Overview and Objectives

Welcome to the Financial Resources Lesson

This section of our online course will introduce you to Financial Resources - Part 2 of the finance function in action: understanding and communicating the value of statistical and graphical information in business and financial environments.

 

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Introduction and Background

 

Much of the learning content in the business and financial environment is presented in a statistical and/or graphical context. In this lesson we provide some of the basic tools so that you will have a better statistical appreciation of the world around you. The lesson will present and explain information that you will see either in hard-copy or via electronic delivery that is "business" or "financial" in nature. 

We define statistics as the science of collecting, organizing, analyzing, and interpreting data so as to assist in effective decision-making. Without a basic understanding of statistical and graphical technique and presentation you will be at a teaching/learning disadvantage given the increasingly quantitative nature of the business world. 

Why is statistics important in data analysis? Statistical tools and techniques can transform large amounts of numerical or categorical data into summary measures which allow an economical route to analysis. Since the quantity of information can be overwhelming the only cost-effective way to deal with it is by statistical summarization. It is these summarizations that represent the "language" of statistics and that can provide key input into the making of good business decisions. 

In the previous lesson on statistics you covered such areas as organizing and describing data, probability distributions and probability density functions, confidence intervals, hypothesis testing, correlation and regression methods, and software proficiency. The objective of the statistics components of these lessons was to make you aware of the quantitative methods available to present, analyze, summarize, and test for relationships in business data. You were made aware that statistics can be used simply to describe data (descriptive statistics) or to infer a property about a population on the basis of a sample (inferential statistics). The former occurs after the fact—what do these numbers describe?; the latter asks the question, "Now that we have "run the numbers," what does the resulting statistic (or statistics) suggest about the population parameter(s)?

As you also saw in the previous lessons on statistics, much of the content was presented graphically as scatter diagrams, bar charts, probability density functions, and probability distributions. These "picture" devices were intended to directly connect you with the concept under study. For example, you used a scatter diagram to graph the coordinates of two columns of data (say from an Excel spreadsheet). The intent of this scatter plot exercise was to allow you to see the (linear) relationship between two variables in an x-y graph 2-space. If you wanted to refine the graphical scatter plot relationship you could do so by applying (i.e., "fitting") a regression line to the data so as to estimate the regression function's intercept and slope coefficients. In so doing you were using statistics to infer conclusions about the regression line in conjunction with scatter points that allowed you to see how well the estimated function fit the data.

In this module we do not re-teach statistical measures that you have already seen. Rather we present data both statistically and graphically that you might normally see in educational, media, and other business contexts. The intended outcome is that you will be able to better understand and communicate this form of information content. You should also appreciate its value in summarizing real-world measurement. The only requirement is that the real world be reducible in such a way that we can transform events and occurrences into numbers or categories. As you will see the data we use in this lesson primarily pull from financial sources. Some of the examples present published research by the author. 

Even though you have seen some of the terms that comprise statistical analysis, it is probably best to begin this lesson with a review of basic statistical definitions and concepts. We will present these verbally; we will not use algebraic formulae as you have seen these in the previous collection of lesson related to statistics. If we state a definition or concept that you have not covered, any principles level business statistics text can give you the answer, the reasoning, and the formula. 

In the second part of this lesson we will present a random collection of statistical and graphical data that you have not seen before but that you might see sometime in the future. This collection of statistical examples is meant to be tutorial in nature.  They are also intended to polish and hone your ability to mentally digest the statistics concepts that you are already aware of.  In some cases the examples will refer to finance and economic output you have studied in other courses or lessons. The example presentations are not meant to be comprehensive; we would need to have a separate textbook for a completely comprehensive presentation. However, they will be representative of statistical and graphical content one might normally see in the media, the press, and in academic research. 

Again, we emphasize that the example presentations will be random in nature in that the sections will not necessarily follow an organized theme. This will keep things interesting. They will use both statistical and related graphical results.  If you see an example that draws a total blank don't panic.  Along with each statistics/graphical theme will be a verbal explanation of what you are looking at and its connection with statistics material you have already covered. Keep in mind that while the examples use statistics and graphs, they also can be viewed as tutorial in nature. The accompanying explanations will support the tutorial nature of the example. Many of the examples use secondary data. When secondary data are used sources will be given.

 

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