STA1010 Semester 2(S2-01) 2018 PDF

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

Unit Guide 

STA1010 Statistical methods for science Semester 2, 2018  

We acknowledge and pay respects to the Traditional Owners and Elders - both past and present of the lands and waters on which our four Australian campuses operate.  Handbook link: http://monash.edu.au/pubs/2018handbooks/units/STA1010.html   Note to students: If you are allocated to a lecture activity as a livestream unit you do not need to come to campus. A link to the livestreams will be available in your moodle unit. For information on how to participate in your lectures via live streaming you can review this guide created by the library:https://guides.lib.monash.edu/learning-tools/video If you have any technical issues please contact the service desk:https://www.monash.edu /esolutions/contact If you need more information on timetabling you can visit their site:https://www.monash.edu /timetables/fix-problems

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Table of contents Unit handbook information

5

Synopsis

5

Mode of delivery

5

Workload requirements

5

Unit relationships

5

Prerequisites

5

Prohibitions

5

Co-requisites

5

Chief Examiner(s)

5

Unit Coordinator(s)

5

Lecturer(s)

5

Academic overview Learning outcomes

6 6

Teaching approach

6

Feedback to you

6

Assessment summary

7

Assessment requirements Assessment tasks

9 9

Examination(s)

11

IMPORTANT:

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Unit schedule WHAT IS DUE when in Continuous Assessment 2018-02? Your feedback to us Previous student evaluations of this unit Unit resources

12 15 15 16 16

Learning resources

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Required resources

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Technologyrequirements

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Examination material or equipment

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IMPORTANT: CALCULATORS NOT APPROVED for use in STA1010 Examinations are: Other information

17 18 18

Policies

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Special Consideration

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Graduate Attributes Policy

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Student Charter

19

Student Services

19

Monash University Library

19

Disability Support Services

19

Plagiarism, cheating and collusion

19

Faculty policy information

20

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Unit handbook information Synopsis Descriptive statistics, scatter plots, correlation, line of best fit. Elementary probability theory. Confidence intervals and hypothesis tests using normal, t and binomial distributions. Use of computer software. Formal treatment of statistical analyses and the role of probability in statistical inference.

Mode of delivery Clayton (On-campus)

Workload requirements Three 1-hour lectures and one 2-hour support class per week

Unit relationships 

Prerequisites SCI1020, VCE Mathematical methods 3 and 4, or equivalent international qualifications listed in the Unit Guide.

Prohibitions ETC1000, ETW1000, ETW1102, ETX1100, FIT1006 and MAT1097. Note: students who have completed STA1010 cannot subsequently undertake SCI1020.

Co-requisites None

Chief Examiner(s) Associate Professor Jonathan Keith

Unit Coordinator(s) Associate Professor Jonathan Keith (Clayton) Dr Daniel McInnes (Clayton) Dr Ashwini Gengatharan (Malaysia)

Lecturer(s) 5

Name:DrDanielMcInnes Campus:Clayton Phone:+61 3 990 54420 Email:[email protected] 

Academic overview Learning outcomes On completion of this unit students will be able to: 1. Understand the key steps of the scientific method and how it can be applied to real problems that involve data analysis and interpretation; 2. Appreciate how statistical data is collected, analysed and stored; 3. Understand the meaning of population parameters such as mean, standard deviation, and median; 4. Understand the importance of statistical techniques in the analysis of data; 5. Present and interpret data graphically; 6. Determine confidence intervals for population parameters, and distinguish between a population parameter and a sample statistic; 7. Determine the appropriate statistical technique for a given context; 8. Perform simple statistical operations using Excel; 9. Take a random sample from a population and determine whether data fits a statistical hypothesis; 10. Prepare and write a scientific report.

