Course Details 1. Introduction to Probabilities Course Outline Initial meeting: 12-jan (37:44) Instructor: Patrick Boily, uOttawa Communications with the Instructor must be conducted through Slack. The Slack invite link is available in the “Announcements” section on Brigthspace. Exercise Sessions Schedule:     Tuesdays, 10:00-11:20, Jan 12 – Apr 13     Thursdays, 08:30-09:50, Jan 14 – Apr 08     No session on Feb 16 and Feb 18 Exercise Sessions Zoom link Important Dates:     Feb 4: assignment 1 due     Feb 25: midterm examination     Mar 16: assignment 2 due     Apr 8: assignment 3 due Note: the assignments must be uploaded as a single PDF document to Brightspace. Mobius Exam Instructions (VERY IMPORTANT!!! you must purchase a license [you may already have one] before the midterm exam. Note that the price of the license is much lower than that of the non-mandatory textbook.) Further instructions have been posted on Slack on Feb 9. Slide Deck Video Lectures: (2:52:22)     12-Jan: 1.1 Sample Spaces and Events (14:29)     12-Jan: 1.2 Counting Techniques (12:15)     12-Jan: 1.3 Ordered Samples (16:19)     14-Jan: 1.4 Unordered Samples (11:27)     14-Jan: 1.5 Probability of an Event (29:25)     19-Jan: 1.6 Conditional Prob. & Indep. Events (52:44)     21-Jan: 1.7 Bayes’ Theorem (35:43) Exercises: Q1-Q30, Q61, Q71-Q72 Important Documents:     Practice Set (in-class exercises)     Additional Questions with Answers     Midterm Exam – 2020 (provided as an example only)     Practice Final Exam – 2015 (provided as an example only)     T Table     Standard Normal Table Assignments and Midterm:     Assignment 1 (due 04-Feb, 3:00PM EST) – Solutions     Assignment 2 (due 16-Mar, 3:00PM EST) – Solutions     Assignment 3 (due 08-Apr, 3:00PM EST) – Solutions Some of the video lectures (from 2-2 onwards) are available without a soundtrack here. 2. Discrete Random Variables 3. Continuous Random Variables Video Lectures: (1:33:32)     26-Jan: 2.1 Random Variables and Distributions (27:36)     26-Jan: 2.2 Expectation of a Discrete R.V. (23:28)     28-Jan: 2.3 Binomial Distributions (19:40)     28-Jan: 2.4 Geometric Distributions (03:20)     28-Jan: 2.5 Negative Binomial Distributions (05:25)     02-Feb: 2.6 Poisson Distributions (14:03) Exercises: Q31-Q46, Q54-Q57, Q59, Q64, Q69-Q70, Q74, Q77-Q78 Video Lectures: (2:10:37)     02-Feb: 3.1 Probability Density Functions (36:23)     04-Feb: 3.2 Expectation of a Continuous R.V. (08:05)     04-Feb: 3.3 Normal Distributions (25:48)     09-Feb: 3.4 Exponential Distributions (11:39)     09-Feb: 3.5 Gamma Distributions (08:44)     11-Feb: 3.6 Joint Distributions (32:44)     11-Feb: 3.7 Normal Approx. of the Binomial Dist. (07:14) Exercises: Q47-Q53, Q58, Q60, Q62-Q63, Q65-Q67, Q73, Q75 4. Descriptive Statistics and Sampling Distributions 5. Point and Interval Estimation Video Lectures: (2:24:42)     02-Mar: 4.1 Data Descriptions (38:43)     04-Mar: 4.2 Visual Summaries (17:57)     09-Mar: 4.3 Sampling Distributions (24:32)     11-Mar: 4.4 Central Limit Theorem (34:49)     16-Mar: 4.5 Sampling Distributions (Reprise) (28:41) Exercises: Q76, Q79-Q92, Q125, Q128, Q136 Video Lectures: (1:17:26)     18-Mar 5.1 Statistical Inference (15:29)     18-Mar 5.2 C.I. for the Mean with Known Variance (32:48)     23-Mar 5.3 Choice of Sample Size (08:53)     23-Mar 5.4 C.I. for the Mean with Unknown Variance (11:46)     23-Mar 5.5 C.I. for a Proportion (08:30) Exercises: Q93-Q101, Q126-Q127 6. Hypothesis Testing 7. Linear Regression and Correlation Video Lectures: (2:09:07)     25-Mar: 6.0 Claims and Suspicions (20:15)     25-Mar: 6.1 Hypothesis Testing (07:54)     25-Mar: 6.2 Null and Alternative Hypotheses (05:58)     30-Mar: 6.3 Test Statistics and Critical Regions (13:29)     30-Mar: 6.4 Test for a Mean with Known Variance (34:13)     01-Apr: 6.5 Test for a Mean with Unknown Variance (06:44)     01-Apr: 6.6 Test for a Proportion (03:38)     01-Apr: 6.7 Paired Two-Sample Test (11:56)     01-Apr: 6.8 Unpaired Two-Sample Test (20:44)     01-Apr: 6.9 Difference of Two Proportions (04:16) Exercises: Q102-Q124, Q129-Q130 Video Lectures: (1:28:44)     06-Apr: 7.0 Motivating Example (03:17)     06-Apr: 7.1 Coefficient of Correlation (09:19)     06-Apr: 7.2 Simple Linear Regression (35:49)     08-Apr: 7.3 Hypothesis Testing for Linear Regression (11:13)     08-Apr: 7.4 C.I. and P.I. for Linear Regression (17:06)     08-Apr: 7.5 Analysis of Variance (08:02)     08-Apr: 7.6 Coefficient of Determination (03:58) Exercises: Q131-Q132, Q137-Q148 Optional Material Statistical Process Monitoring Slide Deck Video Lectures: (0:43:49)     8.1 Motivation (02:56)     8.2 Time Series (08:23)     8.3 Control Charts (32:30) Exercises: Q133-135, Q149-Q150 Data Analysis in R Videos: (3:02:26)     R.1 Basics of R, part 1 (29:50)     R.2 Basics of R, part 2 (25:01)     R.3 Random Variables and Distributions (18:08)     R.4 Descriptive Statistics (05:19)     R.5 Graphics with ggplot2, part 1 (19:32)     R.6 Graphics with ggplot2, part 2 (27:03)     R.7 Central Limit Theorem (12:02)     R.8 Confidence Intervals (14:10)     R.9 Hypothesis Testing (16:13)     R.10 Simple Linear Regression (15:08)
/ Posted on /