¶ … real life example, we would compare the quarterly sales over 4 years. For this the following values would be made Line Graphs

Pie Charts

Colum Chart

Scatter Charts

Misleading charts and graphs

Graphs and charts are tools for statistical analysis of data and information. Graphs and charts help identify and illustrate in a meaningful manner, the trends and details of the data.

Sometimes, data or information involves very large volume of data and would be too cumbersome to get them printed or to make non-technical people understand the trends and details of the data. On such occasions, using graphs and charts to represent the data in a simple and understandable manner is useful (Bhaduri and Ghosh, 2014).

Therefore it can be said that a graph or a chart shows thousands of information in a picture and help convey information quickly and easily to the user. The salient features of the data are highlighted by graphs and charts. Often relationships between various sets of data cannot easily be related. Graphs and charts helps relate and compare such data sets.

Graphs and charts therefore help to convey to the users comparisons and relationships, distribution, trends, composition, flow and/or process and location that are sought to be obtained from a data set.

Easy-to-understand formats that clearly and effectively communicate important points is enabled by graphs and charts by the condensation of large amounts of information.

The selection of the type of chart or graph to be used, the purpose of the graph or chart needs to be considered in terms of what one wants to present. For example, one could choose between expressing certain data set in the form of frequencies, percentages or categories.

Also the type of data that one is working with needs to be considered while selecting the chart or graph type.

While categorical data are grouped into non-overlapping categories such as grade, race, and yes or no responses, this can be represented by bar graphs, line graphs and pie charts. On the other hand continuous data are measured on a scale or continuum like such as weight or test scores which can be best represented through tools like histograms (Dehmer and Emmert-Streib, n.d.).

In this question we would discuss the types of charts and graphs that can be used for quantitative analysis.

There are also several types of quantitative data such as:

Discrete Data -- this is data that has distinct values or observations like 5 customers, 17 points, 12 steps etc.

Continuous Data -- this type of data is represents any value or observation within a finite or infinite interval like conversion rate, visits, page views, bounce rate, height, weight etc. (Forsyth, 2013).

Quantitative data can be summarized through mean, median, mode, standard deviation etc. And for this column chart, bar chart, line chart, Histogram etc. can be used.

In this question we would look into the usefulness of bar and line graphs and pie charts, column charts and scatter charts for analysis of quantitative data.

Types of Graph

Bar Graphs

Direct comparison of two or more sets of data is done by the use of bra graphs. This is used when the number of time intervals is small and when there is time series data.

Such graphs generally tend to have 0 as the baseline if all values are positive integers. Zero would be the midpoint of the scale in cases where the values include both positive and negative integers such as in the case of graphing differences in means.

However the scale ranges should not vary between graphs and should be standardized whenever possible. It is best not to use 3-D features in a bar graph as such graphs are complex in nature and makes them ineffective in conveying results to most audiences. It also leads to distortion of data.

Both horizontal and vertical bars can be included in bar graphs. But horizontal bar charts are rarely used to portray time series. In order to have the maximum effect of comparison of data and information in a bar graph, the columns should be sorted in some systematic order, most often according to size of value, to have visually effective schema.

The standardized grade scale is used to order findings by a particular category. Without the presence of a standard base line, stacked bar graphs that are composed of one or more segmented bars where each one of the segment represents the relative share of a total category, are not very effective in conveying data and for comparison especially in among the second, third or subsequent segments (Hsin-Yi Tsai, Yu-Lun Huang and Wagner, 2009).

It is preferable to create a bar graph that groups these values together, side by side when the graphing data from two or more different series, or different classes within the same series.

Taking a real life example, we would compare the quarterly sales over 4 years. For this the following values would be made:

The Revenue in dollars would be placed along one axis.

The time i.e. quarter number, would be plotted along another axis.

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Line graphs are more effective in presenting five or more data points when in comparison to bar graphs but do not turn out to be much effective in cases where the period of time is less and there is less emphasis on the identification of the differences.
For plotting time series online graphs, the categories should be placed on the x-axis or the horizontal axis such as the week, months, years -- depending on the data and the frequencies of data that is to be measured on the y axis or the vertical axis.

An example of a line graph can be had for representing the population trend of a country over a period of 11 years. In this graph we would plot the time in years along the X- axis and the total population of the country would be plotted along the Y-axis. There would a large number of data points or categories. A line chart is chosen to represent the above mentioned data as the number of data points is very high and a column or bar chart will look pretty cluttered and would not be able to clearly identify a trend or even fit on a computer screen or an average page. The attempt in using the line graph is to show the trend and not to show the maximum or the minimum population. The aim is to show the rate of population growth or decline or change of population which would be denoted by the steepness of the graph line instead of the actual population. Therefore to visualize trend-based data, a line chart is best suited (Hudak and Duplacey, 2008).

The data to be presented on a line chart are as below:

Year (to be plotted on X-axis)

Population (in millions) to be plotted on y-axis

2004

75

2005

80

2006

83

2007

78

2008

90

2009

92

2010

2011

2012

2013

2014

The line graph for the above data is represented a below. Here the blue line represents the change in population in a country over a period of 11 years. A user can easily get an idea about how the population of a country has changed over the 11-year period without actually requiring to know the exact population of the country at any year.

Graphs that have more than four or five lines tend is often confusing to decipher by the user unless the lines are well separated. In such cases it is the norm that, different line styles like solid line, dashed line, etc. And different colors and/or plotting symbols like asterisks, circles, etc. are used to separately identify the lines representing various data in a single graph for comparison.

If we add more values to the table above to denote the male and female population in every year of the 11-year period, the table would look as below:

Year

Population (in millions)

Male (in millions)

Female (in millions)

2004

75

35

40

2005

80

38

42

2006

83

38

45

2007

78

33

45

2008

90

42

48

2009

92

43

49

2010

47

53

2011

49

56

2012

52

58

2013

55

61

2014

57

62

Types of charts

In this section we would discuss the three types of charts that can be used for evaluation and representation of quantitative data.

Pie Charts

Though pie charts are good to view and easy to understand, the generally have limited utility. The charts are used to show parts of a whole where the whole is considered to be 100% and all the parts add up together to make 100.

While pie charts do not make small differences apparent, they are able to emphasize general findings. Generally it is advised that the pie charts should not consist of more than five or six slices and should be used to represent categorical data with a relatively small number of values (Hudak and Duplacey, 2008).

As with bar graphs, pie charts should avoid the use of 3-D features or break out of the pieces as such graphs makes it difficult to compare the relative size of the slices. The category labels should always be included in pie charts so that users can relate a slice with the category that it represents. It is also advised that the category labels should be placed directly…