The boldfaced English podcasts were recorded in Spring 2017. The remaining podcasts are from previous versions of the course, and they may use other slides than those which are currently available.

Please note that only the current version of the Agenda counts this year. Please send an email to pbac@dtu.dk if you find things which should be changed in the table below.

Week Podcast titles and links
1 Welcome and Practical course information (5 min)
1 The first Kahoot (3 min)
1 Why statistics - James Lind, Broad street and Google/IBM/Novo. (11min)
1 Statistics and engineers. (14 min)
1 Summary statistics: mean and median (10 min)
1 Summary statistics: Standard deviation and variance (13 min)
1 Summary statistics: quantiles(percentiles) (8 min)
1 Summary statistics: Sample covariance and correlation (12 min)
1 R introduction - get started (incl. Rstudio)(incl. the boxplot) (12min)
1 Brief summary of Week 1 (from Spring 2015) (5 min)
1 EXTRA MATH: The derivation of the variance formula (7 min)**
2 Discrete Random variables and density functions(15 min)
2 The Distribution function (5 min)
2 Binomial distribution, definition and example (26 min)
2 Hypergeometrical distribution (with example)(18 min)
2 Poisson distribution (with example)(13 min)
2 Distributions in R (3 min)
2 Mean and variance for discrete distributions (12 min)
2 Brief summary of Week 2 (from Spring 2015) (9 min)
2 EXTRA MATH: The mean of a binomial distribution.(8 min)
2 EXTRA MATH: The derivation of the variance formula(5 min)
2 EXTRA MATH: The Variance of the binomial distribution(7 min)
2 EXTRA MATH: The derivation of the poisson distribution (12 min)
2 EXTRA MATH: The mean and variance of the Poisson distribution(6 min)
2 OPTIONAL: Intro (Repetition of Discrete distributions, Eel story, Mobile phone and cancer story) (18 min)
3 Continuous distributions, density, distribution (21 min)
3 Continuous distributions, mean, variance (12 min)
3 Uniform distribution and example (13 min)
3 Normal Distribution, definition, Example (20 min)
3 Standard Normal Distribution (9 min)
3 Log-Normal distribution (3 min)
3 The Exponential Distribution With Example (11 min)
3 Mean And Variance Rules, (16 min)
3 OPTIONAL Software R, Example: Geniuses in Denmark! (6 min)
3 OPTIONAL from previous: Example With Ship Passengers (9 min)
3 OPTIONAL from previous: Example With Stones In Sand - What Is Hypothesis Test? (10 min)
3 From previous: Brief summary of Week 3 (10 min)
3 EXTRA MATH: Mean and variance for the Uniform distribution (5 min)
3 EXTRA MATH: Mean and variance for the Normal distribution (9 min)
3 EXTRA MATH: Mean and variance for the Exponential distribution (8.5 min)
4 Intro example with confidence intervals (10 min)
4 Quantifying the error in estimating the mean: The (sampling) distribution of the sample mean (and the t-distribution) (23 min)
4 The (one-sample) confidence interval for the mean, with example (14 min)
4 The language of statistics and the formal framework (13 min)
4 The Central Limit Theorem (CLT) - and illustration in R by the uniform example (12 min)
4 A formal interpretation of the confidence interval (10 min)
4 Confidence interval for variance and standard deviation (13 min)
4 From Spring 2015: Brief summary of Week 4 (8.5 min)
4 EXTRA MATH: Introduction to likelihood theory (14 minutes)
4 EXTRA MATH: Maximum likelihood estimation for the binomial model (8 minutes)
4 EXTRA MATH: Maximum likelihood estimation for the poisson model (3.5 minutes)
4 EXTRA MATH: Maximum likelihood estimation for the normal model (10 minutes)
5 Hypothesis Test: A motivating example (sleeping medicin) (16 min)
5 Inital Kahooting of the week (4 min)
5 Hypothesis test: One-sample t-test and the p-value (21 min)
5 Hypothesis test: Critical value and confidence interval (8 min)
5 Hypothesis test: the formal procedures and errors (16 min)
5 Checking the normality assumption - the QQ-plot (18 min)
5 The Wally plot version of the QQ-plot (4 min)
5 Transforming towards normality (12 min)
5 From 2015: Brief summary of Week 5 (4.5 min)
5 EXTRA MATH: Sums of random variables and moment generating functions(15 min)
5 EXTRA MATH: Non-linear transformations of distributions (16 min)
5 EXTRA MATH: Sampling distribution of the sample variance (14 min)
5 EXTRA MATH: Derivation of the t-distribution (12 min)
6 2-sample data - motivating Example(7 min)
6 The first Kahoot question (p-value) (4 min)
6 Hypothesis Test and p-values (Repetition) (8 min)
6 Two-sample t-test and p-value (13 min)
6 The confidence interval for the difference (10 min)
6 Overlapping confidence intervals? (8 min)
6 The Paired Setup and paired t-test (17 min)
6 Checking The Normality Assumptions (3 min)
6 Planning for wanted precision (7 min)
6 Planning: Power and sample size (14 min)
6 The Pooled t-Test (4 min)
6 Brief summary of Week 6 (Spring 2015)(7 min)
6 From 2013: Sample Size Determination (5.5 min) the notation is a bit different in this old version
6 EXTRA MATH: Power and sample size formula (one sample case) (16 min)
7 Introduction To Simulation (17 min)
7 Simulation Example 1: Area Of Plates, A=XY (11 min)
7 Non-linear error propagation (18 min)
7 Bootstrapping - an intro (4 min)
