4. Process Modeling
4.6. Case Studies in Process Modeling
4.6.4. Thermal Expansion of Copper Case Study

4.6.4.6.

Work This Example Yourself

View Dataplot Macro for this Case Study

This page allows you to repeat the analysis outlined in the case study description on the previous page using Dataplot, if you have downloaded and installed it. Output from each analysis step below will be displayed in one or more of the Dataplot windows. The four main windows are the Output window, the Graphics window, the Command History window and the Data Sheet window. Across the top of the main windows there are menus for executing Dataplot commands. Across the bottom is a command entry window where commands can be typed in.

Data Analysis Steps	Results and Conclusions
Click on the links below to start Dataplot and run this case study yourself. Each step may use results from previous steps, so please be patient. Wait until the software verifies that the current step is complete before clicking on the next step.	The links in this column will connect you with more detailed information about each analysis step from the case study description.
1. Get set up and started. 1. Read in the data.	1. You have read 2 columns of numbers into Dataplot, variables thermexp and temp.
2. Plot the data. 1. Plot thermexp versus temp.	1. Initial plot indicates that a nonlinear model is required.
4. Fit a Q/Q rational function model. 1. Perform the Q/Q fit and plot the predicted values with the raw data. 2. Perform model validation by generating a 6-plot. 3. Generate a full-sized plot of the residuals to show greater detail.	1. The model parameters are estimated. The plot of the predicted values with the raw data seems to indicate a reasonable fit. 2. The 6-plot shows that the residuals follow a distinct pattern and suggests that the randomness assumption for the errors is violated. 3. The full-sized residual plot shows the non-random pattern more clearly.
3. Fit a C/C rational function model. 1. Perform the C/C fit and plot the predicted values with the raw data. 2. Perform model validation by generating a 6-plot. 3. Generate a full-sized plot of the residuals to show greater detail.	1. The model parameters are estimated. The plot of the predicted values with the raw data seems to indicate a reasonable fit. 2. The 6-plot does not indicate any notable violations of the assumptions. 3. The full-sized residual plot shows no notable assumption violations.