Part+3-+Presentation+of+Data

__Presentation__
Presentation of statistics is an art, not a science. There is always scope for variation and creativity, and your aim should always be to convey your message clearly, otherwise you will confuse your readers and you might lose credibility.

A good presentation of statistics involves making it easy for readers to understand and interpret the data you are presenting, and identify any key patterns or trends.

It is possible to present data in a written form, but when there is a lot of numbers, this is dull and ineffective. Illustrative materials such as tables, charts and maps are much better.

__Common ways of presenting Statistics__
An interval scale is a scale of measurement where the distance between any two adjacents units of measurement is the same (the zero point is arbitrary). Scores on an interval scale can be added and subtracted but can not be meaningfully multiplied or divided. For example, the time interval between the starts of years 1981 and 1982 is the same as that between 1983 and 1984. Other examples of interval scales include the heights of tides, and the measurement of longitude.
 * Interval Scale**

A frequency table is a record of how often each value, or set of values of the variable occurs. It may be enhanced by the addition of percentages that fall into each category. When we have more than one categorical variable in our set of data, a frequency table is sometimes called a contingency table because the figures found in the rows are dependent upon those found in the columns. **Suppose that in thirty shots at a target, a marksman makes the following scores: The frequencies of the different scores can be summarised as:
 * Frequency Table**
 * //Example//
 * 5 2 2 3 4 || 4 3 2 0 3 || 0 3 2 1 5 ||
 * 1 3 1 5 5 || 2 4 0 0 4 || 5 4 4 5 5 ||
 * //Score// || //Frequency// || //Frequency (%)// ||
 * 0 || 4 || 13% ||
 * 1 || 3 || 10% ||
 * 2 || 5 || 17% ||
 * 3 || 5 || 17% ||
 * 4 || 6 || 20% ||
 * 5 || 7 || 23% ||

A pie chart is a way of summarising a set of categorical data. It is a circle which is divided into segments. Each segment represents a particular category. The area of each segment is proportional to the number of cases in that category.
 * Pie Chart**

Suppose that, last year a sports wear manufacturers has spent 6 million pounds on advertising their products; 3 million has been spent on television adverts, 2 million on sponsorship, 1 million on newspaper adverts, and a half million on posters. This spending can be summarised using a pie chart:
 * //Example//**



A bar chart is another way of summarising a set of categorical data. It is often used in exploratory data analysis to illustrate the major features of the data in a convenient form. It displays the data using a number of rectangles, of the same width, each of which represents a particular category. The length (and hence area) of each rectangle is proportional to the number of cases in the category it represents, for example, age group, religious affiliation. Bar charts can be displayed horizontally or vertically and they are usually drawn with a gap between the bars, whereas the bars of a histogram are drawn immediately next to each other. 
 * Bar Chart**

A histogram is a way of summarising data that are measured on an interval scale. It divides up the range of possible values in a data set into classes or groups. For each group, a rectangle is constructed with a base length equal to the range of values in that specific group, and an area proportional to the number of observations falling into that group. This means that the rectangles might be drawn of non-uniform height. The histogram is only appropriate for variables whose values are numerical and measured on an interval scale. It is generally used when dealing with large data sets (>100 observations). A histogram can also help detect any unusual observations, or any gaps in the data set.
 * Histogram**


 * After plotting your graphs, go on to the next step to find out about how to intepret them!**