Teaching approach 1.  Lectures: A schedule for Clayton campus is attached. The lecture Powerpoint material (as .pdf) can be found under Lectures after each block of lectures. The lectures are recorded in Echo360. It is highly recommended that you attend all Lectures ... pass rate IS higher. 2. Support  class (laboratory) every week.Each week, beginning in week 2, you are required to attend 2-hours of computer laboratory/tutorial. Your participation in these classes is monitored and is assessed. Sign up for a laboratory group through the Allocate+( http://allocate.cc.monash.edu.au). Changes in weeks 1-2 are through Allocate+. Any later changes must be done through the Senior Tutor. It is your responsibility to be allocated properly into a workshop that IS suitable for you , so that your continuous assessment throughout the semester is managed correctly.

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Feedback to you Feedback is provided to students during the semester via the “continuous assessment” component of the course, to assist their learning and help identify issues for which they need to seek further assistance. This includes: ●

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tutorial sheets which are marked and returned at the next support class. General feedback will be given to the lab group; weekly online quizzes that are designed to check your understanding of the previous week's material and identify any weak areas; written assignments, returned about two weeks later with individual feedback, with sample solutions on Moodle; and

we encourage students to ask questions and seek help from the lecturer in voluntary help sessions offered each week in the MLC area;

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Assessment summary Examination (3 hours): 60% (Hurdle) Continuous assessment: 40% Hurdle requirement: To pass this unit a student must achieve at least 50% overall and at least 40% for the end-of-semester exam. Assessment task

Value

Due date

On-Line Quizzes

10%

Weeks 3-12

Support class activities

5%

Weeks 3-12

Project

10%

Project parts are to be submitted in support classes in weeks 4, 7 and 12. See schedule of assessment tasks for further details.

Assignments

15%

Week 6 and Week 11

Examination

60%

To be advised

Students who have attempted this unit previously must complete all assessment ttasks asks again. No marks can be carried from a previous enrolment in the unit. Students are required to keep a copy of any submitted work. Printed work that is submitted for assessment must be accompanied by a completed Coversheet. This can be obtained from Moodle page or next to the submission boxes.

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Extensions and penalties: Failure to submit assessment will normally lead to a zero mark being recorded for that work, unless a Special Consideration application has been approved. The application must go to the Chief Examiner within two working days of the relevant class or due date. The appropriate application form must be used and original supporting documentation provided. See http://www.monash.edu/exams/special-consideration.html for the in-semester application form. Applications for extensions should go to: A/Prof Jonathan Keith (Clayton campus), TBA (Malaysia campus) All late assignments must be submitted directly to the Unit co-ordinator on your campus. Late submissions for Assignments and Projects will be penalised with 10% per day or part thereof late, including weekends. Work submitted more than 1 week late will not be marked. This is in accordance with the Faculty of Science policy: http://intranet.monash.edu.au/science/staff /education/policies-procedures/late-submission.html . To achieve a pass in the unit students must achieve at least an overall mark of 50%, with a 40% hurdle on the final examination. It is not necessary that you pass each component of the assessment but the Chief Examiner may consider any differences between marks for work completed under examination and non-examination conditions when assessing any mark-rounding issues.

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Assessment requirements Assessment tasks Assessment title:  On-Line Quizzes Details of task:  Weekly quizzes will be set in Weeks 3-12 (inclusive). The quizzes will contribute to 10% towards your final mark. These are accessed through Moodle and are marked automatically.Times are Australian Eastern Time: OPEN at 12.00am TUESDAY & CLOSE ONE week later 11:55 pm MONDAY.

Value: 10% Due date: Weeks 3-12 Estimated return date: Must be completed by 11:55pm Monday each week in Weeks 4 - 13 inclusive Topic release date: Released 1 week before due at 12:00am Tuesday in Weeks 3-12 inclusive Presentation requirements: N/A Word limit:N/A Individual assessment in group tasks: N/A Hurdle requirements: N/A Additional information: Multiple choice and short answer questions will be marked by computer against standard answers in the quiz database. Criteria for marking: Multiple choice and short answer questions will be marked by computer against standard answers in the quiz database. Assessment title: Support class activities Details of task: For many of the weeks, preliminary exercises at the START of the class. Attendance and active participation is monitored and combined with the preliminary questions count 5% to the unit assessment. Value: 5% Due date: Weeks 3-12 Estimated return date: Preliminary exercises are due at the START of each support class. Topic release date: Preliminary exercises will be available in Moodle from the start of Semester. Presentation requirements: Readable hand-written answers are required. Marks are awarded for quality and detail of answers. Word limit:N/A Individual assessment in group tasks: N/A Hurdle requirements: N/A Additional information: Preliminary questions are to handed in at the beginning of most support classes, starting in week 3. These will be

