| Tasmanian School of Business & Economics 1.1 Introduction to the Unit Ensure that you carefully read the unit outline, including the sections on conduct and behaviour. |
| BEA603 Economics and Statistical Analysis |
| Tasmanian School of Business & Economics 1.2 Unit coordinator: Steve Thollar Email: [email protected] Consultation Times: TBA • Have taught statistics and data analysis, economics, financial mathematics for 25+ years. • But more significantly have 30+ years industrial experience in the real world in a variety of managerial and analysis roles, and have also run my own consulting business. • Have worked in, business improvement, project and program management, marketing, market research, strategy, forecasting and business planning, legal and regulatory, operations, and customer experience…. Just to name a few areas… • … AND have used skills based upon the foundations laid in this unit to help inform multi-million dollar business and investment decisions. |
| Unit coordinator |
| Tasmanian School of Business & Economics 1.3 On completion of this unit, you will be able to: 1. Understand key statistical and economic concepts and their application in business environment. 2. Apply data analysis, basic statistical techniques and the economic way of thinking to explain simple economic events, decisions and actions. 3. Communicate statistical and economic analyses and provide relevant recommendations for business and/or government policy decision making. |
| Intended learning outcomes |
| Tasmanian School of Business & Economics 1.4 *The Online quizzes are scheduled for Saturday mornings. You should plan any other commitments that you have so that you keep this timeslot free. If you cannot free up this timeslot and have supporting documentation (from medical authority or employer for example) please contact the Unit Coordinator as soon as possible to allow investigation of possible alternative arrangements. * * |
| Assessment schedule |
| Tasmanian School of Business & Economics 1.5 |
| Assessment details |
| Tasmanian School of Business & Economics 1.6 More information to be provided by week 2 |
| Assessment details |
| Tasmanian School of Business & Economics 1.7 |
| Assessment details |
| Tasmanian School of Business & Economics 1.8 More information to be provided by week 8 |
| Assessment details |
| Tasmanian School of Business & Economics 1.9 • Prescribed Textbook • Custom e-Book: CP1253 Economics and Statistical Analysis BEA603 UTAS. It can be purchased directly from the publisher via https://au.cengage.com/c/isbn/9780170456470 • As weekly readings and tutorial practice questions will be drawn from this e-Book, please ensure that you purchase it for essential learning purposes. |
| Prescribed textbook |
| Tasmanian School of Business & Economics 1.10 |
| Teaching schedule |
WEEK
DATE
BEGINNING TOPIC/ MODULE/ FOCUS AREA ACTIVITIES
READINGS
(PRESCRIBED E-BOOK)
1 22 February Introduction. What is statistics? Data
types collection and sampling. Chapter 1,2
2 1 March Graphical descriptive techniques. Chapter 3,4
3 8 March Numerical descriptive measures Chapter 5
4 15 March Probability Chapter 6
5 22 March Random variables and probability
distributions Chapter 7,8
6a 29 March Sampling distributions and estimation Chapter 9,10
Mid-semester break: 1 – 7 April (Inclusive)
6b 8 April Sampling distributions and estimation Chapter 9,10
7 12 April Hypothesis Testing Chapter 12
8 19 April
Thinking like economist.
Market supply and demand
Chapter 14, 15
9 26 April Markets in Action Chapter 16
10 3 May Elasticity of supply and demand Chapter 17
11 10 May Measuring the size of the economy Chapter 18
12 17 May International trade and finance Chapter 19
13 24 May Review. Exam preview.
Exam Period: 5–22 June (Inclusive)
| Tasmanian School of Business & Economics 1.11 Lectures: There will be no face-to-face or online lectures during the semester. Each lecture will be pre-recorded and placed on MyLO before the start of the week. Slides from the videos will also be made available. Tutorial Calssess: Face to Face tutorial classes will be held from week 2 to week 13. (at least one session will be recorded). All on-Campus students are expected to attend these sessions at a time slots selected via the ‘Allocate+’ or ‘MyTimetable’. |
| Lectures & Tutorials Arrangement |
| Tasmanian School of Business & Economics 1.12 Week X Week X+1 • View Week’s schedule on MyLO • Complete week’s Readings • Watch week’s Videos • Take notes or make annotations • Identify anything requiring clarification • Attempt practice exercises • Clarify any issues • Review practice exercises • Apply learnings to other scenarios • Learn additional skills (incl Excel etc) (Videos and Readings) (Tutorial Classes) • View Week’s schedule on MyLO • Complete week’s Readings • Watch week’s Videos • Take notes or make annotations • Identify anything requiring clarification • Attempt practice exercises |
| Two Week Learning Cycle |
AssignmentTutorOnline
Tasmanian School of Business & Economics
1.13
End of Introduction to the Unit
Tasmanian School of Business & Economics
1.14
Chapter 1
Part I
Green bits: Anything in green is further comment that I have added to the publishers slides.
