Quantitative Analysis for Business BSNS102. Course Outline Semester One 2014

Transcription

1 Quantitative Analysis for Business BSNS102 Course Outline Semester One 2014

2 Quantitative Analysis for Business This paper covers descriptive and inferential statistics for students majoring in Commerce, including applications to business research and practice. This course makes use of statistical software, with practical work emphasising applications EFTS, 18 points, Prerequisites: none, Restrictions: QUAN101, STAT110, and STAT115. Lectures: Computer Lab/Tutorial: Help Sessions: Mondays and Wednesdays see PIMS for rooms and times. 1x50 minutes per week as streamed on PIMS Un-streamed times to be advised Blackboard Skeleton lecture notes, assignments, data sets and notices will be placed on the BSNS102 Blackboard page. Staff Team Dr Trent Smith (Course Co-ordinator) Room: CO 701 Phone: Dr Sarah Baird Room: CO Warren McNoe (Course Administrator) Room: CO 703 Phone: All administrative enquiries (e.g. regarding assignments, labs, the mid-semester test, etc.) should go to Warren McNoe. If you are missing copies of handouts or other course materials, please download these from Blackboard. Staff Office Hours In addition to formal contact hours lecturers are available during their office hours. Office hours will be posted on the BSNS102 Blackboard staff information page. About BSNS102 BSNS102 is the introductory course in business statistics designed specifically for students majoring in Commerce. It covers the techniques business students are most likely to use in their courses and in their future workplace. The emphasis of the course is on identifying the underlying problem(s), choosing appropriate statistical techniques, justifying the validity of these techniques and interpreting the results in the context of the problem(s). The statistical package SPSS is used extensively and manual calculation is kept to a minimum. Excel will also be used in laboratory sessions BSNS102 follows a business case scenario throughout the whole semester. Assignments and the final exam are largely based on this case. 2

3 Learning Outcomes It is expected that students will develop the following knowledge and skills during the course of this paper: 1. Explain and implement sample survey methods and data collection. 2. Distinguish between different data types. 3. Tabulate, calculate and analyse descriptive statistical measures and interpret and make decisions from them. 4. Define discrete probability distributions and calculate probabilities. 5. Define the standard normal distribution and be able to standardise a normal distribution. 6. Implement hypothesis tests and construct confidence intervals for a mean or a proportion and interpret findings in context. (Both by hand and electronically, calculator and SPSS/Excel) 7. Implement hypothesis tests and construct confidence intervals for differences in two means or proportions for independent random samples and interpret findings in context. (Both by hand and electronically, calculator and SPSS/Excel) 8. Implement simple one-way ANOVA and carryout appropriate post-hoc tests (SPSS/Excel) 9. Carry out Chi-square multinomial (Goodness of fit) test and chi square test of independence and relate findings to decision making. (calculator and SPSS/Excel) 10. Undertake a simple linear regression analysis (SPSS). Assess the suitability of the analysis via residual analysis. Interpret findings in context and forecast. 11. Correlation analysis, Spearman s and Pearson s. Test for significant correlation and interpret findings in context. 12. Identify components of time series for both additive and multiplicative models. Interpret and calculate trends, seasonal factors, de-seasonalised data and forecast. Computer Labs/ Tutorials Refer to PIMS for your weekly computer laboratory time. Computer Laboratories start in the second week of the semester. Laboratories are held in COG04 or COG05 (On Leith St just south of Albany St, next to Liquor land). In the Labs students work on the techniques needed to solve tasks based on the business case scenario. It is our aim that all students will become proficient and self sufficient at using SPSS and Excel as statistical tools. If you are not streamed or need to change your streamed lab then you may do so on the web page If you have further difficulties streaming into a lab then contact either Warren (contact details above. Textbook Australian Business Statistics, Selvanathan, Selvanathan, Keller 6 th Ed. New copies of the sixth edition are available at the University Bookshop. There are also a large number of second hand copies of the third, fourth and fifth edition for sale. This text is also on reserve in the Central library. E book version: Additional reading on close reserve at the Central Library What is a p-value anyway? 34 Stories To Help You Actually Understand Statistics 200 pages Andrew Vickers 3