9 STA1010 Statistical methods for science - Semester 2 (S2-01) - 2018

marked by tutors against a standard marking guide. Criteria for marking: Preliminary exercises will be marked by tutors and returned in class the following week.These will be marked against a standard marking guide. Assessment title: Project Details of task: A three part project runs throughout the whole semester and is a group work organised within the support class: Part A: Group Proposal and Part B: 5% Experimental report; Part C: 5% Inference Calculation Value: 10% Due date: Project parts are to be submitted in support classes in weeks 4, 7 and 12. See schedule of assessment tasks for further details. Estimated return date: Projects will be marked by tutors and returned in support classes the week after submission. Topic release date: Details of the project will be available in Moodle from the start of Semester. Presentation requirements: Readable hand-written or typed answers are required. Marks are awarded for quality and detail of reports. Word limit:N/A Individual assessment in group tasks: The project is group work to be carried out in small groups of 3-5 students. Submissions are to be group efforts, but individual members MUST demonstrate participation in group work via contribution to online forums provided for the purpose. Tutors may deduct marks from individuals deemed to have made insufficient contributions to the group submissions. Hurdle requirements: N/A Additional information: The project is comprised of three parts, which are to be submitted by each project group in three stages (one submission per group each time) and will be marked against a standard marking rubric. Criteria for marking: The project components will be marked according to a standard marking rubric for each part, which will be returned to the groups. Assessment title: Assignments Details of task: Two (2) Assignments,each 7.5%, one released in week 3 (due W6) and one in week 8 (due W11). These are to be done outside of the workshop sessions. Both calculation and short answer exercises will be included. Details will be distributed in lectures AND on Moodle. Value: 15% Due date: Week 6 and Week 11 Estimated return date: Assignments will be marked by tutors and returned in support classes 1-2 weeks after submission. Topic release date: TBA Presentation requirements: Readable hand-written or typed answers are required. Marks are awarded for quality and detail of answers. Word limit:N/A Individual assessment in group tasks: N/A Hurdle requirements: N/A Additional information: The projects will assess your understanding of the material covered up to the week prior to the due date. They are designed to be provide a range of difficulty, from basic understanding to quite challenging. Criteria for marking: Each assignment will be marked by tutors against a standard marking guide, which is designed to provide consistency of marking across the different support classes.

10 STA1010 Statistical methods for science - Semester 2 (S2-01) - 2018

Examination(s) Title: Examination Value: 60% Duration: 3 hours Hurdle requirements: requirements: A hurdle of 40% result is applied to the end-of-semester exam for a pass in this unit. Electronic devices allowed: Anon-programmable scientific calculatorwill be allowed. See attached “Calculators in Exam” statement for the approved calculators. Calculator use has been approved for the STA1010 examination by the Chief Examiner in the School of Mathematical Sciences with the following restriction:

ONLY ONLYANON-PROGRAMMABLE, ANON-PROGRAMMABLE, SCIENTIFIC CALCULATOR WILL WILLBE BE PERMITTED IN EXAMINATIONS INTHIS UNIT.

Examples (but not limited to) of approved calculators (withtheauthorised sticker ) are: a)Simple calculators calculatorswith arithmetic functions only b)Scientific calculators calculators, that arenot not programmable, withstatistics statistics functions.