| TASMANIAN SCHOOL OF BUSINESS AND ECONOMICS Chapter 1 |
| What is statistics? |
| Tasmanian School of Business & Economics 1.16 1.1 Key statistical concepts. 1.2 Statistical applications in business. 1.3 How managers use statistics. 1.4 Statistics and the computer. |
| Chapter 1 outline |
| Tasmanian School of Business & Economics 1.17 * Many scholars will often identify a third branch of statistics. We will come back to this point later. |
| Part I |
| • Introduction • Two branches of statistics* |
| Tasmanian School of Business & Economics 1.18 • In today’s digital world, ever increasing amounts of data are gathered, stored, reported on, and available for further study. • You hear the word data everywhere. • Data are facts about the world and are constantly reported as numbers by an ever increasing number of sources. |
| Introduction to statistics |
| Tasmanian School of Business & Economics 1.19 Data never sleeps. Every minute massive amounts of it are being generated from every phone, website and application across the Internet. Just how much data is being created and where does it come from? For that you should check out this Domo infographic. |
| Introduction to statistics |
| Tasmanian School of Business & Economics 1.20 • For business applications, data are collected from: – Direct observation or measurement. – Customer surveys. – Political polls. – Economic surveys. – Marketing surveys. – As a by-product of business processes – “internet of things” – Etc … |
| Introduction to statistics |
| Tasmanian School of Business & Economics 1.21 • How can we make use of the collected data to help make informed business decisions? • By learning statistics, which is a collection of various techniques and tools, we can help make such decisions. |
| Introduction to statistics |
| Tasmanian School of Business & Economics 1.22 ‘Statistics is a way to get information from data to make informed decisions.’ Data Statistics Information Data: Mostly numerical facts collected from direct observations, measurements or surveys. Information: Knowledge communicated concerning some particular fact, which can be used for decision making. |
| What is statistics? |
| Tasmanian School of Business & Economics 1.23 • A student enrolled in a business program is attending his first lecture of the compulsory business statistics course. • The student is somewhat apprehensive because he believes the myth that the course is difficult. • To alleviate his anxiety, the student asks the lecturer about last year’s exam marks of the business statistics course. • The marks provided composed of all the within-semester assessment items plus the end-of-semester final exam. |
| Example: Stats anxiety |
| Tasmanian School of Business & Economics 1.24 Data Statistics Information List of last year’s statistics marks: Summary information derived about the statistics class. •Examples: Class average Proportion of class receiving F’s Most frequent mark Highest and lowest marks Grade (HD,DN,CR,PP,NN) distribution …. etc. 1.24 |
| Example: Stats anxiety… |
| Tasmanian School of Business & Economics 1.25 • ‘Typical mark’: Mean (average mark) =72.67 Median (mark such that 50% above and 50% – below)=72 • Are most of the marks clustered around the mean or are they more spread out? Range = Maximum – minimum = 92 – 53 = 39 Variance Standard deviation |
| Example: Stats anxiety… |
Tasmanian School of Business & Economics
1.26
• Are there many marks below 60 or above 80?
• What proportion are HD, D, C, P and F grades (distribution of
grades)?
• A graphical technique – histogram – can provide us with this
and other information.
Example: Stats anxiety…
0
10
20
30
50 60 70 80 90 100
Frequency
Marks
Histogram
– Most students received marks between 60 and 90.
– No student received marks below 50.
– A significant number of students received marks above 80.