7 Confidence interval forany one-sample feature by parametric bootstrap. Example: The mean of the exponential distribution.(20 min)
7 Two-sample Confidence intervals by parametric bootstrapping. Comparing means or any other feature. Example: Comparing two exponential means. (6 min)
7 Confidence interval for a one-sample mean (or any one-sample feature) by non-parametric bootstrap with example. (10 min)
7 Two-sample Confidence intervals by non-parametric bootstrapping. Comparing means or any other feature. Example 1: Difference of means for non-normal data. Example 2: Differences of medians for non-normal data.(8 min)
7 Brief summary of Week 7(Spring 2015 )(7 min)
7 EXTRA MATH:The uniform distribution can give all other distributions, proof(4 min)
7 EXTRA MATH: Other bootstrap confidence interval principles, and how to use simulation to investigate which methods are better (18 min)
8 Motivating example: Height-weight (11 min)
8 The Linear regression model (10 min)
8 The Least Squares Method (12 min)
8 Statistics and linear regression?? (15 min)
8 Hypothesis tests and confidence interval for the intercept and slope (15 min)
8 Confidence interval and prediction interval for the line (8 min)
8 The R-output (3 min)
8 Correlation (and regression) (10 min)
8 Residual analysis - model control (7 min)
8 Brief summary of Week 8 (Spring 2015)(14 min)
8 EXTRA MATH: The basic handcomputational formulae (4.5 min)
8 EXTRA MATH: Finding the least squares estimates(10 min)
8 EXTRA MATH: The LS estimates are also Maximum Likelihood Estimates (6 min)
8 EXTRA MATH: Extended regression modelling: Multiple input, non-linear relations and categorical/non-normal observations.(15 mintutes)
9 MLR, Intro. Recap of simple linear regression. Airquality example.(21 min)
9 MLR, Basics. The Multiple Linear Regression model and least squares fit. (15 min)
9 MLR, Model selection. Forward and backward model selection.(12 min)
9 MLR, Model validation. Residual plotting for normality check, variance homogeneity and non-linearity.(10 min)
9 MLR, Non-linearity. How to check for and deal with non-linear effects within the linear model: E.g. linear regression with quadratic input terms.(8 min)
9 MLR, Confidence and prediction Intervals. How to get Confidence intervals and prediction intervals in R.(7 min)
9 MLR, Colinearity. The consequence of x-inputs being correlated to each other.(16 min)
9 The overall MLR method - How to approach and proceed in an analysis (7 min)
9 Brief summary of Week 9 (Spring 2015) (7 min)
- Note that in the recordings this is Week 12, but now moved to Week 10
10 Intro To Proportions (Polls, risk of malformation of newborns, etc)(16 min)
10 Confidence Interval For One Proportion(15 min)
10 Sample Size Determination(9 min)
10 Hypothesis Test For One Proportion, Including Example(8 min)
10 Hypothesis Test and CI for Two Proportions (9 min)
10 The Chi-Square test: Hypothesis Test For Two And Several Proportions (18 min)
10 Analysis Of Contingency Tables (9 min)
10 Proportions and Chi-Square tests in R (14 min)
- Note that in the recordings this is Week 10, but now moved to Week 11
11 Oneway Anova, Intro Example (15 min)
11 Oneway Anova, Model And Hypothesis (6 min)
11 Computation: Decomposition Of Variance, Anova Table (19 min)
11 F-Test, F-Distribution (10 min)
11 Oneway Anova, relation to 2-sample t-test. (9 min)
11 Oneway Anova, Post Hoc Comparison, confidence intervals and hypothesis testing. Bonferroni correction. (17 min)
11 Model control in one-way ANOVA. Residual investigation (4 min)
11 A worked through one-way ANOVA example using R (17 min)
11 Brief summary of Week 11 (Spring 2015) (10 min)
- Note that in the recordings this is Week 11, but now moved to Week 12
12 Lec 12 pre-Intro: Red cards to colored soccer players?? (6 min)
12 Two-way Anova, Intro Examples (TV data) (19 min)
12 Two-way Anova, Model and estimates (7 min)
12 Two-way Anova: Computation: Decomposition Of Variance (7 min)
12 F-Test, F-Distribution, ANOVA table (13 min)
12 Two-way Anova, Post Hoc Analysis (17 min)
12 Model control in two-way ANOVA. Residual investigation. (5 min)
12 A worked through example of two-way ANOVA (25 min)
12 Brief summary of Week 12 (Spring 2015) (9 min)
12 EXTRA MATH: Why is the F-statistic F-distributed? (8.5 min)
12 EXTRA MATH: Parameter solutions in ANOVA models(12 min)
13 Course summary, Week 1-3 Descriptive statistics and probability.(16 min)
13 Course summary, Week 4. One sample confidence intervals.(14 min)
13 Course summary, Week 5. One sample hypothesis testing, p-values. (5 min)
13 Course summary, Week 6. Two sample statistics: CIs and hypothesis test. (2 min)
13 Course summary, Week 7. Simulation and simulation based statistics, bootstrapping. (5 min)
13 Course summary, Week 8. Simple linear regression. (6 min)
13 Course summary, Week 9. Multiple linear regression (MLR). (7 min)
13 Course summary, Week 10. Statistics for proportions and frequency tables. (5 min)
13 Course summary, Week 11-12. One- and twoway Analysis of Variance, ANOVA. (6 min)
13 Course “situations” - A single slide “tree overview” of the course (6 min)
13 Perspective - other statistics courses (6 min)
13 Exam discussion - and a few examples (14 min)