TMA4255 Applied Statistics, spring 2016
Tuesday 13.15-15 S2 and Thursday 12.15-14 in S1 (from January 12).
Detailed plan with handouts
NBNB: the reading list is based on the WMMY book. The slides are used in the lectures and can not replace READING the literature on the reading list.
| Date | Part | Chapter | Topic | Handouts | Data | Additional activity/notes | ||
|---|---|---|---|---|---|---|---|---|
| 1 | 12.01.2016 | 1 | Introduction | Introduction - course and topics, use of software | L1.pdf | |||
| 2 | 14.01.2016 | 1 | 1-7, 8.8, 8.3-8.6, 9.1-9.4 | Normal plot, sampling distribution, chi-distribution, estimation | L2.pdf | Presenting data, Population and sample | ||
| 3 | 19.01.2016 | 1 | 8.6, 10.1-10.4 | T-distribution, Hypothesis testing, one sample inference | L3.pdf | LDLds.MTW LDLds.txt | ||
| 4 | 21.01.2016 | 1 | 8.7,9.8,9.9, 9.13,10.5, 10.10 | Two sample inference, F-distribution | L4.pdf | fusiontime.MTW (MINITAB) and fusiontime.txt (R) | FusionTimeData Comparing means | |
| 5 | 26.01.16 | 2 | 11.1-11.4 (11.5) | Simple linear regression | L5.pdf | stiffdens.MTW (MINITAB) and stiffdens.txt (R) | Linear regression | |
| 6 | 28.01.16 | 2 | 11.5-11.8 | Simple linear regression: inference, prediction | L6.pdf | aswan.MTW (MINITAB) and aswan.txt (R) | ||
| 7 | 02.02.2016 | 2 | 11.6, 12.1-12.5 (partly) | The multiple linear regression model - including matrix notation, least squares estimators | L7.pdf | swiss.MTW (MINITAB) and available in R as swiss R script | ||
| 8 | 04.02.16 | 2 | 12.4-12.6, 12.10 | MLR ANOVA, prediction, residuals | L8.pdf | wrongmodelMLR.r (R script to simulate data for the wrong model example) | ||
| 9 | 09.02.16 | 2 | 12.6-7, 12.11 | Model selection, subsets and orthogonality | L9.pdf | acidrain.MTW (MINITAB), acidrain.txt (R), acid.r (R script to analyse acid rain data) | ||
| 10 | 11.02.16 | 2-3 | DOE note p1-7, 12.7 | 2*2 experiments | L10.pdf | Notat: Factorial experiments at two levels | ||
| 11 | 16.02.16 | 3 | Note p 1-7 | Orthonality, Effects, 2in3 | L11.pdf | R script for pilot example | DOE in MINITAB | |
| 12 | 18.02.16 | 3 | p7-13 | 2ink, Variance, inference, replications | L12.pdf | limabeans.MTW R script lima beans | ||
| 13 | 23.02.16 | 3 | Variance, workflow, blocking | L13.pdf | ||||
| 14 | 25.02.16 | 3 | BHH 12.2-12.4 | Fractional factorial designs | L14.pdf | |||
| 15 | 01.03.16 | 4 | 13.1-13.3 | One way ANOVA | L15.pdf | aggregates.MTW R script for aggregates | Proof thm 13.2(pdf) ANOVA (pdf) | |
| 16 | 03.03.16 | 4 | 13.3, 13.4, 13.6 | One way ANOVA: (unequal sample sizes),homogeneity of variances and multiple tests | L16.pdf | |||
| 17 | 08.03.16 | 4 | 13.6, 13.8 | One way ANOVA: multiple tests, block design | L17.pdf | R script Tukey example | ||
| 18 | 10.03.16 | 4 | 13.8, 14.1-14.3 | block designs, Two-way ANOVA | L18.pdf | R script machine example, eysenck.txt, eysenck.MTW | ||
| 19 | 31.03.16 | 5 | 17.1-17.4 | Statistical process control | L19.pdf | metalltjukkleik.MTW, metalltjukkleik.txt | ||
| 20 | 05.04.16 | 5 | Cancelled | |||||
| 20 | 07.05.16 | 6 | 10.11-10.12 | Goodness of fit, independence | L20.pdf | Test of association | ||
| 21 | 1.04.16 | 6 | 10.12-10.13 | Chisquare tests, Simpsons paradox | L21.pdf | eyehaircolor.MTW bloodtype.MTW Part6Ex.r | ||
| 22 | 14.04.16 | 7 | 4.3, 16.1 | Taylor approximation to E and Var, Sign-test | L22.pdf | |||
| 23 | 19.04.16 | 7 | 16.2-16.3 | Nonparametrics: Wilcoxon signed-rank and rank-sum | L23.pdf | |||
| 24 | 21.04.16 | REP | Parts 1, 5, 6, 7 | One and two population inference | L24.pdf | |||
| 25 | 26.04.15 | REP | Parts 2, 3, 4 | Model mean response as a linear function of covariates | L25.pdf | |||
Tentative lecture plan
| Weeek | Chapter | Topic | |
|---|---|---|---|
| 2-3 | 8-10 | Introduction. One and two sample inference based on normal data | 1+2 |
| 4-6 | 11-12 | Linear regression | 3+4+5 |
| 6-8 | Design of experiments | 6+7 | |
| 9-10 | 13-14 | Analysis of variance (Work with project) | 8 |
| 11 | No lectures, only supervision of project and exercises- since many students are away on excursions. | ||
| 12 | Easter break | ||
| 13-14 | 17 | Statistical process control (no lecture 29/3). Hand in project at the end of the week. | 9 |
| 14-15 | 10 | Statistical analysis of contingency tables | 9 |
| 15-16 | 16,4 | Nonparametric statistics | 10 |
| 16-17 | Summing up and concluding remarks, exam preparation. Last day of lecture may be Tuesday April 26. |