Tuesday, October 30, 2007

Forecasting: Linear Regression

I am not sure how effective I was at explaining the use of trend projection. However, since linear regression is an extension of the same topic within my forecasting series, hopefully I am able to explain the concept a little more clearly.

Linear regression is the art of drawing a line to represent the relationship between points plotted between two axis. This line is the smallest sum of squares between all of the points and this line can be projected forward to help make predictions and educated guesses. In the case of trend projection, an analyst is looking at the data value, typically demand or sales over time. However, linear relationships can be drawn without using time as one of the axis. Consider a relationship drawn between sales and payroll. If pay is based on a commission, a positive linear relationship could show how a higher commission may lead to greater sales. This is just one example.
Another would be how sales might increase as advertising increases. An analyst could predict a certain level of sales based on a specified level of advertising by using this model.

Now as I mentioned in my previous post, predictions in this world are never 100% accurate. However, by using the Standard Error of the estimate, also known as the standard deviation about the line, one can predict the probability that a forecasted value will land within a particular range of the predicted value.

I hope that I haven't lost too many of my readers trying to understand this. I will not try to explain the statistics of this any further. Instead, I will direct you to a wonderful free online statistics resource http://onlinestatbook.com/.

Well, thanks for reading. The next part of this series will be in how to track and follow a forecast.
----Sincerely, Trevor Stasik.

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To visit the next post in the forecasting series, click HERE.

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Monday, October 29, 2007

Forecasting: Trend Projections

I'm back. Hope things have gone well for all of you. I made significant progress on all of my exams (still ongoing) and class projects (also, still ongoing). Anyway, I promised you a new entry in my forecasting series, so today we are going to look at Trend Projections. With it, you can see if your sales are likely to continue upward, or if they are falling over a cliff.

When you have a series of past data, you can plot them on a graph. Then, using the least squares method, you can find a line that will best fit those observations. Draw that line out into the future. Technically, the least squares methods finds the line in which there is the smallest sum of squares in the absolute difference between all of the data points to a particular linear model. If you want more information about it, you can read about it over at this MathWorld link HERE.

The linear model follows that same linear model you probably learned back in Junior High School:
       y=bx + a
       with a=the y-intercept
       x=independent variable of time.
       y is the value that we want to predict.

So after you draw out your line, you can predict what the most likely plot will be along that line. Now, of course real life and real sales doesn't always occur in a linear fashion. To help deal with that, you could figure out what the standard error of the estimate is. Simply put, that's just creating a bell curve about the next projected plotted point. This will tell us the likelihood that our next period demand or sales will fall in a particular range of values, and forms a sort of band about the linear trend projection.

Hopefully, I've given you a good starting look into the use of trend projection. I intend to look more into linear regression and standard errors one more time in tomorrow's blog post. Statistics isn't my strongest area, but I will try to explain it to the best of my ability. See you then.
--------Sincerely, Trevor Stasik.

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Saturday, October 20, 2007

Trevor Stasik Produces FMA Video With Peter Sayre From Prudential Financial

I will be back officially from my hiatus on October 29th. However, I thought I would take a moment to post this. I'm the VP of Communications for the Financial Management Association at Temple University. One of the things I've been pushing to add this semester are video segments. I think of it as a win-win situation for Temple, the FMA, and the Corporate Sponsor. Temple gets to show that they are "more connected" into technology, the FMA can attract more student members and corporate sponsors with video, and the corporate sponsor get to reach potential new recruits.

Well, I had a chance to sit down with Peter Sayre, the Senior VP for Prudential Financial and Corporate Controller to ask him a few question. I took the best responses and edited them into the short interview video you see at the top.

I'm still working through assignments, projects, and midterms in school. I should return to more frequent posting after Oct. 29th. Thanks for visiting.