4 Expected Workload The expected workload for an 18 credit paper is 12 hours per week for 15 weeks. This time includes formal contact hours, completion of assignments and workshops, self study and exam preparation. For BSNS102 the expected workload is: Lectures 26 Computer Labs/ Tutorials 12 Assignments 30 Exam and quiz preparation 30 Computing outside lab times 22 Self study of notes and text 60 Total 180 Hours Guide to Assessment (100%) 1. Five hand in assignments 25% 2. Practical Lab Test 10% 3. Quiz 10% 4. Clickers 5% 5. Final Exam 50% To pass BSNS102 you must pass the final exam. If you fail the final exam your overall mark returned will be returned as F failed regardless of your internal assessment. Assignments Assignments are due on Friday by 12 pm (Monday 12 pm for Thursday Lab streams), see actual date on assignments. They should be posted in the appropriate Economics/BSNS102 box on the third floor of the Commerce building. Laboratory Participation Students should be punctual at labs. Lab sessions are designed to give you hands on data analysis experience in both Excel and SPSS. Due to time constraints help will be given with the current lab exercise, but help with previous material covered in labs may be limited. Printing facilities in the labs are limited, so in most cases you will need to print your output at a later time and place. Quiz A multi-choice quiz will provisionally be held in the evening on Wednesday 16 April 7pm rooms to be advised. The emphasis of the quiz will be on choosing the correct statistical technique and interpreting the results correctly. The quiz will be based on the same scenario as that used during the semester. Clickers Answering clicker questions (correctly) during lectures will accumulate points throughout the semester. Allowing for the occasional malfunction and/or absence from lectures, your mark out of 5% will be calculated by dropping your worst 6 lectures clicker results. You will need to bring your clicker with you to all lectures. Clickers will be issued at lectures in week one. Every student is required to register a clicker following the instructions given in lecture, and to return their clicker in working condition at semester s end or pay the $50 replacement cost. Failure to meet these conditions will result in an incomplete grade being issued for the semester. If you withdraw from the course you must return your clicker to the Economics department receptionist as soon as practicable or a $50 invoice will be issued. 4

5 Practical Lab Test A 40 minute Lab test will be held during one scheduled Laboratory time in the week beginning 7 April. The Lab practical is based on data analysis using SPSS and will primarily assess your level of competence in using SPSS. Students must attend the laboratory session they are streamed into and be punctual. Final Exam Long answer questions in the final exam will be of a similar format to the assignment questions with the emphasis on identifying the underlying problem(s), choosing appropriate statistical technique(s), justification of the validity of technique(s) and interpretation of result(s) in the context of the problem(s). There will be a multiple choice component to the exam. Additional help sessions will be held prior to the final exam. Topic Schedule Topics will be taught in the order shown in the table below, however the week shown may be subject to change. Week Lecturer Week Beginning Topics 1 Trent 24 February Data Types and Descriptive Measures 2 Trent 3 March 3 Trent 10 March 4 Trent 17 March Descriptive Stats, Sampling, Questionnaires Probability Distributions/ Cross Tabulation Central Limit Theorem, Confidence Intervals for One Sample Computer Labs Lab 1 Due Friday* 12 pm of week Lab 2 Assignment 1 Lab 3 5 Warren 24 March Hypothesis Tests for One Sample Lab 4 Assignment 2 6 Warren 31 March Confidence Intervals for Two Samples Lab 5 7 Warren 7 April 8 Trent 14 April 9 Sarah 28 April Hypothesis Tests for Two Samples, Non parametric Tests Revision of CI s and Hypothesis Testing. Evening Quiz Wednesday 16 April No Wednesday Lecture 21 April Mid Semester Break Chi-square Tests(Tests of Independence and Multinomial Goodness of Fit Test) Lab Test Lab 6 Assignment 3 Lab 7 10 Sarah 5 May One Way Analysis of Variance Test Lab 8 Assignment 4 11 Sarah 12 May Linear Regression Lab 9 12 Sarah 19 May Correlation and Time Series Forecasting Lab 10 Assignment 5 Due Friday 23 rd for all 13 Warren 26 May Review * Students streamed into Thursday Labs are to hand in their assignments by the following Monday 12 pm. (Exception: Assignment 5 due Friday 23 May for all students) 5

