Graph Your Data

A boxplot works best when the sample size is at least 20. For example, a scientist creates a boxplot to compare the height of plants grown with two different fertilizers and a control group with no fertilizer. To see an example, go to Minitab Help: Example of Boxplot.

In a contour plot, the values for two predictor variables are represented on the x- and y-axes, and the values for the response variable are represented by shaded regions, called contours. A contour plot is like a topographical map in which x-, y-, and z-values are plotted instead of longitude, latitude, and altitude.

A food scientist wants to determine the optimal time and temperature for heating a frozen dinner. The scientist prepares 14 samples at various times and temperatures, and then has professional food tasters rate each sample for overall quality. The scientist creates a contour plot to examine the results. To see an example, go to Minitab Help: Example of Contour Plot.

A dotplot divides sample values into small intervals and represents each value or small group of values with a dot along a number line. A dotplot works best when the sample size is less than approximately 50.

For example, a quality engineer creates a dotplot to examine the distribution of the amount of torque that is required to remove the caps from a sample of shampoo bottles. To see an example, go to Minitab Help: Example of Dotplot.

A histogram divides sample values into many intervals and represents the frequency of data values in each interval with a bar. A histogram works best when the sample size is at least 20. However, a sample size that is considerably greater than 20 may better represent the distribution.

For example, a quality engineer creates a histogram to examine the distribution of the amount of torque that is required to remove the caps from a sample of shampoo bottles. To see an example, go to Minitab Help: Example of Histogram.

An individual value plot shows a dot for the actual value of each observation in a group, making it easy to spot outliers and see distribution spread. An individual value plot works best when the sample size is less than approximately 50.

Like a boxplot, an individual value plot helps you to identify possible outliers and visualize the distribution of your data. However, unlike a boxplot, an individual value plot displays each value separately. Separate values are especially useful when you have relatively few observations or when it is important to assess the effect of each observation.

For example, an engineer creates an individual value plot to compare the elasticity of plastic samples made with different additives. To see an example, go to Minitab Help: Example of an individual value plot of multiple Y variables.

For example, researchers at the department of highway safety want to understand the relationship between driver experience and road type on the number of steering corrections. The researchers create an interaction plot to display the effect of the factors on each other and on the response. To see an example, go to Minitab Help: Example of Interaction Plot.

For example, a carpet manufacturer wants to see the results for a one-way ANOVA. The manufacturer creates a main effects plot of the average carpet durability scores by carpet type. To see an example, go to Minitab Help: Example of Main Effects Plot.

For example, a business analyst wants to study successful small-to-medium size manufacturing companies. The analyst collects data on the number of clients, rate of return, sales, and the years the companies have been in business. As part of the initial investigation, the analyst creates a matrix plot to examine the relationships between number of clients, rate of return, and years. To see an example, go to Minitab Help: Example of Matrix Plot.

A multi-vari chart provides a graphical representation of the relationships between factors and a response. The multi-vari chart displays the means at each factor level for every factor. In Minitab, each multi-vari chart can display up to four factors.

For example, a manufacturer produces plastic pipes using two different machines with three temperature settings. The quality engineer is concerned about the consistency of pipe diameters from the different machines and settings. The engineer creates a multi-vari chart to investigate the variation in pipe diameters. To see an example, go to Minitab Help: Example of Multi-Vari Chart.

For example, a medical researcher creates a scatterplot to show the positive relationship between Body Mass Index (BMI) and body fat percentage in adolescent girls. To see an example, go to Minitab Help: Example of Scatterplot.

For example, a food scientist wants to determine the optimal time and temperature for heating a frozen dinner. The scientist prepares 14 samples at various times and temperatures, and then has professional food tasters rate each sample for overall quality. The scientist creates a 3D surface plot to examine the results. To see an example, go to Minitab Help: Example of 3D Surface Plot.

For example, a stock broker compares the monthly performance of two stocks during the past two years. The stock broker creates a time series plot to visualize the performance of the two stocks. To see an example, go to Minitab Help: Example of Time Series Plot.