Demonstrate an Understanding of Financial and Managerial Analysis 10% Flashcards Preview

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Flashcards in Demonstrate an Understanding of Financial and Managerial Analysis 10% Deck (11):

Why do Government spend money?

Current operation
Capital Outlays ( Future)
Debt Services ( Present, Past and Future


Present value Analysis ( If the entity invested in it today what would it be worth in the future.)

P= principal today
F future value of the investment
R= Interest rate
n= Number of years the until the benefits would be received,

A dollar received in the future is worth today; that mount needed to be invested today that results in the dollar at the future time.


Future value analysis ( What a need save to date to have in the future)

P= principal today
F future value of the investment
R= Interest rate discount rate or opportunity cost
n= Number of years the until the benefits would be received,

Future value is the value of an asset at a specific date. It measures the nominal future sum of money that a given sum of money is "worth" at a specified time in the future assuming a certain interest rate, or more generally, rate of return; it is the present value multiplied by the accumulation function.


Payback Analysis

how long will it take to recover the amount invested in a new capital asset or project.
Example $10,000 Investment returns $5,000 per year it will take two years to receive the benefits or pay it back.

Opportunity Cost to invest was $60,000 per year and estimate generate revenue $100,000 from the investment. The payback would be 7.2 Months
60,000/100,000=.60 12 monthsX.60=7.2 months


Flowcharting ( Business Process Reengineering)

is technique that can be used to obtain or convey and understanding of the process.


Earned Value Management

Earned value management is a project management technique for measuring project performance and progress. It has the ability to combine measurements of the project management triangle: scope, time, and costs.

In a single integrated system, earned value management is able to provide accurate forecasts of project performance problems, which is an important contribution for project management
Essential features of any EVM implementation include:
A project plan that identifies work to be accomplished
A valuation of planned work, called planned value (PV) or budgeted cost of work scheduled (BCWS)
Pre-defined "earning rules" (also called metrics) to quantify the accomplishment of work, called earned value (EV) or budgeted cost of work performed (BCWP)

EVM implementations for large or complex projects include many more features, such as indicators and forecasts of cost performance (over budget or under budget) and schedule performance (behind schedule or ahead of schedule). However, the most basic requirement of an EVM system is that it quantifies progress using PV and EV.

Application example[edit]

Project A has been approved for a duration of one year and with the budget of X. It was also planned that the project spends 50% of the approved budget in the first six months. If now, six months after the start of the project, a project manager would report that he has spent 50% of the budget, one can initially think, that the project is perfectly on plan. However, in reality the provided information is not sufficient to come to such a conclusion. The project can spend 50% of the budget, whilst finishing only 25% of the work, which would mean the project is not doing well; or the project can spend 50% of the budget, whilst completing 75% of the work, which would mean that project is doing better than planned. EVM is meant to address such and similar issues.


Regression Analysis

regression analysis is a set of statistical processes for estimating the relationships among variables. It includes many techniques for modeling and analyzing several variables, when the focus is on the relationship between a dependent variable and one or more independent variables (or 'predictors').


Data Analysis

Analysis of data is a process of inspecting, cleaning, transforming, and modeling data with the goal of discovering useful information, suggesting conclusions, and supporting decision-making. Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names, in different business, science, and social science domains.


Data Mining

Data mining is the process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems. It is an essential process where intelligent methods are applied to extract data patterns.


Predictive Analytics

Predictive analytics encompasses a variety of statistical techniques from predictive modelling, machine learning, and data mining that analyze current and historical facts to make predictions about future or otherwise unknown events.[1][2]

In business, predictive models exploit patterns found in historical and transactional data to identify risks and opportunities. Models capture relationships among many factors to allow assessment of risk or potential associated with a particular set of conditions, guiding decision making for candidate transactions.[3]


Benford Digital Analysis

Benford's law, also called Newcomb-Benford's law and first-digit law, is an observation about the frequency distribution of leading digits in many real-life sets of numerical data. The law states that in many naturally occurring collections of numbers, the leading significant digit is likely to be small.[1] For example, in sets that obey the law, the number 1 appears as the most significant digit about 30% of the time, while 9 appears as the most significant digit less than 5% of the time. By contrast, if the digits were distributed uniformly, they would each occur about 11.1% of the time.[2] Benford's law also makes predictions about the distribution of second digits, third digits, digit combinations, and so on.