6 Reading List Australian Business Statistics Topics 3 rd edition 4 th and 5 th edition 6 th edition Types of Data Descriptive Statistics. Sampling Probability and Probability Distributions including concept of independence Sampling Distributions, Central Limit Theorem Confidence intervals for one population -means Confidence intervals for one population -proportions sample size estimation-one mean ands one proportion Fundamentals of Hypothesis testing Hypothesis Test for One Mean Hypothesis Test for One Proportion Confidence interval-difference between two means Confidence interval-difference between two proportions Hypothesis test - difference between two means: independent samples Hypothesis test difference between two proportions: independent samples Matched Pair experiment Non parametric techniques Chi-squared test -Test of independence Chi-squared test -Multinomial Test One Way Analysis of Variance Test Linear Regression The analysis of residuals. Correlation , , ,5.5,5.7, p , ,10.2, , , , , ,7.6,8.1, p396 (352 5 th ed.) 11.2, , , , ,14.2, ,4, ,8.1, , , , , ,15.2, , , Time Series, Forecasting Policy on late submissions and extensions Late assignments are not accepted. If you are unable to complete an assessment due to illness or other serious circumstances please contact the course administrator, Warren. Impairment The Economics Department encourages students to seek support if they find they are having difficulty with their studies due to disability, temporary or permanent impairments, injury or chronic illness. Contact either: Economics Disability Contact Person Or Disability Information and Support Phone: Fax: disabilities@otago.ac.nz Website: 6

7 Dishonest Practice and Plagiarism: Students should make sure that all submitted work is their own. Any student found responsible for dishonest practice (for example, copying, the use of unauthorised material in tests etc) in relation to any piece of work submitted for assessment shall be subject to the University s dishonest practice regulations which may result in various penalties, including forfeiture of marks for the piece of work submitted, a zero grade for the paper or in extreme cases exclusion from the University. Plagiarism is a form of dishonest practice. Plagiarism is defined as the copying or paraphrasing another's work, whether intentionally or through failure to take proper care, and presenting it as one's own (University of Otago Calendar 2006 page 193). In practice, this means plagiarism includes any attempt in any piece of submitted work (e.g. an assignment or test) to present as one's own work the work of another (whether of another student or a published authority). Any student found responsible for plagiarism in any piece of work submitted for assessment shall be subject to the University's dishonest practice regulations as outlined above. Class Representatives: The class representative system is an avenue for encouraging communication and consultation between staff and students involved in a particular paper or course of study at the University of Otago. It provides students with a vehicle for communicating their views on matters associated with the teaching and delivery of their paper or course of study. It provides staff with the opportunity to communicate information to and gain constructive feedback from students. It contributes to the development of a sense of community within a Department/School/Faculty and it adds a further dimension to the range of support services that the University of Otago offers its students. The School of Business fully supports the class representative system. Volunteers to act as class representatives for this paper will be called early in the semester. The OUSA then invites all class representatives to a training session, conducted by OUSA, about what it means to be a class representative and some of the possible procedures for dealing with issues that arise. They also provide information on the services that OUSA offers and the role OUSA can play in solving problems that may occur. The OUSA also provides ongoing support to class representatives during the semester. School of Business staff will also meet during the semester with the class representatives for this paper to discuss general issues or matters they wish to have considered. Disclaimer While every effort has been made to ensure that the information contained in this document is accurate, the information is subject to change. Changes will be notified in class and/or tutorials. Students are encouraged to check notice boards, etc for any changes. It is a students responsibility to be informed. 7

8 Movie Magic Scenario Movie Magic is part of a large chain of cinemas that are based throughout the country. The chain prides itself on its box office hits, up to date facilities and customer satisfaction. The data was collected daily over several months using a systematic sampling plan. The films showing during that period were considered to be fairly typical and nothing exceptional was noted happening in the city over the period during which the data was collected. The data includes the following variables: AGE DAY measured in years most frequent day to visit cinema 1. 7 Monday to Sunday DISCOUNT 0 Student/Beneficiary 1 Book of tickets 2 Membership Card 3 None GENDER 0 Male 1 Female INCOME MOVIECAT NEWRELEASE SNACKBAR SESSIONTIME annual income before tax measured in dollars type of movie most frequently visit the cinema to see 0 Family 1 Action/Thriller 2 Drama/Romance 3 Comedy 4 Sci-Fi/Fantasy 5 Horror whether a person sees movies in their first week of showing 0 Not first week of showing 1 First week of showing measured in dollars (amount spent at snack bar for person s own consumption) most frequent session time 0 Morning (before noon) 1 Afternoon (12 noon-5pm inclusive) 2 Evening (after 5pm and before 10pm) 3 Late (10pm or later) VISITFREQ 0 Very Often (more than once a week) 1 Often (once a week) 2 Regularly (two or three times per month) 3 Occasionally (once a month) 4 Seldom (less than once a month) 8