Licence:
Course Description:
This is a math course aimed at students with life science majors covering elementary probability, probability distributions, random variables, and limit theorems.
Lectures:
Lecture 1 - Introduction: Probability and Counting
Outcomes, sample, space, events, and probability functions.
Lecture 2 - Probability Functions
Probability functions, permuations.
Lecture 3 - Permutations
Permutations.
Lecture 4 - Probability Functions (continued)
Probability functions.
Lecture 5 - Conditional Probability
Conditional probability.
Lecture 6 - Conditional Probability (continued)
Conditional probability.
Lecture 7 - Independent Events
independence of 3 or more events.
Lecture 8 - Random Variables
Random variables.
Lecture 9 - Expected Values
Random variables (continued), expected value, standard deviations.
Lecture 10 - Binomial Distributions
Standard distribution, binomial distribution, two random variables.
Lecture 11 - Midterm Review
Midterm review.
Lecture 12 - Multinomial Distributions
Multivariable distribution, binomial distribution, Bernoulli trials, geometric distributions.
Lecture 13 - Geometric Distributions
Geometric distributions.
Lecture 14 - Poisson Distributions
Poisson distribution, continuous trials, poisson processes.
Lecture 15 - Poisson Distributions (continued)
Poisson distribution, poisson processes.
Lecture 16 - Density Function
Density funtion, continuous random variables, uniform distribution.
Lecture 17 - Exponential Distributions
Continuous random variables, exponential distribution.
Lecture 18 - Normal Distributions
Normal distribution.
Lecture 19 - Normal Distributions (continued)
Standard normal distribution, cumulative distribution function.
Lecture 20 - Standard Normal Distributions
Nomal distribution continued.
Lecture 21 - Central Limit Theorem
Central limit theorem, normal distribution applications.
Lecture 22 - Hitstogram Correction
Normal approximation, histogram correction.
Lecture 23 - Midterm Review 2
Midterm review 2.
Lecture 24 - Analyzing Data in Probability
Analyzing data in probability, samples, incomplete data, means.
Lecture 25 - Analyzing Data in Probability (continued)
Analyzing data in probability, samples, incomplete data, means.
Lecture 26 - Limit Theorems
Limit theorems, Markovs inequality theorem, Chebyshevs inequality theorem, Law of large numbers.
Lecture 27 - Limit Theorems (continued)
Limit theorems, Markovs inequality theorem, Chebyshevs inequality theorem, Law of large numbers.
Lecture 28 - Course Review
Course Review
Source: http://academicearth.org/courses/math-and-proability-for-life-sciences
