(Weiss, 2004) For me, statistics are everywhere around us in this world. We face a lot of situation which involves statistics and more particularly descriptive statistics in our lives. In fact this interaction with statistics is so frequent that it is often very difficult to spend even hours of our lives without having to look at statistics or descriptive statistics. The reason behind statistics being all around us, confines in the daily activities or routine tasks, which we go through. For example, suppose we are watching a match involving our favorite NBA team, Lakers. Before the match we are going to make some inferences. These inferences will be based on the basis of statistics are more importantly descriptive statistics. For example, to say Lakers is going to win will be based on certain variables that both the Lakers and the opposing team possess. For example, our inferences could be based on the fact that on average, Lakers is scoring more baskets than the opposing team. Similarly it can be on the basis that the players of Lakers such as Bryant and Gosol are taller than the players of opposing team. It could be any reason based on the variable between the two teams. If we define variable, it is used to define certain characteristics that differ from one person to another or from one entity to another. This is how we can use statistics as tool to make inferences in our daily life and gives us a little idea about how important statistics can be.
From the above example, it has become really clear that statistics play vital role in our daily lives. The above example might not affect our lives greatly but think about certain situation where using the techniques of descriptive statistics can make our lives better, if we apply the right techniques to a data and make inferences which might benefit us. Let's now take an example of a company which trades stocks in NYSE. Suppose that the market is bullish and shares prices are rising. This increase could be graphed as the following diagram:
Date: 28 September 2009
The above diagram shows the data of NYSE index on a 28 September 2009. From the above data, we can see that the market has reached peak at 1'o'clock. By the use of descriptive statistics and comparing the data of several days, we may come to a conclusion that NYSE reaches its peak each day at 1'o'clock and can disburse all the investment before 1'o'clock to make huge profits. This will create a motivation for the company to invest in the shares by large amount to maximize its profits. However, suppose that the market has a trend that after reaching a certain point it goes down or loses its value. (Orr, 1995) If this trend line or line of best fit, which is a part of descriptive statistics, is known, it will provide this company comprehendible information about not only the timings and compositions of optimum investment in NYSE, but will also tell the company when to divest from the market, before the market indices start to fall. This is how giant brokers operating New York Stock Exchange make the use of descri