What are the Analysis Options available for each type of Dial in Discovery?
Most dials in Discovery include Analysis Settings that can be applied to dials. Users with the "Use Analysis Tools" permission included in their role will have the ability to adjust the Analysis Setting on dials. The Analysis Settings can be accessed in the Editors area of the Visual Designer after selecting a dial. The following chart shows which analysis settings can be used in the dials on which they are available.
Standard View: Returns you to the dial’s normal appearance.
Basic Analysis options:
- Min and Max: Highlights the minimum and maximum values.
- Average: Highlights the average of the data.
- Standard Deviation: In statistics and probability theory, standard deviation shows how much variation or "dispersion" exists from the average (mean, or expected value). A low standard deviation indicates that the data points tend to be very close to the mean, whereas high standard deviation indicates that the data points are spread out over a large range of values. This chart shows the mean and standard deviation values of the dial’s data.
- Linear Trendline: Shows you a best-fit straight line that is used with simple linear data sets. Your data is linear if the pattern in its data points resembles a line. A linear trendline usually shows that something is increasing or decreasing at a steady rate.
- Polynomial Trendline: A curved line that is used when data fluctuates. It is useful, for example, for analyzing gains and losses over a large data set. The order of the polynomial can be determined by the number of fluctuations in the data or by how many bends (hills and valleys) appear in the curve. Description for a visualization. Explains what a dial will look like
- Moving Average: Smooths out fluctuations in data to show a pattern or trend more clearly. A moving average uses a specific number of data points, averages them, and uses the average value as a point in the line.
- Correlation Chart: A Scatter Plot and a Linear Regression Line. A scatter plot or scattergraph is a type of mathematical diagram using Cartesian coordinates to display values for two variables for a set of data. Correlation is indicated by the line of best fit determined by linear regression on the data set.
- Control Chart: Also known as a Shewhart chart or process-behaviour chart, is a tool used in statistical process control to determine whether a manufacturing or business process is in a state of statistical control. The "Individuals Control Chart" uses data samples that are individual measurements. Control charts for individual measurements, e.g., the sample size = 1, use the moving range of two successive observations to measure the process variability. The moving range is defined as the absolute value of the first difference (e.g., the difference between two consecutive data points) of the data. Analogous to the Shewhart control chart, one can plot both the data (which are the individuals) and the moving range. For the control chart for individual measurements, the lines plotted are: UCL (Upper Control Limit), Centreline and LCL (Lower Control Limit).
- Pareto: shows you a type of chart that contains both bars and a line graph, where individual values are represented in descending order by bars, and the cumulative total is represented by the line. The purpose of the Pareto chart is to highlight the most important among a (typically large) set of factors.
- Relative Variance: Shows you the point by point difference between two data sets relative to the first data set.
- Actual Variance: Shows you the point by point difference between two data sets compared to zero.
- Waterfall: Helps in determining the cumulative effect of sequentially introduced positive or negative values. The waterfall chart is also known as a flying bricks chart or Mario chart due to the apparent suspension of columns (bricks) in midair. Often in finance, it will be referred to as a bridge.
- Previous Period: Shows you a comparison of two data sets, one representing the current period and the second representing the preceding calendar period of the same type.
- Comparable Period: Shows you a comparison of two data sets, one representing the current period and the second representing the same calendar period from the previous year.