| Tasmanian School of Business & Economics 1.27 1. Descriptive Statistics 2. Inferential Statistics Scholars often identify a third branch, Design. Design includes decisions about what data are needed, how it be collected or acquired, how it will be coded and stored. When you’ve got the data |
| Two major branches of statistics |
| Tasmanian School of Business & Economics 1.28 • Descriptive statistics deals with methods of organising, summarising, and presenting data in a convenient and informative way. • One form of descriptive statistics uses graphical techniques*, which allow statistics practitioners to present data in ways that make it easy for the reader to extract useful information. * Sometimes referred to as data visualisation. |
| Descriptive Statistics |
| Tasmanian School of Business & Economics 1.29 • Another form of descriptive statistics uses numerical measures to summarise data. • The mean and median are popular numerical measures to describe the location of the data. • The range, variance and standard deviation measure the variability of the data. |
| Descriptive Statistics |
| Tasmanian School of Business & Economics 1.30 • Descriptive statistics describe the data set that is being analysed, but does not provide any tools for us to draw any conclusions or make any inferences about the data. • Hence, we need another branch of statistics: inferential statistics. – It is also a set of methods, but it is used to draw conclusions or inferences about characteristics of populations based on sample statistics calculated from sample data. |
| Inferential Statistics |
| Tasmanian School of Business & Economics 1.31 Some people think that that statistics is about making things difficult or confusing. In fact it is exactly the opposite, Statistics is about making things easier: • It about taking masses of confusing data and processing it so it can be understood, communicated, and used as a basic for making decisions. • Or it can be about how to collect data, when you have none. Its about turning data into information (which we can then use). |
| Part I: Summary |
| • What is statistics? • Descriptive statistics • Inferential statistics |
Tasmanian School of Business & Economics
1.32
Chapter 1
Part II
| Tasmanian School of Business & Economics 1.33 1.33 |
| Part II |
| • Key statistical concepts • Statistical inference • Confidence and statistical levels |
| Tasmanian School of Business & Economics 1.34 1.34 |
| Part II |
| 1.1 Key statistical concepts |
| Tasmanian School of Business & Economics 1.35 • Population – A population is the group of all items (data) of interest. – Population is frequently very large; sometimes infinite. Examples: 1. All current 2 million or so members of an automobile club. 2. All prawns available at the freshwater prawn Farm A in Queensland. 1.35 |
| 1.1 Key statistical concepts |
| Tasmanian School of Business & Economics 1.36 • Sample – A sample is a set of items (data) drawn from the population of interest. – Sample could potentially be very large, but much less than the population. – Example: 1. A sample of 500 members of the automobile club selected. 2. A sample of 1000 prawns selected from different sections of the freshwater prawn Farm A. 1.36 |
| 1.1 Key statistical concepts … |
| Tasmanian School of Business & Economics 1.37 • A descriptive measure of a population is called a parameter (e.g. Population mean). • A descriptive measure of a sample is called a statistic (e.g. Sample mean). Parameter Population Sample Statistic Subset 1.37 NB measuring a population is called taking a census |
| 1.1 Key statistical concepts … |
| Tasmanian School of Business & Economics 1.38 • Statistical inference is the process of making an estimate, prediction, or decision about a population based on a sample. Population Inference • What can we infer about a population’s parameter based on a sample’s statistic? 1.38 Sample Parameter Statistic |
| Statistical inference |
| Tasmanian School of Business & Economics 1.39 • We use sample statistics to make inferences about population parameters. • Therefore, we can produce an estimate, prediction, or decision about a population based on sample data. • Thus, we can apply what we know about a sample to the larger population from which it was drawn! 1.39 |
| Statistical inference |
| Tasmanian School of Business & Economics 1.40 • Rationale – Large populations make investigating each member impractical and expensive. – Easier and cheaper to take a sample and make estimates about the population from the sample. – However, such conclusions and estimates are not always going to be correct. – For this reason, we build into the statistical inference ‘measures of reliability’, namely confidence level and significance level. 1.40 |
| Statistical inference |
| Tasmanian School of Business & Economics 1.41 • When the purpose of the statistical inference is to draw a conclusion about a population, the significance level measures how frequently the conclusion will be wrong in the long run. – Example, a 5% significance level means that, in the long run, this type of conclusion will be wrong 5% of the time. 1.41 Don’t worry too much about this and the following slide. We will come back to this in later weeks. |
| Confidence and significance levels |
| Tasmanian School of Business & Economics 1.42 • The confidence level is the proportion of times that an estimating procedure will be correct. – Example: A confidence level of 95% means that, estimates based on this form of statistical inference will be correct 95% of the time. 1.42 |
| Confidence and significance levels |
| Tasmanian School of Business & Economics 1.43 1.43 |
| Part II: Summary |
| • Key statistical concepts • Statistical inference • Confidence and statistical levels |
Tasmanian School of Business & Economics
1.44
Chapter 1
1.44
Part III
| Tasmanian School of Business & Economics 1.45 1.45 |
| Part III |
| 1.2 Statistical applications in business 1.3 How managers use statistics 1.4 Statistics and the computer |
