1. 9/15/2015330 Lecture 11 STATS 330: Lecture 1

2. 9/15/2015330 Lecture 12 Today’s agenda: Introductory Comments: –Housekeeping –Computer details –Plan of the course Statistical Modelling: an overview –Our Analysis strategy –Goals for the course Role of Graphics Data cleaning

3. 9/15/2015330 Lecture 13 Housekeeping Contact details…. Plus much else on course web page www.stat.auckland.ac.nz/~lee/330/ Or via Cecil

4. 9/15/2015330 Lecture 14

5. 9/15/2015330 Lecture 15 Assignments There will be five assignments (20% of total grade) The due dates are listed in the course summary Assignment 1 is due on August 2. No paper will be issued in class – download assignments and data from the Web or Cecil

6. 9/15/2015330 Lecture 16 Tutorials These will cover computing details Held in basement tutorial lab, 303S Extension (Rm 303S-B75) –Tutorial 1: 11-12 Wed –Tutorial 2: 10-11 Fri –Tutorial 3: 2-3 Fri Start second week of semester

7. 9/15/2015330 Lecture 17 Computer details All analyses will be done using R I require homework to be typed, use Word, cut and paste R text and graphics into Word Use home/laptop computer or basement lab (303S) See web page for info on downloading R330 package URL is www.stat.auckland.ac.nz/~lee/330

8. 9/15/2015330 Lecture 18 Course Plan There will be 33 lectures, divided into chapters as follows Chapter 1: Introduction (1 lecture, this one!) Chapter 2: Graphics (3 lectures) Chapter 3: Multiple Regression Model (12 lectures) Chapter 4: Factors (4 lectures) Chapter 5: Models for categorical and count responses (12 lectures) Revision (1 lecture)

9. 9/15/2015330 Lecture 19 Course book This covers most of the material we will discuss in the lectures Has 5 chapters corresponding to the division on the previous slide On-line version available on the course web site Paper copies available from the Statistics Department, Commerce A

10. 9/15/2015330 Lecture 110 Overview of Statistical Modelling Statistical models summarize relationships between variables Regression models focus on one variable (the response) and how its distribution can be modelled by one or more explanatory variables

11. 9/15/2015330 Lecture 111 Example: Response is BPD BPD: Bi-Parietal Diameter For an unborn baby, depends on several factors, including Gestational Age.

12. Ultrasound image 9/15/2015330 Lecture 112

13. 9/15/2015330 Lecture 113 Points to take into account: Not all babies of the same age have the same BPD. In general, the older the baby, the bigger the BPD.

14. 9/15/2015330 Lecture 114 Model relating BPD and GA Gestational Age BPD Mean BPD for 30 weeks =  +  30 Mean BPD for 40 weeks = a +  40 Mean BPD for GA weeks = a +  GA Line is BPD =a + b GA Each bell curve has sd 10 mm 3040

15. 9/15/2015330 Lecture 115 Regression model features Model specifies the distribution of the response Also says how the distribution of the response is affected by the covariates (explanatory variables) In this case the covariate GA determines the mean response: mean BPD = a + b GA ie a straight-line relationship.

16. 9/15/2015330 Lecture 116 In general…. Response has a distribution depending on (conditional on) the explanatory variables Mean of the distribution given by some function of the explanatory variables Need to –describe the function –describe the variability Balance accuracy with simplicity.

17. 9/15/2015330 Lecture 117 Our Analysis strategy: Explore the data using graphics and summary statistics Construct a useful model (trial and error) Use the model to gain knowledge about the system under study (How big should a 40 week baby’s BPD be? ) Communicate findings (be able to write a report!!)

18. 9/15/2015330 Lecture 118 Goals for 330 Get more practice in exploring data Expand knowledge of regression Get better at fitting models Improve your diagnostic skills (how to recognize when a model doesn’t describe the data properly) Improve your interpretation and communication skills, in a more flexible way.

19. 9/15/2015330 Lecture 119 Role of Graphics Data Cleaning: Are there gross errors, outliers, special codings (eg 999 for missing), missing data, absolute rubbish in the data? Exploratory analysis: what sort of model might be appropriate? Diagnostics: having tentatively selected a model, is it any good? (Residual plots, etc)

20. 9/15/2015330 Lecture 120 Exploratory analysis for BPD data Outlier Linear relationship (straight line) appropriate?

21. 9/15/2015330 Lecture 121 Diagnostic Graphics: residuals versus fitted values Smoothing enhances interpretation. Conclusion? Outlier stands out

22. 9/15/2015330 Lecture 122 Data Cleaning All real-life data sets are likely to contain errors These will usually be revealed by suitable plots, such as the one on the previous slide Before starting any analysis, data should always be carefully checked To encourage this practice, I will occasionally introduce errors into the assignment data supplied on the web.

23. 9/15/2015330 Lecture 123 Data Cleaning (2) It is your responsibility to check all data supplied on the web, against the assignment sheets. Failure to so will cost marks. YOU HAVE BEEN WARNED!!!