.1 Assessment Item 2 BFA534 2017 Case Study With rising obesity levels around the world and a greater focus on health, there has been increased participation in the fitness industry. In Australia the fitness industry produced revenue of around a billion Australian dollars annually, with close to 3 000 fitness centres (McMalcolm 2013). In Japan the health club industry accounted for revenue of 6.8 billion Australian dollars in 2015 (Statista Inc.). Australia and Japan led the Asian-Pacific region for revenue in this area in 2015 (Statista Inc.). However at the beginning of the second decade of the 21st Century, growth in revenue in the industry in Australia slowed because of market saturation (McMalcolm 2013). Joe Brooks is a veteran of the health industry, having managed, and then owned gyms and fitness centres in the Melbourne area since 1991. In 2005 he teamed up with a partner, Takashi Sato, to increase access to capital and help expand his successful business. Sato is originally from Yokohama in Japan, and as a wealthy businessman has many investments in Australia, Japan and some South East Asian nations. Sato met Brooks at one of the two Brooks Fitness centres in Melbourne’s east in 2002, where he went to work out. Sato was impressed by Brooks’ drive, charisma and confidence, along with his knowledge of the fitness industry. Although Sato was reserved and cautious by nature, the two first became trusted friends, and then business partners. They co-owned a chain of eleven fitness centres around Melbourne, which Brooks oversaw with a manager at each location. Sato was largely a silent partner. While on a trip to the US in 2016 to select new exercise machines for the fitness chain, Brooks became aware of the potential of the ClassPass business arrangement that operates in 20 US cities. ClassPass offers unlimited lessons for a relatively modest monthly fee to allow clients to choose from a diverse range of fitness studios that included yoga, dance, martial arts, Pilates, strength training and other choices. Clients were limited to choosing no more than three classes from the same studio in a month. However they could take an unlimited number of classes from other studios while conforming to this requirement. The arrangement benefited clients by enabling them to trial exercise options and increase their awareness of what was available in an affordable way. Small fitness studios benefited too by having more clients aware of their offerings, and encouraging clients to trial classes, utilising slots that otherwise may have been vacant. The incremental cost of participating for the fitness studios was small. The aim was that some of the trials would convert into ongoing membership and future recommendations with a fitness studio (Greenwald 2015). Brooks saw the potential of the scheme, and was aware that it was not implemented in Australia. He was able to confirm the scheme was not in use in Japan, either. After a sixmonth limited trial, the partners established their new business, SelectFit, which operated in Australia and Japan with managers in each nation, but was largely controlled from Australia by Brooks. Brooks had not travelled to Japan, but was confident the operation would be successful there. His networks in the Australian fitness industry and his ebullient and selfassured nature resulted in excellent take-up in Australia. Sato’s business contacts in Japan, and his reputation, led to SelectFit being embraced enthusiastically by fitness studio managers and customers there. The partners started action to list their combined fitness interests on the Australian Stock Exchange (ASX) as an Initial Public Offering (IPO). They met the profit and asset tests of the2 ASX. The owners’ motivation for listing arose from wanting access to a larger capital market, to set up SelectFit in other Asian areas where there was no similar competition. Conforming with ASX’s advice on how to list as an IPO on the ASX, the partners appointed professionals to advise on SelectFit’s corporate structure, financial matters, marketing and distribution of securities, as well as communication strategies (for investor, public and government relations) and other legal matters. Now the owners sought to retain specialist advisers on the requirements for compliance with the third Edition, ASX Corporate Governance Principles and Recommendations 2014, once their company is listed on the ASX. They want to learn about compliance and disclosure requirements relating to their first annual report once listed, and other key ways in which the corporate governance regulatory environment could affect them as a listed company. They wondered whether the corporate governance compliance requirements in Australia will detract from the benefits of