MNS Corporation\'s Activities show the following information: Revenues: Budgets

Question

MNS Corporation's Activities show the following information: Revenues: Budgets $ 1,000, 000 Expenses: Budget $ 800, 000 Seven months later, financial records show that IT expenses were 6,000, compared to $ 4,500 as planned. Revenues through the seventh month was $ 450, 000 and expenses were $ 600, 000 What should be the Year end forecast and what is the forecast error, if any? What is Durbin - Watson model and how can it help business? Define Exponential Smoothing. In what situations would we apply this approach? Discuss. Distinguish between MAD & MSD. How can we apply each to solve a problem?

Explanation / Answer

Question no: 7

For many companies, the end of summer signals the beginning of the corporate budget season. Since sales drives profitability, revenue forecasting is a critical part of producing an accurate budget. Many marketers regard the sales forecast element in budgeting to be the bean counters’ responsibility.

Since these forecasts are likely to become your marketing sales goals, get involved in the budgeting process. While the timing and success of promotions may vary from plan, attracting management attention, the greater challenge marketers face is management pressure to budget overly aggressive sales targets without historic basis or regard for uncontrollable outside factors, such as changes in technology, competitors’ strategies or market demand.

Developing a revenue forecast based on a solid foundation allows you to make explicit your product’s, brand’s and/or company’s revenue dynamics and plan more effective marketing. Consider the environment in which you operate as well as related factors that could affect your ability to attract customers and complete transactions.

Question no: 8

The Durbin Watson statistic is a number that tests for autocorrelation in the residuals from a statistical regression analysis. The Durbin-Watson statistic is always between 0 and 4. A value of 2 means that there is no autocorrelation in the sample. Values approaching 0 indicate positive autocorrelation and values toward 4 indicate negative autocorrelation.

How Durbin- Watson Model can help business:

Autocorrelation can be a significant problem in analyzing historical pricing information if one does not know to look out for it. For instance, since stock prices tend not to change too radically from one day to another, the prices from one day to the next could potentially be highly correlated, even though there is little useful information in this observation. In order to avoid autocorrelation issues, the easiest solution in finance is to simply convert a series of historical prices into a series of percentage-price changes from day to day.

Question no: 9

Exponential smoothing is a rule of thumb technique for smoothing time series data, particularly for recursively applying as many as three low-pass filters with exponential window functions. Such techniques have broad application that is not intended to be strictly accurate or reliable for every situation. It is an easily learned and easily applied procedure for approximately calculating or recalling some value, or for making some determination based on prior assumptions by the user, such as seasonality.

Exponential smoothing is commonly applied to smooth data, as many window functions are in signal processing, acting as low-pass filters to remove high frequency noise.

Question no: 10

Mean absolute deviation (MAD)

Expresses accuracy in the same units as the data, which helps conceptualize the amount of error. Outliers have less of an effect on MAD than on MSD. The equation is:

where yt equals the actual value, equals the fitted value, and n equals the number of observations.

Mean squared deviation (MSD)

A commonly-used measure of accuracy of fitted time series values. Outliers have a greater effect on MSD than on MAD. The equation is:

where yt equals the actual value, equals the forecast value, and n equals the number of forecasts.

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