Probability
Below are links to my lecture videos on YouTube.
Event Composition Method Examples 1
Event Composition Method Examples 2
Binomial as sum of Indep. Bernoullis
Moment Gen. Funct. for Binomials
Exam 1
Covers all videos prior to this, and essentially chapters 2 and 3 of the text.
Expectation and Variance for Continuous RVs
Expectation and Variance of Uniform
Moment Generating Function for Uniform
z-scores or standardized variables
Standard Normals and Percentiles
Exponential pdf cdf and Memoryless Property
Gamma Mean, Variance; Chi Squared
Expectations of Functions of Random Variables
General Real-Valued RVs - Example Part 1
General Real-Valued RVs - Example Part 2
General Real-Valued RVs - Example Part 3
Multivariate Discrete Example Part 1
Multivariate Discrete Example Part 2
Multivariate Continuous Part 1
Multivariate Continuous Part 2
Multivariate Continuous Example
Independent Random Variables 1
Independent Random Variables 2
Independent Random Variables 3
Expectation of Functions of RVs 1
Expectation of Functions of RVs 2
Expectation of Functions of RVs 3
E and Var of Linear Functions of RVs 1
E and Var of Linear Functions of RVs 2
Exam 2
Covers all videos since Exam 1, and essentially chapters 4 and 5 of the text.
Here is a review guide.
Method of Distribution Functions 1
Method of Distribution Functions 2
Method of Distribution Functions 3
Moment Generating Function Methods 1
Moment Generating Function Methods 2
Moment Generating Function Methods 3
Moment Generating Function Methods 4
Moment Generating Function Methods 5
Moment Generating Function Methods 6
Sums of Normals and Sample Variance 1
Sums of Normals and Sample Variance 2
Sums of Normals and Sample Variance 3
Sums of Normals and Sample Variance 4
Sums of Normals and Sample Variance 5
Exam 3
Covers all videos since Exam 2, and essentially chapters 6 and 7 of the text. Here is a review guide.
Quiz Dates
Aug. 25- Section 2.3: Set Notation
Aug. 27 - Sections 2.4 & 2.5: Basic Probability
Aug. 29 - Section 2.6: Counting
Sept. 1 - No Class! Labor Day Holiday
Sept. 3 - Section 2.7: Conditional Prob. & Independence
Sept. 5 - Section 2.10: Total Probability and Bayes's Rule
Sept. 8 - Expectation and Variance
Sept. 10 - Binomial Random Variables
Sept. 12 - Geometric Random Variables
Sept. 15 - Negative Binomial and Hypergeometric
Sept. 17 - Poisson
Sept. 19 - Moment Generating Functions and Chebyshev's Ineq.
Sept. 22 - Continuous Variables
Sept. 24 - Expectation for Continuous Variables
Sept. 26 - Exam I (covers Chapters 3 and 4)
Sept. 29 - Uniform Random Variables
Oct. 1 - Normal Random Variables
Oct. 3 - Gamma & Exponential
Oct. 6 - Beta
Oct. 8 - Moment Generating Functs. & Chebyshev's Ineq.
Oct. 10 - Mixed Probability Distributions
Oct. 13 - FALL BREAK!
Oct. 15 - Multivariate Distributions 1
Oct. 17 - Multivariate Distributions 2
Oct. 20 - Marginal and Conditional Distributions
Oct. 22 - Independent Random Variables
Oct. 24 - Expectation of Functions of RVs
Oct. 27 - Covariance
Oct. 29 - Expectation and Variance of Linear Funcs. of RVs
Oct. 31 - Multinomial
Nov. 3 - Conditional Expectation
Nov. 5 - Functions of RVs: Method of Dist. Functions
Nov. 7 - Exam II
Nov. 10 - Method of Transformations
Nov. 12 - Moment Generating Function Methods 1
Nov. 14 - Moment Generating Function Methods 2
Nov. 17 - Order Statistics
Nov. 19 - Behavior of Sums of Normals
Nov. 21 - Behavior of Sample Variance
Nov. 24 - Central Limit Theorem
Nov. 26 - Thanksgiving Break!
Nov. 28 - Thanksgiving Break!
Dec. 1 - Exam III
Dec. 3 - Final Exam Review and Last Day of Classes
Old Exams
Homework & Due Dates
Summer 2019 class: you should plan to take your exams according to this rough schedule:
Exam I: around Friday, June 21.
Covers sections 2.3 through 3.11 (scroll down and look to the left... you'll see where the Exam 1 videos end and the Exam II videos begin)
Exam II: around Friday, July 12.
Covers sections 4.2 through 5.11.
Exam III: around Friday, August 2.
Covers sections 6.3 through 7.3.
Final Exam: around Friday, August 9. Cumulative.
Email your professor to arrange times and dates for your exams.
For Summer 2019 class: please ignore the homework due dates below. However, these are the homework problems you should work. Homework will not be collected, but it is strongly suggested you do these problems.