| Tasmanian School of Business & Economics 1.46 Example: Pepsi’s Exclusivity Agreement • A large university with a total enrolment of about 50 000 students has offered Pepsi-Cola an exclusivity agreement that would give Pepsi exclusive rights to sell its products at all university facilities for the next year with an option for future years. • In return, the university would receive 35% of the on-campus revenues and an additional lump sum of $200 000 per year. • Pepsi has been given 2 weeks to respond. 1.46 |
| 1.2 Statistical applications in business |
| Tasmanian School of Business & Economics 1.47 • The market for soft drinks is measured in terms of 375 ml cans. • Pepsi currently sells an average of 10 000 cans per week (over the 30 weeks of the year during two teaching semesters that the university operates). • The cans sell for an average of $2.00 each. The costs include a labour amount of 50 cents per can. • Pepsi is unsure of its market share but suspects it is considerably less than 50%. 1.47 |
| Statistical applications in business |
| Tasmanian School of Business & Economics 1.48 • A quick analysis reveals that if its current market share were 25%, then, with an exclusivity agreement, Pepsi would sell 40 000 (10 000 is 25% of 40 000) cans per week or 1 200 000 cans per year. • The profit or loss can be calculated. • The only problem is that we do not know how many soft drinks (all types including Pepsi) are sold weekly at the university. 1.48 |
| Statistical applications in business |
| Tasmanian School of Business & Economics 1.49 • The population in this example is the soft drink consumption of the university’s 50 000 students. The cost of interviewing each student would be prohibitive and extremely time consuming. • Statistical techniques make such endeavours unnecessary. • Instead, we can sample a much smaller number of students (the sample size is 500) and infer from the sample data the number of soft drinks consumed by all 50 000 students. • We can then estimate annual profits for Pepsi. 1.49 |
| Statistical applications in business |
| Tasmanian School of Business & Economics 1.50 • Pepsi assigned a recent university graduate to survey the university’s students to supply the required information. • Accordingly, she organises a survey that asks 500 students to keep track of the number of soft drinks by type of drink (Pepsi, Coke, Lemonade etc.) they purchase during the next 7 days. 1.50 |
| Statistical applications in business |
| Tasmanian School of Business & Economics 1.51 • Solution: – The information we would like to acquire in the Example is an estimate of annual profits from the exclusivity agreement. – The sample data to be used for this purpose are the number of cans of the various types of soft drinks consumed during the 7-day survey period by the 500 students in the sample. – To summarize the data collected from the 500 sampled students, we could use the graphical descriptive statistics methods (to show the distribution of purchase by drink type) and numerical descriptive measures (to calculate the mean number of soft drinks purchased per day by the students). 1.51 |
| Statistical applications in business |
| Tasmanian School of Business & Economics 1.52 • Solution … – To make an informed decision about signing-up for the Exclusivity agreement, we want to estimate the mean number of the various soft drinks consumed by all 50 000 students on campus. – To accomplish this goal we use another branch of statistics – inferential statistics, which is a collection of techniques used to make inferences about the population using sample data. 1.52 |
| Statistical applications in business |
| Tasmanian School of Business & Economics 1.53 • Statistical Applications in Business – Statistical analysis plays an important role in virtually all aspects of business and economics. – Throughout this course, we will see applications of statistics in accounting, economics, finance, human resources management, marketing, and operations management. 1.53 NB This clearly a simplified example, because another critical piece of information is missing – how consumers will behave, under a exclusivity arrangement, for example what proportion of Coke drinkers will begrudgingly switch to Pepsi on campus, versus buy Coke off campus, or cut back drink consumption. Revisiting this example might be helpful when you are thinking about assignment 1. |
| 1.3 How managers use statistics |
| Tasmanian School of Business & Economics 1.54 Will review in Week 2 tutorials NB We won’t use data Analysis Plus – there is little point in learning a proprietary package that wont necessarily be available in the workplace. • Calculating manually. • Computer applications using Microsoft Excel – Data Analysis (See file ‘Excel add-ins Instructions’) *** Introduction to MS Excel, pages 13-16. – Data Analysis Plus (CourseMate for Business Statistics Website) – Excel Workbooks (CourseMate for Business Statistics Website) 1.54 Will be available on MyLO |
| 1.4 Statistics and the computer |
| Tasmanian School of Business & Economics 1.55 1.55 Data tab Data Analysis Tools If you haven’t already got analysis tools add-in loaded in Excel, have a go, using the instructions in the text. In next week’s class we will go through the loading of data analysis add-in for Excel (for windows), in case anyone has difficulty. We won’t be using the publishers “data analysis plus” add-in, as you may not have access to proprietary tools in the workplace… its better to skill up on the features that come with Excel. |
| Excel: ‘Data Analysis’ |
| Tasmanian School of Business & Economics 1.56 1.56 |
| Part III: Summary |
| • Statistical applications in business • How managers use statistics • Statistics and computer |
| Tasmanian School of Business & Economics 1.57 • Describe two* major branches of statistics – descriptive statistics and inferential statistics. • Understand the key statistical concepts – population, sample, parameter, statistic and census. • Provide examples of practical applications in which statistics have a major role to play. • Understand how statistics are used by business managers. • Understand the basics of the computer spreadsheet. 1.57 |
| Chapter 1: Summary |
| Tasmanian School of Business & Economics 1.58 |
| Chapter 1 Tutorial: Week 2 |
| Selected practice questions (Prescribed e-book) *Page 12: 1.3, 1.4, and 1.5 |
