Statistics for management
Statistics is the formal science of making effective use of numerical data relating to groups of individuals or experiments. It deals with all aspects of this, including not only the collection, analysis and interpretation of such data, but also the planning of the collection of data, in terms of the design of surveys and experiments.[1]
A statistician is someone who is particularly well versed in the ways of thinking necessary for the successful application of statistical analysis. Often such people have gained this experience after starting work in any of a number of fields. There is also a discipline called mathematical statistics, which is concerned with the theoretical basis of the subject.
The word statistics can either be singular or plural.[2] When it refers to the discipline, "statistics" is singular, as in "Statistics is an art." When it refers to quantities (such as mean and median) calculated from a set of data,[3] statistics is plural, as in "These statistics are misleading."
Statistics is the formal science of making effective use of numerical data relating to groups of individuals or experiments. It deals with all aspects of this, including not only the collection, analysis and interpretation of such data, but also the planning of the collection of data, in terms of the design of surveys and experiments.[1]
A statistician is someone who is particularly well versed in the ways of thinking necessary for the successful application of statistical analysis. Often such people have gained this experience after starting work in any of a number of fields. There is also a discipline called mathematical statistics, which is concerned with the theoretical basis of the subject.
The word statistics can either be singular or plural.[2] When it refers to the discipline, "statistics" is singular, as in "Statistics is an art." When it refers to quantities (such as mean and median) calculated from a set of data,[3] statistics is plural, as in "These statistics are misleading."
Statistical Forecasting
Statistical forecasting concentrates on using the past to predict the future by identifying trends, patterns and business drives within the data to develop a forecast. This forecast is referred to as a statistical forecast because it uses mathematical formulas to identify the patterns and trends while testing the results for mathematical reasonableness and confidence.
Here below are information on topics related to the field of forecasting and statistics. You will also find topics related to forecasting in business.
Budget Forecasting
A good cash flow forecast, also called a cash flow budget, is at the core of the corporate financial process and is important for corporate survival. How can you get somewhere if you don't have a map to follow? How can you ensure that you will have the financial resources available to fund your company's growth or to just "make payroll" if you don't plan out the cash receipts and disbursements for the week, month, and year? You can't!
Your cash flow budget doesn't have to be intricate to be effective. You can use a spreadsheet, purchase a simple budgeting program, or even do a forecast by hand. The important thing is that you have one.
To create one, use your financial or income statement monthly forecast and a calendar year for financial reporting, and do the following:
Outline the expected collections from your budgeted monthly invoicing. If your terms are net 30 and your clients typically pay in 45 days, use this fact as your basis for forecasts. For example, under that scenario, March's invoices become May's collections.
For the first months of the year, add in when you expect to collect existing accounts receivable. If you have $20,000 in accounts receivable that were all invoiced in December of the prior year, then, based on the above assumption, the $20,000 should be added as projected cash inflow for the second month of your budget, which is February.
Identify any other expected cash receipts. In your cash receipts forecast, include proceeds from bank loans or equity transactions, refunds, and customer deposits.
Start looking at expenses and cash disbursements. Look at your expenses for the prior and current months and identify when they will be paid. Items such as payroll, rent, leases, travel, and entertainment are either recurring or paid out in the current budget month. Also, identify what fixed asset purchases and loan repayments you will make during the year.
Review your accounts payable balance at the end of December for the prior year, and identify when these items will be paid. Add the amounts to your cash disbursements forecast.
Statistical Forecasting Method
Multiple Regression Analysis: Used when two or more independent factors are involved-widely used for intermediate term forecasting. Used to assess which factors to include and which to exclude. Can be used to develop alternate models with different factors.
Nonlinear Regression: Does not assume a linear relationship between variables-frequently used when time is the independent variable.
Trend Analysis: Uses linear and nonlinear regression with time as the explanatory variable-used where pattern over time.
Decomposition Analysis: Used to identify several patterns that appear simultaneously in a time series-time consuming each time it is used-also used to deseasonalize a series
Moving Average Analysis: Simple Moving Averages-forecasts future values based on a weighted average of past values-easy to update.
Weighted Moving Averages: Very powerful and economical. They are widely used where repeated forecasts required-uses methods like sum-of-the-digits and trend adjustment methods.
Adaptive Filtering: A type of moving average which includes a method of learning from past errors-can respond to changes in the relative importance of trend, seasonal, and random factors.
Exponential Smoothing: A moving average form of time series forecasting-efficient to use with seasonal patterns- easy to adjust for past errors-easy to prepare follow-on forecasts-ideal for situations where many forecasts must be prepared-several different forms are used depending on presence of trend or cyclical variations.
Hodrick-Prescott Filter: This is a smoothing mechanism used to obtain a long term trend component in a time series. It is a way to decompose a given series into stationary and nonstationary components in such a way that there sum of squares of the series from the nonstationary component is minimum with a penalty on changes to the derivatives of the nonstationary component.
Modeling and Simulation: Model describes situation through series of equations-allows testing of impact of changes in various factors-substantially more time-consuming to construct-generally requires user programming or purchase of packages such as SIMSCRIPT. Can be very powerful in developing and testing strategies otherwise non-evident.
Certainty models give only most likely outcome-advanced spreadsheets can be utilized to do "what if" analysis-often done e.g.; with computer-based spreadsheets.
Probabilistic Models Use Monte Carlo simulation techniques to deal with uncertainty-gives a range of possible outcomes for each set of events.
Forecasting error: All forecasting models have either an implicit or explicit error structure, where error is defined as the difference between the model prediction and the "true" value. Additionally, many data snooping methodologies within the field of statistics need to be applied to data supplied to a forecasting model. Also, diagnostic checking, as defined within the field of statistics, is required for any model which uses data.
Using any method for forecasting one must use a performance measure to assess the quality of the method. Mean Absolute Deviation (MAD), and Variance are the most useful measures. However, MAD doesn't lend itself to further use making inferences, but that the standard error does. For the error analysis purposes variance is preferred since variances of independent (uncorrelated) errors are additive. MAD is not additive.