listing on the ASX, and were concerned about any specific risks that the future company may face. References: Allan, G. (2006) The HIH Collapse: A costly catalyst for reform, Deakin Law Review, Vol. 11, No. 2, pp. 137-145 (only). Damiani, C., Bourne, N. and Foo, M. The HIH Claims Support Scheme, Available: http://treasury.gov.au/~/media/Treasury/Publications%20and%20Media/Publications/2015/R oundup%2001/Downloads/PDF/Roundup_01-2015_article3_HIH.ashx, Accessed: 4th March, 2017. (to end of p. 9 only) Greenwald, M. (2015) Top 11 Innovative Products and Services of 2014, CMO Network, Forbes, Available: https://www.forbes.com/sites/michellegreenwald/2015/01/12/top-11- innovative-products-and-services-of-2014/#b0a0d0f5ce99, Accessed: 28th February, 2017. McMalcolm, J. (2013) Australia’s Fitness Sector Sees Growth in the Billions, Australian Business Review, November 22, Available: http://www.businessreviewaustralia.com/leadership/153/Australia's-fitness-sector-seesgrowth-in-the-billions, Accessed: 28th February, 2017. Statista Inc., Revenue of the Health Club Industry in Asia-Pacific Countries 2015, Available: https://www.statista.com/statistics/308841/revenue-of-the-health-club-industry-asia-pacificcountries/, Accessed: 28th February, 2017. BRIEF You have been retained by SelectFit as a specialist Corporate Governance Advisor. Your brief is to present a written paper on corporate governance practice to SelectFit at its next company meeting. You are required to: 1. Outline how to maintain and establish good corporate governance once the company is listed, in accordance with the Third Edition ASX Principles and Recommendations 2014 and other relevant statements, law or guidance (and address any specific risks SelectFit may have). 2. Discuss the advantages and disadvantages of corporate governance, demonstrating the benefits to the business as a listed entity in having good corporate governance, together with any disadvantages.3 3. Assist your client to understand the importance and benefits of adhering to the ASX Principles and Recommendations by referring to HIH’s situation. Inform yourself about 2001 HIH’s collapse before you start this assignment, by doing some searching and reading. Two references are provided above on HIH to get you started. 4. Follow the business report structure, order and guidance as outlined in https://www.business.unsw.edu.au/Students-Site/Documents/Writingareport.pdf. However do not include a letter of transmittal. You will need a brief title page that replicates the requirements needed in business (and not those of a student cover page that you will provide in a TSBE cover page). A brief executive summary is needed with all the components mentioned in the link above, and an automatically generated table of contents. Search Google to learn how to generate a table of contents for the version of Word you use. ASSESSMENT CRITERIA: This Assignment accounts for 25% of your assessment in this unit. Word Limit: 1500 words NOTE: Record your paper in either Word or pdf format, and submit in to the appropriate MyLO drop box. Be aware that your submission will be analysed by sophisticated TurnItIn software for plagiarism. Assessment Item 2 requires a list of references and use of citations. Please use the Harvard style for both. There is a guide to the Harvard style at: http://utas.libguides.com/referencing/Harvard. Make use of headings and subheadings. Spell- and grammar-check your work using Word just before submitting. You are encouraged to incorporate the text of the Principles within the document rather than in appendices, as it will assist the flow. Note that title pages, table of contents, reference list and appendices are all included in the word count. The TSBE cover page is not included in the word count. Please refer to the Unit Outline for information about submission requirements and penalties, over length submissions and plagiarism. The Intended Learning Outcomes Table on page 5 of the BFA534 Unit Outline shows how Assessment 2 relates to the relevant Learning Outcomes. All the BFA534 Learning Outcomes need to be passed to pass this Unit. ASSESSMENT 2 RUBRIC An Assessment Item 2 Rubric on MyLO sets out how your work will be marked. Refer to this Rubric as you develop this assessment.MODULE ONE: PRESENTING AND DESCRIBING INFORMATION TOPIC 3: NUMERICAL DESCRIPTIVE MEASURES Deakin University CRICOS Provider Code: 00113B+ Learning Objectives At the completion of this topic, you should be able to: • calculate and interpret numerical descriptive measures of central tendency, variation and shape for numerical data • calculate and interpret descriptive summary measures for a population • describe the relationship between two categorical variables using contingency tables • describe the