----------Sincerely, Trevor Stasik.
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Saturday, October 13, 2007

Measuring Forecasting Error

Forecasting error can tell us how accurately we predicted the future values. This is the formula we can use in calculating the error present in our models.
MAD stands for "Mean Absolute Deviation". Comparing the actual values to the forecasts can allow us to see how well we are doing in executing sales or production strategies, among other things. However, having just the deviation value can be confusing, especially when dealing with large numbers over various product & service classes. How can we make this error value more useful? We do this by finding the error in terms of percentages. Here is the formula we'll want to use:
MAPE stands for "Mean Absolute Percent Error". Now, lets take a quick look at at error calculations for a made up set of data. We are looking for the error in forecasting for MP3 Players sold.

This is just a quick example of how you could calculate error. I'm not going to do this now, but with Excel or other graphing software you could even make a visual representation of your error to track it over time. This may ultimately help an analyst or project determine the usefulness of their forecasting models.

The next part of the series will be trend adjustments in your forecasts. I look forward to seeing you then. Thanks for reading.
----Sincerely, Trevor Stasik.

To return to initial post about forecasting, click HERE.
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Thursday, October 11, 2007

More sleep may help your career

When I was in the military, a brave soldier by the name of Rob Blackwood taught me about the importance of sleep. Sleep can refresh the mind. A crisp and new mind will retain facts and figures far better than a tired one. If you want to look a little brighter and more intelligent in that boardroom meeting tomorrow, trying going to bed an hour earlier if you can and waking up a little earlier too.

See this goofy video about sleep and your career that I found online:

This is just a short tip for tonight, because I am going to bed. I should be back by Saturday with another part in my forecasting series.
Thanks for visiting and goodnight.
------------Sincerely, Trevor Stasik.

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Tuesday, October 9, 2007

Time-Series Forecasting: Exponential Smoothing Part 2

Like smooth jazz floating gracefully out of a sweet saxaphone, the forecasting series continues today with part II of exponential smoothing. Previously we looked at the equation which will help us find the projected sales or demand for the following period.

That equation is:The formula for smoothing is:

Let's see how that would work in action, shall we. Consider this simple data set with the demand for Oranges:
Oranges sold (actual):
Jan 7
Feb 5
Mar 10
Apr 14
May 11
Jun 12
Next we will want to find out what the 2 month moving average of each of these are. This will provide us with a forecasted value.

Oranges sold (actual):2 mo avg
Jan 7 
Feb 5 
Apr 147.5
May 1112
Jun 1212.5

Now that we know our 2 month moving averages, we can use those as our original forecasts. Now we can apply a smoothing constant. If we use a smoothing constant of .8, inserting it into the equation above, we can see how our forecast is affected. Note that January, February, and March are not able to do an forecast using "exponential smoothing" since we do not have a previous period forecast to work from.

Oranges sold (actual):2 mo avg
Smoothing Constant
EquationNew Forecast
Feb 5   
Jun 1212.512+0.8(11-12)11.2
Ultimately, the end result of this will be a smoother and hopefully more accurate forecast. We only looked at a few months, but in reality these forecasts would continue on as long as necessary.

This will move us into the the next part in my series: Forecasting Errors. Using the mean absolute deviation, we should be able to compare the accuracy of forecasts. Thanks for visiting and I will see you next time.
-----Sincerely, Trevor Stasik.

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Monday, October 8, 2007

Yum Brands profit jumped 17%

I'm not going to have time today to look further into exponential smoothing. I just have too many school assignments and projects on my plate. I will try to add the next segment of my forecasting series tomorrow.

In the meantime, take a look at Yum Brands. What were they doing right?! Apparently it was mostly due to sales overseas. Sales in China alone rose 11%. That tells me that the foreign diet is changing, even more than before. I'm sure many of us are aware of the growth experienced by other restaurant service companies in China such as McDonalds and Starbucks. Perhaps this is a trend that will continue into other south Asian markets. This could be an interesting area that people should investigate for their investment dollars, rupies, yen, or other currencies. Fast food could equal fast money.

Okay, sorry for such a short post but my homework and class projects need to be my priority right now. Have a good night. ----Sincerely, Trevor Stasik

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Friday, October 5, 2007

Time-Series Forecasting: Exponential Smoothing Part 1

We have already looked at the standard moving average and the weighted moving average. However, there is another forecasting tool that an analyst or project manager can use. It is both simple and sophisticated. It is known as Exponential Smoothing.