Aug. 27- Section 2.3, #s 4, 5, 6, 7
Aug. 29 - Sections 2.4 and 2.5, #s 11, 14, 15, 18, 21, 22, 23, 27, 29, 33
Sept. 3 - Section 2.6, #s 35, 36, 41, 43, 46, 47, 51, 53, 57, 58, 68, 69
Sept. 5 - Section 2.7, #s 71, 75, 77, 81, 83. Section 2.8, #s 84, 86, 89, 97, 99, 104
Sept. 8- Section 2.9, #s 114, 115, 119, 120, 121, Section 2.10, #s 128, 132, 135, 137
Sept. 10 - Section 3.2, #s 2, 4, 6, 10, Section 3.3, #s 19, 23, 27, 29, 32, 33
Sept. 12 - Section 3.4, #s 40, 44, 48, 51, 56, 57, 65
Sept. 15 - Section 3.5, #s 66, 67, 70, 72, 77, 80, 85
Sept. 17 - Section 3.6, #s 90, 91, 93, 94, 97, Section 3.7, #s 102, 105, 110
Sept. 19 - Section 3.8, #s 122, 130, 134, 138, 141,
Sept. 22 - Section 3.9, #s 147, 148, 150, 151, 153, 159, 160, Section 3.11, #s 167, 168
Sept. 24 - Exam I Review - Also, Section 4.2, #s 8, 9, 15, 17, Section 4.3, #s 21, 25, 26, 32, 34, 35
Sept. 26 - Exam I (covers Chapters 2 and 3)
Sept. 29 - No homework due!
Oct. 1 - Section 4.4, #s 39, 41, 42, 43, 45, 50,
Oct. 3 - Section 4.5, #s 58, 59, 61, 62, 71, 75
Oct. 6 - Section 4.6, #s 81, 82, 89, 92, 94, 95, 104, 109, 112
and the following:
1. The times between arrivals to the YMCA are independent exponential random variables with mean 3 minutes.
(a) What is the probability that the time between the 4th and 5th customers exceeds 5 minutes?
(b) Given the 4th customer arrived 10 minutes ago, what is the probability that the time from now until the next arrival exceeds 5 minutes?
(c) Given the 4th customer arrived 10 minutes ago, what is the expected time from now until the next arrival?
(d) The 6th customer just arrived. What is the distribution of time until the 11th customer arrives, and what is the expected amount of time until this happens?
2. Prove that the geometric random variable has the memoryless property (in fact, it's the only discrete rv. that does, but you don't have to prove uniqueness).
Oct. 8 - Section 4.7, #s 124, 125, 128, 130
Oct. 10 - Section 3.9, #158, Section 4. 9, # 137, 139, 140, 141, 143, 144, 145, Section 4.10, #s 146, 149
Oct. 13 - No classes. Fall break!
Oct. 15 - Section 4.11, #s 155, 156, 157, 159
Oct. 17 - HA! No homework due.
Oct. 20 - Section 5.2, #s 7, 9, 11, 15, 17
Oct. 22 - Section 5.3, #s 19, 23, 25, 34, 36
Oct. 24 - Section 5.4, #s 43, 45, 48, 57, 61, 64, 70, 71
Oct. 27 - Sections 5.5 and 5.6, #s 75, 77, 79, 81
Oct. 29 - Section 5.7, #s 89, 91, 93, 94, 96, 98
Oct. 31 - Section 5.8, #s 103, 105, 107, 108, 110
Nov. 3 - Section 5.9, #s 119, 123, 124, 126
Nov. 5 - Exam II Review. Also, Section 5.11, #s 133, 136, 139, 140, 142
Nov. 7 - Exam II (covers Chapters 4 and 5).
Nov. 10 - Section 6.3, #s 1, 3, 7, 8, 14, 22
Nov. 12 - Section 6.4, #s 23, 28, 29, 30
Nov. 14 - No Homework Due! But the next one is long!
Nov. 17 - Section 6.5, #s 37, 38, 40, 41, 42, 43, 49, 52, 54, 57, and the following: Suppose X is distributed geometric(p). How does the random variable pX/lambda behave as p goes to 0? Here, lambda is a constant. Hint: Use moment generating functions.
Nov. 19 - Section 6.7, #s 72, 74, 76, 81
Nov. 21 - No homework due!
Nov. 24 - Exam III Review. Section 7.2, #s 11, 12, 14, 15, 20, 21, 33, 34, 35
Nov. 26 - No classes- Thanksgiving break.
Nov. 28 - No classes - Thanksgiving break.
Dec. 1 - Exam III (covers Chapters 6 and 7). Also, Section 7.3, #s 43, 44, 46, 47, 52, and the following:
The average and standard deviation of railroad cars and their contents are 8,000 lbs and 1,000 lbs, respectively. Fifty of these cars are to be loaded onto a barge. What is the approximate chance that the total weight exceeds the load capacity of the barge of 425,000 lbs?
Dec. 3 - Final Exam Review. Final exam is cumulative.
Dec. 5 - Final Exam. 8:00 am - 10:00 am.