Examples of APPROVED makes and models of Scientific C alculators: Canon: F720, F720i Casio: fx-82, fx-100, fx-115, fx-350, fx-570, fx-911, fx-991 and fx-992 series Citizen: SR-135, SR-260, SR-270, SR-275 Hewlett Packard: HP-9s, HP-10s (1-var stats only), HP-30s, HP SmartCalc 300s Texas instruments: TI-30 and TI-34 series Sharp:EL-506, EL-509, EL-520, EL-531, EL-535 series

IMPORTANT: Only these listed calculators or their equivalent, and with theauthorized authorized STICKER : “ MonashUniversity Science” Science”will be accepted in the exam by the invigilators. The sticker will be available fromDr Daniel McInnes (Clayton 9Rnf/453),Dr Dr Dianne Atkinson (Clayton 9Rnf/449) orDr Sharon Chen (Malaysia)

You should practise using the appropriate calculator before the exam.

Exam details: The exam will beclosed book.Some statistical formulae and all necessary statistical tables will be provided with the paper. 

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Unit schedule For units with on-campus classes, teaching activities are normally scheduled to start on the hour (teaching will commence on the hour and conclude 10 minutes prior to the scheduled end time). The table below shows the planned schedule of activities and assessment for this unit but from time to time it may be necessary to adjust this for operational reasons. Please listen for announcements in lectures and/or check official announcements on Moodle regularly. Design of Experiments Lecture 1 Producing data (Design of experiments) Lecture 2 Sampling and randomisation Descriptive statistics Lecture 3  Looking at data: histograms and quartiles Lecture 4 Summarising data: boxplots, means and SD’s Lecture 5 Robust statistics, linear transformations of a variable

Lecture 6 Relations in categorical data: marginal, conditional and  joint distributions Fitting straight lines to quantitative data Lecture 7 Scatterplots, linear relationships, least squares regression Lecture 8 More on regression, correlation Lecture 9 Residuals: linear, exponential and power relationships 2

Lecture 10 r (proportion of variability explained) & regression to the mean Lecture 11 Multiple linear regression

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Probability Lecture 12 Probability of events Lecture 13 The law of total probability Lecture 14 The Binomial distribution Lecture 15 Poisson distribution Lecture 16 Random variables, their means and variances Normal distribution

Lecture 17  The normal distribution Lecture 18 Calculations with normal random variables Lecture 19 Testing for normality Introduction to Statistical inference Lecture 20 Population and sample means & Central Limit Theorem Lecture 21 Towards statistical inference Lecture 22 Statistical inference: confidence intervals for the mean Lecture 23 Statistical inference: hypothesis testing for the mean Applications of Statistical inference Lecture 24 Comparing two means

Lecture 25 Non-parametric tests for comparing two means Lecture 26 Testing the slope in regression

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Lecture 27 Normal approximation to Binomial Lecture 28 Confidence intervals for proportions Lecture 29 Hypothesis testing for proportions Lecture 30 Chi-squared tests for independence

Lecture 31 Goodness-of-fit using the c2

Lecture 32 ANOVA – comparing several different means at once Lecture 33 Levene’s test, Tukey’s Honest Significant Differences Lecture 34 Understanding hypothesis testing The lectures have been divided into convenient topics. They will not all be the same length. Each lecture has at the end a set of Student Examples. It is intended that these should be done during lectures and so students should bring calculators. Solutions do not appear in these notes, but will be given in class. The solutions will, however, be in the version of the notes on the Moodle site.

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WHAT IS DUE when in Continuous Assessment 2018-02? Week starting Monday

Lectures

Laboratory Laboratory5% 5%

Quiz opens Tues am 10%

Week 1 23 Jul

L1-L3

(NO CLASS)

---(none)---

Week 2 30Jul

L4-L6

Lab 1 – (no Prelim.)

---(none)---

Week 3 6 Aug

L7-L9

Lab 2 – Preliminary Work

Quiz 1: Design & Sampling

Assignment 1 released Friday 10 Aug

Week 4 13 Aug

L10-L12

Lab 3 – Preliminary Work

Quiz 2: Descriptive stats

PROJECT PROJECTPartA Pa...