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Graphical Analysis Techniques [WLO: 1] [CLO: 3]

There are strengths and weaknesses to graphical analysis research techniques. For this discussion, begin by reviewing the technique of graphical analysis in your textbook. Then, keeping this technique in mind, read the following quotes:

“Errors using inadequate data are much less than those using no data at all.”—Charles Babbage

“Statistics is the science of variation.”—Douglas M. Bates (1985)

“All models are wrong, but some models are useful.”—George E. P. Box (1979)

The greatest moments are those when you see the result pop up in a graph or in your statistics analysis - that moment you realize you know something no one else does and you get the pleasure of thinking about how to tell them.—Emily Oster

https://www.goodreads.com/quotes/search?utf8=%E2%9C%93&q=statistics&commit=Search

Also consider the following ways to make a graph misleading from Misleading Graphs (Passy, 2012):

“Vertical scale is too big or too small.

Vertical axis skips numbers, or does not start at zero.

Graph is not labeled properly.

Graph does not have a title to explain what it is about.

Data is left out.

Scale not starting at zero.

Scale made in very small units to make graph look very big.

Scale values or labels missing from the graph.

Incorrect scale placed on the graph.

Pieces of a pie chart are not the correct sizes.

Oversized volumes of objects that are too big for the vertical scale differences they represent.

Size of images used in pictographs being different for the different categories being graphed.

Graph being a non-standard size or shape.”

Based on the above quotes, along with this week’s assigned readings and Instructor Guidance, compare graphical analysis with quantitative analysis (a technique you explored last week), and discuss why graphical analysis is important in research. Finally, describe guidelines for using graphical tools to present information clearly and effectively.

Answer 1

The analyses of data done through graph techniques to determine the optimal output is called Graphical analysis.

One of the powerful tools used for data evaluation are the graphs.

The graphs help in making summaries of characteristics of data in effective and efficient manner.

Using graphical techniques, the complex equations or tests of statistics and mathematics can be interpreted.

For instance, the graphical techniques used to interpret the data on the environment are histograms, box plots, and probability plots.

Basically, the graphs provide the information about the shape of distributions, the relation among the different variables and data sets, outliers, and trends.

Since the graphical techniques are qualitative in nature, so the accurate conclusions cannot be made based on these techniques only.

These techniques are used with quantitative statistical evaluations.

Some graphical techniques are described as follows:

1. Time Series Methods:

Using time series methods, the graph of interest can be constructed by keeping time on the x-axis and interest on the y-axis. At the time of plotting multiple series, the time series method helps in normalizing the data.

2. Box-plots:

Under the graphical techniques box-plot, the data is plotted in the box and divided into 4 groups, each group represents the 25% of the total data. The boxes in the box-plot graph show the percentile values (25th, 50th, and 75th).

3. Scatter plots:

This technique is used to describe the relationships between two or more than two variables when the different data sets with several observations are compared.

4. Histograms:

The histograms represent the data in bars, and the height and size of bars provide the basis of comparison.

Why graph analysis is important

Graphs are representation of all of the datas, therefore it shows a long trend over a long period of times, or any period of times.

Graphical presentation is a good tool to predicts the progress of a system, a significant process, or a behavior even.

Graphical presentation does not always indicate correctly the true values,

however, it reflects a trend that an analysis need to be done to implement improvement and significant changes for a problem

One advantage of graphical analysis is that it facilitates looking at individual data points, rather than merely summaries. It’s not that one can’t look at individual data points in a table one can but it’s daunting.

It’s much easier to see the points in a graph. And seeing the individual points can often lead to good things.

That’s at least part of the reason for making many plots of residuals, fitted values, leverage values, and so on.

For an example, consider the graphic I posted yesterday about how individual points contribute to the likelihood ratio or AIC, BIC, and DIC. LR, AIC, BIC, DIC are one-number summaries of goodness-of-fit, and users would never think to read a table of the contributions from individual points. But the graphic shows immediately that the one-number-summary is dominated by just one of those points.

Guidelines for using graphical tools to present information clearly and effectively.

Recognize that presentation matters

Don’t scare people with numbers

Maximize the data pixel ratio

Save 3D for the movies

Friends don’t let friends use pie charts

Choose the appropriate chart

Don’t mix chart types for no reason

Don’t use axes to mislead

Never rely solely on color

Use color with intention