Qrb/501 Quantitative Reasoning Business: The Intro Answer

¶ … QRB/501 Quantitative Reasoning Business: The intro answer questions: How convert data index? Give examples Why people index (indices) explain data? What time-series data? How time-series data index forecast inventory year? Please ALSO write a conclusion based answers You HAVE TO USE PRESENT TENSE ONLY -- I MEAN ONLY.

Converting data into indexes is essential in business statistics. Raw data is of little use in and of itself. However, being able to compare data in the form of a chart or tracking a phenomenon over time via a table enables the viewer to make meaning out of what would otherwise be a series of incomprehensible numbers. Each index number "measures the relative change in price, quantity, value, or some other item of interest from one time period to another" (Chapter 15, 2008, McGraw Hill: 3).

Indexes, by virtue of their brevity and ease of viewing, also make it far easier to analyze large numbers. While a simple index measures the change in only one variable, complex indexes offer the ability to simultaneously compare a variety of different variables, further enhancing their utility in research. For example, a value index tracks changes in price and quantities, which makes it particularly valuable for retailers (Chapter 15, 2008, McGraw Hill: 22). The change in the price of gas is an essential data point for many industries to track over time, to assess the degree to which it affects input costs. The government regularly takes the economic 'temperature' of the economy using the Consumer Price Index (CPI), which is made up of over 400 variables, including goods and services such as food and medical bills. Although prices are expressed in different units, through the creation of an index and reducing such goods to a single 'basket' value the government can track the impact of inflation upon consumers over time (Chapter 15, 2008, McGraw Hill: 8).

Data indexes provide a vision of the present and the past and enable a comparison between the two while time series data seeks to predict the future. "A time series is a sequence of observations of a random variable…Examples include the monthly demand for a product, the annual freshman enrollment in a department of a university, and the daily volume of flows in a river" (Time series, n.d., University of Texas: 1). This knowledge enables retailers to anticipate seasonal shifts in demand; a university to know how many students are likely to enroll from those whom it chooses to admit; and a scientist to anticipate likely damage due to a weather event. Forecasting is necessary in all of these instances to engage in effective planning for the future.