Course Title: Prepare financial forecasts and projections
Part B: Course Detail
Teaching Period: Term1 2008
Course Code: BAFI5122C
Course Title: Prepare financial forecasts and projections
School: 650T TAFE Business
Campus: City Campus
Program: C6072 - Advanced Diploma of Accounting
Course Contact : Gowri Raviganesh
Course Contact Phone: +61 3 9925 1337
Course Contact Email:firstname.lastname@example.org
Name and Contact Details of All Other Relevant Staff
- Phone: 61 3 9925 5464
- email: email@example.com
Nominal Hours: 70
Regardless of the mode of delivery, represent a guide to the relative teaching time and student effort required to successfully achieve a particular competency/module. This may include not only scheduled classes or workplace visits but also the amount of effort required to undertake, evaluate and complete all assessment requirements, including any non-classroom activities.
Pre-requisites and Co-requisites
This unit describes the functions involved in preparing financial forecasts and projections. It will also provide students with the skills and knowledge in the application of statistical analyses and processing of business data.
National Codes, Titles, Elements and Performance Criteria
National Element Code & Title:
FNSICORG517A Prepare financial forecasts and projections
Collect, consolidate, analyse and model data
<!--[if !supportLists]-->· <!--[endif]-->Data is reviewed to ensure consistency with actual results and mode l used.<o:p></o:p>
<!--[if !supportLists]-->· <!--[endif]-->Analysis results are documented in a clear and unambiguous way.<o:p></o:p>
<!--[if !supportLists]-->· <!--[endif]-->Data collected are reliable, valid, complete and comprehensive.<o:p></o:p>
<!--[if !supportLists]-->· <!--[endif]-->Processing is completed in accordance with established timetable.<o:p></o:p>
<!--[if !supportLists]-->· <!--[endif]-->Data are consolidated in a logical structured format that enables ready analysis.<o:p></o:p>
Document results and obtain approval
<!--[if !supportLists]-->· <!--[endif]-->Results are documented in a clear and understandable manner.<o:p></o:p>
<!--[if !supportLists]-->· <!--[endif]-->Documented results are in a format suit t meet needs of target users.<o:p></o:p>
<!--[if !supportLists]-->· <!--[endif]-->All approvals are obtained in accordance with management objectives, financial and company policies.<o:p></o:p>
<!--[if !supportLists]-->· <!--[endif]-->Results are distributed within timetable and according to company policy.<o:p></o:p>
Identify assumptions and parameters
<!--[if !supportLists]-->· <!--[endif]-->Business plans and exception reports are reviewed to identify and resolve conflicts in assumptions. <o:p></o:p>
<!--[if !supportLists]-->· <!--[endif]-->Assumptions and parameters are reviewed to ensure compliance with company policy and procedures<o:p></o:p>
<!--[if !supportLists]-->· <!--[endif]-->Current and historical financial reports are examined to establish trends.<o:p></o:p>
<!--[if !supportLists]-->· <!--[endif]--> External environment is examined to gain objective overview.<o:p></o:p><o:p></o:p>
Issue instructions and relevant aids for preparations of forecasts and projections
<!--[if !supportLists]-->· <!--[endif]-->Instructions issued are clear and unambiguous and comply with company format at to ensure ease of use and consistency of interpretation. <o:p></o:p>
<!--[if !supportLists]-->· <!--[endif]-->Types of business are identified to enable effective models to be selected.<o:p></o:p>
<!--[if !supportLists]-->· <!--[endif]-->Training is provided to ensure comprehensive understanding and effective use of financial models by users.<o:p></o:p>
Plan preparation timetable
<!--[if !supportLists]-->· <!--[endif]-->All critical milestones are identified to ensure financial forecasts and <o:p></o:p>
projections can be prepared within timeframes. <o:p></o:p>
<!--[if !supportLists]-->· <!--[endif]-->Business plans are reviewed to identified timeframe parameters used.<o:p></o:p>
<!--[if !supportLists]-->· <!--[endif]-->Business plans; financial forecasting and processing systems are reviewed to identify all potential conflicts.<o:p></o:p>
Please refer to Elements of Competency.
Details of Learning Activities
- An initial introduction to topics and techniques via a lecture will include the fundamentals and methods of business statistics.
- Students will participate in tutorial-based discussion groups that will enable them to relate topics to the environment, in which they live and work. They will be required to present their own thoughts, opinions and ideas. A range of learning team activities will assist them to develop and apply their knowledge in different situations that reflect tasks, which they might encounter in a business environment.
- A range of self directed learning activities would be provided for students to practice, reinforce and summarise the key learning.
