A long list of data is usually not practical for conveying information about a process. One of the best ways to analyze problems in any process is to plot the data and see what it is telling you. This is often recommended as a starting point in any data analysis during the problem-solving process. A wide range of graphical tools are available which can generate graphs quickly and easily such as Minitab and Microsoft Excel.

Different graphs can reveal different characteristics of your data such as the central tendency, the dispersion and the general shape for the distribution. **Graphical Analysis** allows to quickly learn about the nature of the process, enables clarity of communication and provides focus for further analysis. It is an important tool for understanding sources of variation in the data and thereby helping to better understand the process and where root causes might be. Conclusions drawn from the graphical analysis may require verification through further advanced statistical techniques such as significance testing and experimentation.

Graphing the data can be utilized for both historical data already available and when analyzing the data resulting from live data collection activities. Of course, you need to pick the right graphical tool as there are a lot of different ways to plot your data. A number of commonly used graphical tools will be covered here. However, note that if one graph fails to reveal anything useful, try another one.

## Line Charts:

**Line Charts** are the simplest forms of charts and often used to monitor and track data over time. They are useful for showing trends in quality, cost or other process performance measures. A line chart represents the data by connecting the data points by straight lines to highlight trends in the data. A standard or a goal line may also be drawn to verify actual performance against identified targets. Line charts are the most preferred format to display time series data. Time series plots, run charts, SPC charts and **radar charts** are all line charts.

## Time Series Plots:

**Time Series Plots** are line charts that are used to evaluate behavior in data over a time interval. They can be used to determine if a process is stable by visually spotting trends, patterns or shift in the data. If any of these are observed, then we can say that the process is probably unstable. More advanced charts for assessing the stability of a process over time are run charts and SPC charts.

A time series plot requires the data to be in the order which actually happened and that the data collection frequency is constant. ** Time Series Analysis** is the analysis of the plotted data in order to get meaningful information out of it. Different behaviors of the data can be observed such as upward and downward trends, shifts in the mean and changes in the amount of variation, patterns and cycles, or anything not random. **Time Series Forecasting** is the use of a model to predict future values based on previously observed values.

**continuous data**. It displays the average time it needed to change a label in a manufacturing process.

**count data**).

## Pie Charts:

**Pie Charts** are ways that make it easy to compare proportions. They are widely used in the business and media worlds for their simplicity and ease of interpretation. They display the proportion of each category relative to the whole data set representing each as a slice of the pie. The percentage represented by each category is usually provided near to the corresponding slice of the pie.

A **Doughnut Chart** is a variation of the pie chart with a blank center allowing for additional information to be included about the data. Pie and doughnut charts work well with fewer categories and are suitable for presenting data for around seven groups or fewer. A pie chart with one or more categories separated from the rest of the chart is known as an **exploded pie chart**.