relationship between two numerical variables using scatter diagrams and time-series plots • construct and interpret a box-and-whisker plot • calculate and interpret the covariance and the coefficient of correlation for bivariate data 2+3.1 Measures of Central Tendency, Variation and Shape 3 Arithmetic Mean Median Mode Describing data by its central tendency, variation and shape Variance Standard Deviation Coefficient of Variation Range Interquartile Range Skewness Central Tendency Quartiles Measures of Variation Shape+Measures of Central Tendency 4 Central Tendency Arithmetic Mean Median Mode X n X n i ∑ i = = 1 Midpoint of ranked values Most frequently observed value+Arithmetic Mean For a sample of size n, the sample mean, denoted , is calculated: Where Σ means to sum or add up 5 n X X X X n X n n i i + + + = = ∑=  1 1 2 X i’s are observed values X+Median In an ordered array, the median is the ‘middle’ number (50% above, 50% below) Its main advantage over the arithmetic mean is that it is not affected by extreme values 6 0 1 2 3 4 5 6 7 8 9 10 Median = 3 0 1 2 3 4 5 6 7 8 9 10 Median = 3+Median The location of the median: Median = ranked value •Note that is not the value of the median, only the position of the median in the ranked data Rule 1: If the number of values in the data set is odd, the median is the middle ranked value Rule 2: If the number of values in the data set is even, the median is the mean (average) of the two middle ranked values 7 2 n +1 2 n +1+Mode • A measure of central tendency • Value that occurs most often (the most frequent) • Not affected by extreme values • Used for either numerical or categorical (nominal) data • Unlike mean and median, there may be no unique (single) mode for a given data set An example of no mode: An example of several modes: 8 0 1 2 3 4 5 6 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 Modes = 5 and 9+Quartiles Similar to the median, we find a quartile by determining the value in the appropriate position in the ranked data, where: First quartile position: Q1 = (n+1)/4 Second quartile position: Q2 = (n+1)/2 (the median) Third quartile position: Q3 = 3(n+1)/4 where n is the number of observed values (sample size) 9+Quartiles Use the following rules to calculate the quartiles: Rule 1 If the result is an integer, then the quartile is equal to the ranked value. For example, if the sample size is n = 7, the first quartile, Q1, is equal to the (7 + 1)/4 = 2, second-ranked value Rule 2 If the result is a fractional half (2.5, 4.5, etc.), then the quartile is equal to the mean of the corresponding ranked values. For example, if the sample size is n = 9, the first quartile, Q1, is equal to the (9 + 1)/4 = 2.5 ranked value, halfway between the second- and the third-ranked values Rule 3 If the result is neither an integer nor a fractional half, round the result to the nearest integer and select that ranked value. For example, if the sample size is n = 10, the first quartile, Q1, is equal to the (10 + 1)/4 = 2.75 ranked value. Round 2.75 to 3 and use the third-ranked value 10+Measures of Variation 11 e.g. same centre, different variation Variation Variance Standard Deviation Coefficient of Variation Range Interquartile Range Measures of variation gives information on the spread or variability of the data values+Range • Simplest measure of variation • Difference between the largest and smallest values in data set • Ignores the distribution of the data • Like the Mean, the Range is sensitive to outliers 12 Range = Xlargest – Xsmallest 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 Range = 14 - 1 = 13 Example:+Interquartile Range Like the Median, Q1 and Q3, the IQR is a resistant summary measure (resistant to the presence of extreme values) Eliminates outlier problems by using the interquartile range, as high- and low-valued observations are removed from calculations IQR = 3rd quartile – 1st quartile IQR = Q3 - Q1 13+Interquartile Range Example: Range = 200–10 = 190 (misleading) IQR = 60 – 30 = 30 14 Xminimum Q1 Q2 Q3 Xmaximum 25% 25% 25% 25% 10 30 45 60 200+Variance and Standard Deviation The Sample Variance – S2 • Measures average scatter around the mean • Units are also squared 15 n -1 (X X) S n i 1 2 i 2 ∑= − = Where: = sample mean n = sample size X i = ith value of the variable X X+Variance and Standard Deviation The Sample Standard Deviation – S • Most commonly used measure of variation • Shows variation about the mean • Has the same units as the original data 16 Where: = sample mean n = sample size X i = ith value of the variable X X n -1 (X X) S n i 1 2 ∑ i = − =+Variance and Standard Deviation Advantages •Each value in the data set is used in the calculation •Values far from the mean are given extra weight as deviations from the mean are squared