- Students will be responsible for managing the progress of their self-directed learning. Students will work on self-directed learning activities (multiple choice & Internet based case study) independently of the lecturer.
|Week beginning||Topics /Assessment||Textbook References|
|Week 1 Feb 11||Introduction to Statistics, sampling and data collection||Chapter 7 & 8|
|Week 2 Feb 18||Organisation and Visual representation of data||Chapter 7 & 9|
|Week 3 Feb 25||Measures of Central tendencies||Chapter 10|
|Week 4 Mar 3||Measures of Dispersion||Chapter 11|
|Week 5 Mar 10||Probability and Normal Distribution||Chapter 12|
|Week 6 Mar 17||Estimation & Revision|
|Easter Break (Mar 20th to 26th)|
|Weel 7 Mar 31||Class Test (20%)|
|Week 8 Apr 7||Excel in lab|
|Week 9 Apr 14||Hypothesis Testing||Chapter 14|
|Week 10 Apr 21||Correlation and Regression||Chapter 16|
|Week 11 Apr 28||Correlation and Regression Excel in lab||Chapter 16|
|Week 12 Mar 5||Time series||Chapter 18|
|Week 13 May 12||Time Series, Excel in lab||Chapter 18|
|Week 14 May 19||Index numbers||Chapter 17|
|Week 15 May 26||
Revision, (Due date for Assignment 20%)
|Week 16 Jun 2||Revision|
|Week 17 Jun 9||Examination (60%)|
|Week 18 Jun 16||Course review and assessment feedback|
Greg Dickman, Financial Forecasting And Data Analysis. Thomson.
Levine etal, Statistics for Managers, 4th Edition.
Overview of Assessment
Assessment will incorporate a variety of methods including tests, assignments and an examination. Tests will be closed book consisting of multiple choice questions, problem solving exercises and applied exercises. The assignment will require students to undertake basic research on a business theme. Students working in groups will be required to analyse data using MS Excel 2003 and write reports as directed.
Students will receive ongoing feedback on their progress in the course. Feedback on assessments will be given in a timely manner. Students will be informed about how to improve their performance in the competency / course and what they will need to to to be deemed competent or to gain a pass in the assessment.
Assessment in this course will consist of 3 tasks:
- 1 Test (held in week 8)
In-class test worth 20 % will be held in week 8. Test will be closed book and one & ½ hour in duration. Formula sheet will be provided and only non-programmable calculators are allowed.
- 2 Group Assignment (due week 15)
Students need to source information from specified web sites. Analyse data using EXCEL “Data Analysis” tool pack and write reports. This is worth 20% and due in week 15.
- 3 Exam (During Exam period)
A closed book exam worth 60% covering all topics will be held during exam period.
Assessment will incorporate a variety of methods including written test, assignment and exam.
Students are assessed against all the performance elements of the course and must demonstrate an understanding of all elements to be deemed competent. To receive a pass for this course student must also pass the final exam and achieve at least 50% overall.
|FNSICORG517A/01 Plan Preparation Table||Y||Y||Y|
|FNSICORG517A/02 Identify assumption and Parameters||Y||Y||Y|
|FNSICORG517A/03 Issue Instructions and relevant aids for preparation of forecast and projection||Y||Y|
|FNSICORG517A/04 Collect, consolidate, model and analyse data||Y||Y|
|FNSICORG517A/05 Document Result and obtain approval||Y||Y||Y|
This course will enable students to achieve the following learning outcomes:
1. Organising, summarising and presenting business data using statistical tools.
- 1.1 Identify types of statistics that are common to business
- 1.2 Differences between Sample and Population
- 1.3 Introduction to sampling
- 1.4 Distinguish primary and secondary data
- 1.5 Explain why data is presented visually
- 1.6 Organising raw data into Frequency and grouped frequency distribution
- 1.7 Represent data tables graphically * Histograms * Polygons * Ogives * Bar chart * Pie chart
- 1.8 Calculate the measure of central tendencies (only for raw data) * Mean * Mode * Media
- 1.9 Identify the appropriate measure for a given situation
- 1.10 Discuss the significance of skewness of a dataCalculate the measure of dispersion (only for raw data) * Range * Inter-Quartile range * Standard deviation
- 1.11 Writing a brief Business report
- 1.12 Use of software (excel) for analysing data
2. Drawing inferences about population from sample statistics
- 2.1 Distinguish between a population and a sample
- 2.2 Describe the special features of a normal distribution
- 2.3 Understand and apply central limit theorem
- 2.4 Calculate point estimates and confidence interval for the population mean
- 2.5 Solve business problems that can be represented by a normal distribution
- 2.6 Understand the principles of statistical inference
- 2.7 Identifying research questions and Formulate hypothesis
- 2.8 Decision-making based on test statisticsUse of EXCEL
3.Measure the nature and degree of relationship between two variables and represent this relationship by a linear equation
- 3.1 Discussing examples where there is association between two variables
- 3.2 Understand dependent and independent variable
- 3.3 Draw and interpret a scatter diagram
- 3.4 Assess relationship with the help of correlation coefficient
- 3.5 Interpret of correlation coefficient
- 3.6 Understand linear regression
- 3.7 Establish linear relation between two variables using the method of Least squares Regression
- 3.8 Prediction based on linear regression equation
- 3.9Check the goodness of fit using coefficient of determinationUse of EXCEL
4.Observing data at specified time intervals and use it for forecasts.
- 4.1 Identify and interpret the four basic measures of variation in a time series analysis
- 4.2 Describe a time series and explain its use by giving example
- 4.3 Use common methods of fitting secular trend lines to time series (including semi averages, moving averages and least-squares)
- 4.4 Forecast using trendUse of EXCEL
5.Observing Prices of different items at specified time intervals and use it to measure the changes in retail price:
- 5.1 Use of simple, composite & weighted Price indices to measure the change in retail prices.
- 5.2 Special applications of the CPI
Course Overview: Access Course Overview