Видео похожее на "Mathematical Statistics I - Lecture 1 - UCCS MathOnline", с 1 по 23 из (примерно) 87
1. Introduction and Probability Review
 
76:27
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
More information at http://ocw.mit.edu/terms
More courses at http://ocw.mit.edu
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Mathematics Gives You Wings
 
52:28
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:
http://www.stanford.edu/

Stanford Alumni Association:
http://www.stanfordalumni.org/

Department of Mathematics at Stanford:
http://math.stanford.edu/

Margot Gerritsen:
http://margot.stanford.edu/

Stanford University Channel on YouTube:
http://www.youtube.com/stanford
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Intro to Hypothesis Testing in Statistics
 
23:41
Get the full course at: http://www.MathTutorDVD.com
The student will learn the big picture of what a hypothesis test is in statistics. We will discuss terms such as the null hypothesis, the alternate hypothesis, statistical significance of a hypothesis test, and more.
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Statistics math song By MBP.mp4
 
3:19
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Lecture 24: Gamma distribution and Poisson process | Statistics 110
 
48:49
We introduce the Gamma distribution and discuss the connection between the Gamma distribution and Poisson processes.
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C1: WHAT IS STATISTICS?
 
11:40
WHAT IS STATISTICS?
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)

WHY SHOULD WE STUDY STATISTICS?
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 & SAMPLE
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"

PARAMETERS AND STATISTICS
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)

FINAL DEFINITIONS
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}

ATTAINING THE DATA
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
o
HOW DO WE COLLECT PRIMARY 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

DECISIONS INVOLVED IN SAMPLING
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?

SAMPLE SIZE DEPENDS ON
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

HOW TO CREATE THE SAMPLE
There are several statistical sampling methods you can use:
1. Simple Random Sample
2. Stratified Random Sample
3. Cluster Sample

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

STRATIFIED RANDOM SAMPLE
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

CLUSTER SAMPLING
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

SAMPLING ERRORS
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

NON-SAMPLING ERRORS
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
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Mathematical Statistics I - Lecture 4 - UCCS MathOnline
 
81:54
Taught by Dr. Greg Morrow from University of Colorado in Colorado Springs
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Lecture 1 | Modern Physics: Statistical Mechanics
 
120:52
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:
http://www.stanford.edu/

Stanford Continuing Studies Program:
http://csp.stanford.edu/

Stanford University Channel on YouTube:
http://www.youtube.com/stanford
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Statistical Mechanics Lecture 1
 
107:39
(April 1, 2013) Leonard Susskind introduces statistical mechanics as one of the most universal disciplines in modern physics. He begins with a brief review of probability theory, and then presents the concepts of entropy and conservation of information.

Originally presented in the Stanford Continuing Studies Program.

Stanford University:
http://www.stanford.edu/

Continuing Studies Program:
http://csp.stanford.edu/

Stanford University Channel on YouTube:
http://www.youtube.com/stanford
Просмотров: 72224
Lecture 1 | Machine Learning (Stanford)
 
68:40
Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng provides an overview of the course in this introductory meeting.

This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include supervised learning, unsupervised learning, learning theory, reinforcement learning and adaptive control. Recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing are also discussed.

Complete Playlist for the Course:
http://www.youtube.com/view_play_list?p=A89DCFA6ADACE599

CS 229 Course Website:
http://www.stanford.edu/class/cs229/

Stanford University:
http://www.stanford.edu/

Stanford University Channel on YouTube:
http://www.youtube.com/stanford
Просмотров: 670580
Unit 4: Probability, Lecture 1 | MIT 6.050J Information and Entropy, Spring 2008
 
112:10
Unit 4: Probability, Lecture 1
Instructors: Paul Penfield, Seth Lloyd

See the complete course at: http://ocw.mit.edu/6-050js08

License: Creative Commons BY-NC-SA
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More courses at http://ocw.mit.edu
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Mathematics and Statistics at Oxford University
 
10:14
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
Просмотров: 6643
Math Made Almost Bearable: Statistics (Made Almost Bearable)
 
7:24
In this episode of Math Made Almost Bearable Frank Kelly explains some of the mathematical AND philosophical techniques employed by statisticians in order to make statistics almost bearable! www.smokeandgold.com
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Mathematical Statistics I - Lecture 6 - UCCS MathOnline
 
79:07
Taught by Dr. Greg Morrow from University of Colorado in Colorado Springs
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Lecture 1: Probability and Counting | Statistics 110
 
46:29
We introduce sample spaces and the naive definition of probability (we'll get to the non-naive definition later). To apply the naive definition, we need to be able to count. So we introduce the multiplication rule, binomial coefficients, and the sampling table (for sampling with/without replacement when order does/doesn't matter).
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Statistiques - Exemple - Maths seconde - Les Bons Profs
 
5:45
Un exemple pour illustrer la notion de statistiques en seconde. Plus de vidéos sur http://www.lesbonsprofs.com/notions-et-exercices/seconde/mathematiques-2e#!statistiques-1
Просмотров: 4646
Statistical Aspects of Data Mining (Stats 202) Day 1
 
50:50
Google Tech Talks
June 26, 2007

ABSTRACT

This is the Google campus version of Stats 202 which is being taught at Stanford this summer. I will follow the material from the Stanford class very closely. That material can be found at www.stats202.com. The main topics are exploring and visualizing data, association analysis, classification, and clustering. The textbook is Introduction to Data Mining by Tan, Steinbach and Kumar. Googlers are welcome to attend any classes which they think might be of interest to them. Credits: Speaker:David Mease
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DAY1/14 Probability & Statistics with Prof David Spiegelhalter
 
68:51
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.
Просмотров: 7028
Statistics 101: Understanding Correlation
 
27:06
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:

http://www.youtube.com/user/BCFoltz/videos?flow=list&view=1&live_view=500&sort=dd
Просмотров: 100976
Dicas - Estatística - Médias, Moda, Mediana, Variância e Desvio-Padrão - ENEM 2013
 
24:03
QUEM CURTE, COMPARTILHA..

Inscreva-se no canal, comente...participe.

http://www.universodalogica.blogspot.com.br/
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Lecture - 13 Function of Two Random Variables
 
62:50
Lecture Series on Probability and Random Variables by Prof. M. Chakraborty, Department of Electronics and Electrical Communication Engineering, I.I.T.,Kharagpur. For more details on NPTEL visit http://nptel.iitm.ac.in
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Introduction to Probability and Statistics 131A. Lecture 1. Probability
 
104:04
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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