Refer to the WSJ article attached here, and answer the following questions.

Investing In Funds & ETFs: A Monthly Analysis --- Fundamentals of Investing: What Does the Trend Line Say About Stocks? Not Much --- Market forecasts depend a lot on what historical period is highlighted

Hulbert, Mark.Wall Street Journal, Eastern edition; New York, N.Y. [New York, N.Y]10 Dec 2018: R.3.

IN THE WORLD of stocks, the past is a funny thing. It doesn't really tell us much about the future. Yet most investors, market professionals and amateurs alike operate as if it does.

If the pace of stocks' growth were predictable, as the widespread study of market trends and past performance assumes it to be, one might reasonably conclude that the S&P 500 today should be 95% higher than it currently is, based on a trend line that begins with the index's 1932 low and is drawn through its March 2000 high. Such a line suggests that the stock market is significantly undervalued.

But one could just as easily draw a trend line based on the high in 1929 and the low in 2009, and that trend line is 58% lower than where the S&P stands today. For anyone drawing conclusions based on this second trend line, it's "look out below."

Of course, neither such outlook -- rosy or doom-laden -- seems particularly warranted by current market conditions. The truth is, though both trend lines are based on a common forecasting technique that extrapolates into the future the annualized return of the S&P 500's inflation-adjusted values between two points, in reality, any number of different forecasts could be drawn depending on what two points are used to define the trend line.

So what is an investor to do?

We need to give up the notion that history speaks with one voice about what the future has in store.

To be sure, both of these trend lines were cherry-picked to maximize or minimize stocks' historical return. But we're fooling ourselves if we think that we can avoid being at least somewhat subjective when picking the start and end dates of whatever database is used to draw the lessons of history, according to Edward McQuarrie, a professor emeritus at the Leavey School of Business at Santa Clara University who has devoted himself to reconstructing U.S. stock-market history.

This is even true of the academic databases on which researchers rely, whose start dates are "accidental and arbitrary," says Prof. McQuarrie.

Consider the stock-pricing database that is perhaps the most widely used both by academics and by numerous researchers on Wall Street: the one maintained by the Center for Research in Security Prices, or CRSP, at the University of Chicago, which dates back to 1926. This database reflects New York Stock Exchange-listed stocks back to the mid-1920s; American Stock Exchange stocks starting in 1962; and Nasdaq stocks from 1972 on. The famous Ibbotson yearbook "Stocks, Bonds, Bills, and Inflation" is based on this data set. When the CRSP database was set up in the 1960s, its creators could have chosen an even earlier starting point. They had access to stock prices going back decades further, but "the sample felt big enough, and 1926 wasn't an obviously bad start date," so they stopped there, Prof. McQuarrie says.

One consequence of the 1926 start date, however, is it doesn't reflect the devastatingly poor performance of stocks during World War I. The stock market on a price-only, inflation-adjusted basis fell by almost as much in the 1910s as it did in the 1929 crash and its aftermath. Investors who focus on just the CRSP/Ibbotson database would never know there was a 30-year period that suffered two separate declines each in excess of 75%.

In fact, it wasn't until 1984 that the S&P 500 on an inflation-adjusted basis rose for good above the level at which its predecessor index stood in late 1909. Given today's low dividend yields, it has to be a sobering thought indeed that there could be a 75-year period in which, after inflation, all equity investors would have to show for their risk-taking is the dividends they received along the way.

To be sure, there are some who believe that, because today's dividend yields are so low, stock prices in coming years will rise at a faster pace than history would otherwise suggest. Perhaps the best-known proponent of this argument is Jeremy Siegel, a finance professor at the University of Pennsylvania's Wharton School and author of "Stocks for the Long Run." Prof. Siegel's theory is that companies will invest in growth with the cash they otherwise don't pay out as dividends, leading to a faster pace of earnings growth. Supporting his argument is the fast pace of the S&P 500's price-only inflation-adjusted return over the past three decades of unusually low dividend yields.

There are many who question this narrative, however. Cliff Asness, managing and founding principal at AQR Capital Management, says in an email that research he conducted with Research Affiliates founder and chairman Robert Arnott found that companies' earnings growth rates are actually lower, on average, when their dividend yields are low. He adds that the S&P 500's rapid price-only appreciation over the past couple of decades hardly settles the matter, since his research found the contrary over nearly a century.

You don't have to take sides in this debate to see the monkey wrench this throws into efforts to extrapolate the past into the future.

Prof. McQuarrie says that because investors tend to extrapolate the immediate past into the indefinite future, one of the most helpful roles stock-market historians can play is to "cherry-pick historical periods with outcomes maximally discrepant from those which investors have recently experienced."

And right now that means pointing out that the stock market in the past has endured 30-year periods in which it failed to even keep pace with inflation.

1. Describe the importance of forecasting in operations. How is an accurate forecast beneficial? Why is it important to know how a forecast might be wrong and how likely it is to be wrong?

2. What is the role of understanding "standard deviation" when determining a forecast value? How does the standard deviation influence the confidence you can place in the forecast?

3. Forecasts are estimates that are typically incorrect. How would you address forecast accuracy? What levels of error are acceptable? How do you incorporate the natural variations present in real-world data when making and interpreting forecasts? What levels of forecasted change are required to influence your management team to make a process change?