Using only the most recent data, you can create a forecast for the next month's demand or sales. This method places an exponentially decreasing weight on each sequential piece of older data. With a series of data, you can see how the exponential smoothing really "smoothes" the results so that there are fewer noticeable spikes and dips.

The formula for smoothing is:

Now the tricky part is determining what the smoothing constant (alpha) should be. A table could be a handy tool to help. The constant used for the most recent period would be just alpha. NOTE: I'm going to use a little "a" to represent alpha, because I'm not sure how to make that symbol out of HTML.
For the 2nd most recent period, your weight should be: a(1-a).
Then for the third most recent period, the weight would be: a(1-a)^2.
The fourth most recent period would have a weight of: a(1-a)^3.
The weights would continue like this.
All the weighted values are then summed to give you a forecast.

I know it's a little confusing. However, in my next post in this forecasting series I plan to do a sample problem with exponential smoothing. That may make it easier to understand. If somebody reading this has a better explanation of exponential smoothing, drop me a comment. Your contribution would be welcomed.

Thanks for visiting. -------Sincerely, Trevor Stasik.

To return to initial post about forecasting, click HERE.
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Enterprise Rent a Car Visits the FMA

We will come back to forecasting. For today, I want to give you the chance to see a short video I made for the The Temple FMA. This features Allison James and David Mischel discussing opportunities for graduating students with Enterprise Rent-a-Car:


Thanks for visiting.

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Thursday, October 4, 2007

Forecasting: Weighted Moving Averages

In my previous entry, I discussed the importance of a moving average and how it can be found. The moving average is an excellent tool at smoothing out all of the spikes and dips that actually occur in sales and demand. This can hopefully help us achieve a more accurate and more consistent forecast. However, once you begin to think about it, isn't more recent data also more relevant data. The recent data may (but not always) be more accurate when looking to forecast future trends. Therefore, it sometimes makes sense to add a weight to various data when calculating your moving averages.

In my last blog post I introduced the Weighted Moving Average and left you with this equation to think about:
Now lets consider how to use this equation in practice. We will be using the data I collected last time about unit sales of Batman comic books in 2006 and adding weights ranging from 1 to 3 for a 3 month average, or 1 to 4 for a 4 month average. I've given the more recent comics a higher weight. Remember that the denominator of our equation is going to be the sum of the weights for only the months we are looking at. Now we apply the equation as a formula in Excel to the first cell for the 3 month weighted moving average.

Then we can continue applying it for the entire run of 3 month data. I also filled in the WMA for the 4 month data while I was at it.

The next thing we will do is apply that data to a line graph. This will allow us to see how our data compares against the moving averages.

So, knowing all of this, we can take the data we have been given and forecast what the sales will be in January of 2007. Under the 3 month moving average, I predict that DC Comics will sell 84425 units of the Batman comic book. If we instead chose to use the 4 month moving average, we would come up with a value of 85988.

This simple method of Weighted Moving Averages may be better than a standard moving average, but it could still be adjusted to make a more accurate and useful time series model. The next part of my forecasting series will deal with exponential smoothing inside a moving average.

Thanks for visiting. ---------Sincerely, Trevor Stasik.

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Tuesday, October 2, 2007

Quantitative Methods Of Forecasting: Moving Averages

Once demand and sales are understood to be dynamic and not static, different methods of forecasting are needed. One such method is the moving average.

The moving average is used as a way to smooth forecasts into a more likely model. Consider the PDA example from the previous post. In that post we discussed how sales of PDAs from last year have dropped by 40%. (Read article HERE). Well, with the use of a moving average, the forecasted sales for the coming time period will not be lowered by the same amount as the previous periods loss in sales. Perhaps the actual equation for moving averages would be helpful here:

Using this equation, you can find the moving average. This can be spaced over various time periods. You can look at a moving average over days, months, quarters, or years. Consider that many technical analysts in the stock market use a 200 Day Moving Average in helping them consider whether to make investments. In sales, often the number of units sold monthly can be a demand indicator.