Disadvantages •Sensitive to extreme values (outliers) •Measures of absolute variation not relative variation 17+Comparing Standard Deviations 18 Mean = 15.5 11 12 13 14 15 16 17 18 19 20 21 S = 3.338 11 12 13 14 15 16 17 18 19 20 21 Data B Data A Mean = 15.5 S = 0.926 11 12 13 14 15 16 17 18 19 20 21 Mean = 15.5 S = 4.567 Data C+Coefficient of Variation Measures relative variation •i.e. shows variation relative to mean Can be used to compare two or more sets of data measured in different units Always expressed as percentage (%) 19   = ⋅     S CV 100% X+Z Scores The difference between a given observation and the mean, divided by the standard deviation For example: • A Z score of 2.0 means that a value is 2.0 standard deviations from the mean • A Z score above 3.0 or below -3.0 is considered an outlier 20 − = X X Z S+Shape 21+Shape 22+Microsoft Excel Descriptive Statistics Output 23+3.2 Numerical Descriptive Measures for a Population • Population summary measures are called parameters • The population mean is the sum of the values in the population divided by the population size, N 24 N X X X X N N N i i + + + = = ∑=  1 1 2 µ+Population Variance and Standard Deviation 25 Population Variance: • the average of the squared deviations of values from the mean N (X μ) σ N 1 i 2 i 2 ∑= − = N (X μ) σ N 1 i 2 ∑ i = − = Population Standard Deviation: • shows variation about the mean • is the square root of the population variance • has the same units as the original data μ = population mean; N = population size; Xi = ith value of the variable X+The Empirical Rule If the data distribution is approximately bellshaped, then the interval: • contains about 68.26% of values of the population • contains about 95.44% of values of the population • contains about 99.73% of values of the population 26 μ ± 1σ μ ± 2σ μ ± 3σ+The Chebyshev Rule 27+3.3 Calculating Numerical Descriptive Measures from a Frequency Distribution Sometimes only a frequency distribution is available, not the raw data Use the midpoint of a class interval to approximate the values in that class where: n = number of values or sample size c = number of classes in the frequency distribution m j = midpoint of the jth class fj = number of values in the jth class 28 n m f X c j 1 ∑ j j = =+3.3 Calculating Numerical Descriptive Measures from a Frequency Distribution (cont) 29+3.4 Five-Number Summary and Box-and-Whisker Plot 30 Xminimum Q1 Q2 Q3 Xmaximum 25% 25% 25% 25% Minimum(Xsmallest) -- Q1 -- Median -- Q3 -- Maximum (Xlargest)+Five Number Summary 31+Distribution Shape and Box-andWhisker Plots 32+2.4 Cross Tabulations 33+2.4 Cross Tabulations 34+Side-by-Side Bar Charts 35+Side-by-Side Bar Charts 36+Side-by-Side Bar Charts 37+2.5 Scatter Diagrams and TimeSeries Plots Scatter diagrams are used to examine possible relationships between two numerical variables In a scatter diagram: •one variable is measured on the vertical axis (Y) •the other variable is measured on the horizontal axis (X) 38+Scatter Diagrams 39+Time-Series Plots A time-series plot is used to study patterns in the values of a variable over time In a time-series plot: •one variable is measured on the vertical axis •the time period is measured on the horizontal axis 40+Time-Series Plots 41+3.5 Covariance The covariance is a measure of the strength and direction of the linear relationship between two numerical variables (X and Y): As a covariance can have any value, it is difficult to use it as a measure of the relative strength of a linear relationship A better, and related, measure of the relative strength of a linear relationship is the Coefficient of Correlation, r 42 1 ( )( ) cov( , ) 1 n i i i X X Y Y X Y n = − − = − ∑+3.5 Coefficient of Correlation The coefficient of correlation measures the relative strength of a linear relationship between two numerical variables (X and Y) Values range from-1 (perfect negative) to +1 (perfect positive) 43+3.5 Coefficient of Correlation (cont) 44+3.5 Coefficient of Correlation - Calculation The sample coefficient of correlation is the sample covariance divided by the sample deviations of X and Y where: 45 SXSY cov(X,Y) r = n 1 (X X) S n i 1 2 i X − − = ∑= n 1 (X X)(Y Y) cov(X,Y) n i 1 i i − − − = ∑= n 1 (Y Y) S n i 1 2 i Y − − = ∑=+3.6 Pitfalls in Numerical Descriptive Measures and Ethical Issues Data analysis is objective •Should report the summary measures that best meet the assumptions about the data set Data interpretation is subjective •Should be done in fair, neutral and transparent manner •Should document both good and bad results •Results should be presented in a fair, objective and neutral manner •Should not use inappropriate summary measures to distort facts •Do not fail to report pertinent findings even if such findings do not support original argument 46