Видео похожее на "Mathematical Statistics I - Lecture 1 - UCCS MathOnline", с 1 по 23 из (примерно) 92
ACTSC232week01
 
4:52
University of Waterloo
ACTSC 232: Intro to Actuarial Mathematics
Week 1 video
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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
Просмотров: 668698
What does an actuary do? Learn from the experts.
 
39:17
http://science.nd.edu

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.
Просмотров: 30817
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: 6088
Mathematical Statistics I - Lecture 2 - UCCS MathOnline
 
78:46
Taught by Dr. Greg Morrow from UCCS in Colorado
Просмотров: 1938
Mathematical Statistics I - Lecture 3 - UCCS MathOnline
 
74:58
Taught by Dr. Greg Morrow
Просмотров: 932
Statistics 21 - Lecture 1
 
68:18
Introductory Probability and Statistics for Business
Просмотров: 321006
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
Просмотров: 74941
Self management1 episode 63
 
29:51
BK Jaya
Просмотров: 5685
Linear Models - Lecture 2 - UCCS MathOnline
 
72:30
Linear Models taught by Dr. Greg Morrow from UCCS.
**NOTE: There is no Lecture 1**

Methods and results of linear algebra are developed to formulate and study a fundamental and widely applied area of statistics. Topics include generalized inverses, multivariate normal distribution and the general linear model. Applications focus on model building, design models, and computing methods. The Statistical Analysis System (software) is introduced as a tool for doing computations.
Просмотров: 3377
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
Просмотров: 47029
Lec 1 | MIT 18.03 Differential Equations, Spring 2006
 
48:56
The Geometrical View of y'=f(x,y): Direction Fields, Integral Curves.
View the complete course: http://ocw.mit.edu/18-03S06

License: Creative Commons BY-NC-SA
More information at http://ocw.mit.edu/terms
More courses at http://ocw.mit.edu
Просмотров: 686583
IB Math SL Statistics and Probability Review - Topic 6
 
52:36
An overview of some of the basic concepts and problem types in statistics and probability for the IB Math standard level course. If you want to follow along and do the problems, please download the handout at https://docs.google.com/open?id=0B6hD-LURYkgEYWI4NThkN2QtMDk3OC00YTI4LWI4YTEtMzUxYWRiMzRhM2Yx . This video is neither produced by nor endorsed by the IB.
Просмотров: 34754
Math & Stats - Careers in Science Speakers Series
 
46:55
In celebration of Year of Science, Langara College held a Careers in Science Speakers Series. This one is Mathematics & Statistics specific.

Speakers: David McGowan and Michael Wach
Dave grew up on the mean streets of Winnipeg before moving West to Kelowna where he attended Okanagan University College (now UBC Okanagan), receiving a Bachelors of Science degree in physics and math. Dave is a consulting actuary at Towers Watson, a global management consulting firm, where he consults to large corporations on the financial management of their post-retirement benefits plans. Despite several allegations, his performance enhancing drug use has never been proven.

Michael is an actuary with 10 years of experience in the human resource consulting industry. He is a consultant at Towers Watson where he helps clients manage the financial implications of their benefit plans. Michael has a Bachelor of Science degree in Actuarial Mathematics from the University of Manitoba. Contrary to various news reports, Michael was not responsible for the economic collapse of 2008.
Просмотров: 1395
Mathematical Statistics I - Lecture 4 - UCCS MathOnline
 
81:54
Taught by Dr. Greg Morrow from University of Colorado in Colorado Springs
Просмотров: 725
Lec 1 | MIT 18.06 Linear Algebra, Spring 2005
 
39:49
Lecture 1: The Geometry of Linear Equations.
View the complete course at: http://ocw.mit.edu/18-06S05

License: Creative Commons BY-NC-SA
More information at http://ocw.mit.edu/terms
More courses at http://ocw.mit.edu
Просмотров: 1223349
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: 618620
Some Applications of Mathematics in Finance
 
59:39
Dr Robert Campbell of the Unviersity of St. Andrews, Scotland, gives a talk to PhD students about working as a "quant" in the finance industry. This was filmed 7th November 2008 and is part of a series of seminars supported by the UK's Economics Network. Slides can be downloaded from http://www.economicsnetwork.ac.uk/archive/standrews_phd/campbell_finance.htm

The Economics Network
Speaker: Dr Robert Campbell
Просмотров: 13863
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.
Просмотров: 10627
Mathematical Statistics I - Lecture 5 - UCCS MathOnline
 
74:39
Taught by Dr. Greg Morrow from University of Colorado in Colorado Springs
Просмотров: 464
UCCS Recruitment Video -
 
5:04
At UCCS, our faculty, staff, and students are constantly reaching higher to achieve their goals. It's also a place where students are having fun, making new friends, and experiencing college life to the fullest, because we also understand that the things you do outside of class are an important part of the learning process.
Просмотров: 5606
Statistics and Probability Part 2 (GED Prep Math)
 
10:18
This is the second of three videos concerned with statistics and probability. This is a complimentary video from Dave Pilmer that aligns with GED Prep Plus resource. Did you know you can download this free print resource from the NSSAL site? Search "FREE Math Resources" on YouTube for more information or go to http://gonssal.ca/General-Public/Documents-Resources/ALPLocalResources.shtml
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