As an example, lets look at sales data for Batman comic books over 2006. I found the unit sales data for each month and plugged it into Excel so it could do most of the hard work for me. I've included the source of my data next to each month:

I want to look at the 3-month moving average. We should now apply the formula to the first month.

This can be continued by pulling down on the corner of the cell. I continued this with the 4 month moving average:

Finally, lets put our results into a visual form, the line graph:

As you can see, the sales jumped in July. However, due to smoothing created by the use of a moving average, we can find a trend that will help us predict future sales (assuming that the market remains fairly steady). Once we have a long enough pattern, we may be able to apply a weight to the results to provide an even more accurate trend for forecasting. The formula for figuring out the weights is as follows:

I do not have enough time to do an excel example of the weighted moving average today. I think I'm going to stretch this into another blog entry dedicated to the weighted moving average. I hope my information has been helpful. Thank you for visiting and please drop me a comment.
------------Sincerely, Trevor Stasik.

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Monday, October 1, 2007

Trevor Stasik: Resume of a Professional in Finance

This is my extended professional resume. I thought that I should post it here so my readers have a better understanding about my background.

Trevor Stasik

1148 Neshaminy Valley Drive
Bensalem, PA 19020
tel: 215.833.6384

TEMPLE UNIVERSITY, Fox School of Business, Philadelphia, PA
Bachelor of Business Administration, May 2008
Primary Major: Finance
Second Major: Film and Media Arts
GPA: 3.73

EXCELSIOR COLLEGE, (formerly known as Regents College), Albany, NY
Associates Of Applied Science, 2000
Specialization in Nuclear Technology

FINANCIAL MANAGEMENT ASSOCIATION, Student Organization, 2006 - Present
Vice-President of Communications, 2007
• Lead team in successfully publishing newsletters, upgrading website at www.temple-fma.org, distributing email to over 200 members, and marketing to student body.
• Received the Most Valuable Member Award for FMA.
Newsletter Director, 2006

OFFICEMAX, Fairless Hills, PA (Varied full time and part time) July 2003 - Present
Print and Document Services Associate
• Coordinate reprographic, computer graphic design, and other business media services for individuals and small business clients at an office supplies retailer with $5 - 10k in weekly departmental sales.
• Work closely with clients and external vendors on business card and stationary design, operation of 8 printer/scanners, a hydraulic cutter, a folding machine, binding machine, and troubleshooting equipment repairs and maintenance.
• Placed over a thousand DHL orders manually and via computer, tracking packages, and successfully ensuring that all orders reached their proper destinations.

UNITED STATES NAVY, Carl Vinson CVN-70 July 1996 - July 2002
Nuclear Electrician, Petty Officer 3rd Class
• Maintained and operated the four steam driven electric generators and four motor generators on the Aircraft Carrier Carl Vinson, which provided the power for the ship.
• Maintenance included monthly inspections, extensive log keeping, intricate repairs and laborious carbon brush changes.
• Trained and oriented up to 20 Electrician Mates on control panel operation of the generators, 4160 volt circuit breakers, backup diesel generator operations, and emergency reactor shutdown procedures.
• Monitored and maintained 16 salinity chemistry and salinity safety alarm modules including the Steam Generator High level/Low level alarms resulting in the 100% safe operation of the Nuclear Reactor.

• Microsoft Office Suite (including MS Access)
• Microsoft Publisher
• Adobe Photoshop

• Wrote and Produced Short Film “Quitting Is For Losers”,
shown at 2005 Coatesville, PA Film Festival.
• Produced corporate video interviews for the Financial Management Association at Temple University
• Worked on numerous student and independent films while at school.

If anyone has any questions about my experiences, please send me an email.
If you have any comments about how to improve my resume, please leave me a comment so that all of my readers can benefit from your tips and advice.
Thanks for visiting. ----------Sincerely, Trevor Stasik

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