size will INCREASE with the variation

o The amount of error that can be tolerated
The sample size will DECREASE with the accepted error

o The amount of resources available
The sample size will INCREASE with resources

There are several statistical sampling methods you can use:
1. Simple Random Sample
2. Stratified Random Sample
3. Cluster Sample

Each subject is equally likely to be chosen
o Like raffles, drawing from a hat, etc.
o Subject choice is determined by random numbers

The population is divided into mutually exclusive subgroups called strata
o i.e. age, gender, home type
Within strata, the sampling is random (simple)
Advantages: Assures the sample has the same structure as the population
Inferences can also be made about the subcategories

The population is divided into groups, called clusters
Geographical regions, classrooms in a school
Each clusters ideally has the same characteristics as the population
We use simple random sampling to select only a few clusters
We then use either simple random or stratified sampling within each cluster

A sampling error refers to the difference between the sample statistic and the population parameter
Example: survey shows 51% of students work when in fact only 50.42% work
We will learn how to deal with this error in later classes

A non-sampling Error refers to errors in data acquisition Inaccuracies & mistakes; less-than-truthful responses
Non-response Bias: only people with a certain agenda respond to the survey
Selection bias: sampling problems
Просмотров: 46099
Issues in Financial Mathematics and Statistics
The inauguration of the Center for Research in Financial Mathematics and Statistics at UC Santa Barbara featured three distiguished speakers addressing issues in financial mathematics and statistics. The talks are titled: A Practitioner's Overview of Mathematical Finance; Investments: Theory and Practice; Are There Central Problems in Finance? Series: "Voices" [5/2007] [Science] [Show ID: 12126]
Просмотров: 44756
The surprising beauty of mathematics: Jonathan Matte at TEDxGreensFarmsAcademy
Jonathan Matte has been teaching Mathematics for 20 years, the last 13 at Greens Farms Academy. Formerly the Mathematics Department Chair, he is currently the 12th Grade Dean and Coach of the GFA Math Team and the CT State Champion Quiz Team. A former Jeopardy! contestant, Jon's outside-of-the classroom passions lie in the world of puzzles and games, both as a competitor (in the American Crossword Puzzle Tournament and the World Puzzle Championships, among others) and a creator (orchestrating the long-running GFA Puzzle Hunt and crafting puzzles that have made their way into GAMES Magazine).

In the spirit of ideas worth spreading, TEDx is a program of local, self-organized events that bring people together to share a TED-like experience. At a TEDx event, TEDTalks video and live speakers combine to spark deep discussion and connection in a small group. These local, self-organized events are branded TEDx, where x = independently organized TED event. The TED Conference provides general guidance for the TEDx program, but individual TEDx events are self-organized.* (*Subject to certain rules and regulations)
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Probability Density Functions (Introduction) : ExamSolutions
Go to http://www.examsolutions.net/maths-revision/index.php to see the main index of maths video tutorials
Просмотров: 49018
Mathematics and Statistics at Oxford University
Want to know more about studying at Oxford University? Watch this short film to hear tutors and students talk about this undergraduate degree. For more information on this course, please visit our website at http://www.ox.ac.uk/admissions/undergraduate_courses/courses/mathematics_and_statistics/mathematics_and.html
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Intro to Probabilities in Statistics (Full Length)
An AP Statistics lecture introducing probabilities, randomness, Law of Large Numbers, Probability Model, Tree Diagram, 5 Rules of Probability,etc.
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