Tutor/(or Student) Guide to: Tutor-led Tutorials

Transcription

1 Tutor/(or Student) Guide to: Tutor-led Tutorials (Module Code: Stat10050) Tutor Name: Module Co-ordinator: Dr. Patrick Murphy

2 Description of Tutorials Introduction to Statistical Modelling Tutorials: Aim of the tutorials is for the students to get solutions to their homework and ask questions related to their homework; indeed they are like mini lectures. The solutions are worked through by the tutor on the board. The students get extra questions to do in the tutorial INDIVIDUALLY (not all tutors correct the homework corresponding the tutorials they give). The exam-like materials are covered in some tutorials (towards the end of the semester). The tutorials are set aside to help the students, especially those struggling with the course materials, to get more help. The tutors should be ready to answer students questions. In fact, the tutors should be a link between the lecturers and the students; which means that the students are free to approach the tutors (their fellow student), and the tutors are to communicate with the lecturers the feelings of the students. Module Descriptor Lecture Topic Topic Code 1, A Introduction and Motivation,3,4 B Statistical Concepts: Samples and Populations, Variability, Seven Critical Components of a Study, Bias in Questions and Bias in Sampling, Types of Variables and Data 4,5,6 C Collecting Data 1: Sampling and surveys 5,6 D Collecting Data : Experiments and Observational Studies Learning Goals The students will know the following: the difference between sample and population; spread in data set; how to word questions correctly in questionnaire; how to critically analyse a study conducted by other people, how to avoid mistakes in sampling; the different types of variables and data. The students will discover the different types of sampling. They will also know how to carry out surveys in a correct manner, and identify when there is misleading information from surveys in the newspapers. The students should identify the difference between observational studies and experimental studies, and the various different methods used in each of these.

3 7,8 E Summarising Data 1 Numerical Summary Measures Mean, Median, Mode, Trimmed Means, Quartiles, Percentiles, Range, Variance, Standard Deviation, Inter-quartile Range 7,8,9 F Summarising Data Graphical Techniques for Displaying Data Stem and Leaf Plots, Histograms, Box Plots The students will know how to, calculate these quantities, and be able to use them to make statements about a data set. The students will know how to, construct these various graphical techniques, and display data visually. They will be able to identify when each is used. 10,11 G Probability Concepts: Probability of Events, Probability for Discrete and Continuous Random Variables (probability distribution function, probability density function) The students will familiarise themselves with different axioms of probability. They will know the difference between discrete and Continuous Random Variables, and how to calculate their respective probabilities. 1,13 H Particular Probability Distributions: Bernoulli, Binomial, Geometric, Negative Binomial and Normal Distributions. The students will discover how to, identify the different distributions, and calculate their respective probabilities. 14 I Sampling Distributions and the Central Limit Theorem. The students will discover how to form a distribution, and the different issues with small and large sample. 15,16,17,18 J Margin of Error, Confidence Interval Estimation for population mean and proportion using a single sample. The students will discover how to calculate these quantities. They will also know how to interpret them. 19,0,1,,3 K Hypothesis testing: Principle of a Hypothesis Test, Errors in The students will know the following: different steps and errors involved in

4 Hypothesis Testing, P-values, Tests for population means and proportions in a single sample Hypothesis Testing; how to calculate P-values from the statistical tables; when to use the t-distribution or normal Distribution function, how to calculate the t or z statistic; how to carry out the Tests for population means and proportions in a single sample. 4 L Summary Schedule Hours Lectures 4 Small Group 11 Labs 6 Totals 41 Recommended Text book: Introduction to Statistics and Data Analysis 3 rd Edition by Roxy Peck, Chris Olsen and Jay Devore.

5 Tutor-led Tutorial Schedule: Weeks Lectures Homework Tutorials Labs 1 1(A) (B) 3(C) 4(C,D) 1(B,C) *Introductory session Introduction 3 5(D) 6(E) (D) 1(B,C) 1(B,C,D) 4 7(E,F) 8(F) 3(E,F) (D) 5 9(G) 10(G) 4(G) 3(E,F) (E,F) 6 11(H) 1(H) 5(H) 4(G) 7 13(I) 14(I,J) 6(I,+) 5(H) 3(G,H) 8 15(J) 16(J) 7(J) 6(I,+) 9 17(J) 18(J,K) 8(J) 7(J) 4(I,J) 10 19(K) 0(K) 9(K) 8(J) 11 1(K) (K) 10(K) 9(K) 5(K) 1 3(K) 4(L) 10(K) Note: A L: Topic codes; +: Summary of the first six weeks

6 Lectures (n=147) Hours Specified Learning Activities (online, etc ) Submission of Assignment Tutorials (n= 30-50) Laboratories Week 1 Week Topics A,B Topics C,D Topics B,C Problem to Introduction Week 3 Week 4 Week 5 Week 6 Week 7 Week 8 Week 9 Topics D,E Topic G Topic D Topic G Topic H Topic H Topics I,J Topic I Topic J Topic J Topics J,K Topic J Introductory session Topics B,C Topic D Topic G Topic H Topic I Topic J Topics B,C,D Topics G,H Topics I,J

7 Week 10 Week 11 Week 1 Week 13,L Revision Topic J Weeks Exam material from lectures, labs.