4. Process Modeling
4.6. Case Studies in Process Modeling
4.6.1. Load Cell Calibration

4.6.1.11.

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 Deflection and Load.
2. Fit and validate initial model. 1. Plot deflection vs. load. 2. Fit a straight-line model to the data. 3. Plot the predicted values from the model and the data on the same plot. 4. Plot the residuals vs. load. 5. Plot the residuals vs. the predicted values. 6. Make a 4-plot of the residuals. 7. Refer to the numerical output from the fit.	1. Based on the plot, a straight-line model should describe the data well. 2. The straight-line fit was carried out. Before trying to interpret the numerical output, do a graphical residual analysis. 3. The superposition of the predicted and observed values suggests the model is ok. 4. The residuals are not random, indicating that a straight line is not adequate. 5. This plot echos the information in the previous plot. 6. All four plots indicate problems with the model. 7. The large lack-of-fit F statistic (>214) confirms that the straight- line model is inadequate.
3. Fit and validate refined model. 1. Refer to the plot of the residuals vs. load. 2. Fit a quadratic model to the data. 3. Plot the predicted values from the model and the data on the same plot. 4. Plot the residuals vs. load. 5. Plot the residuals vs. the predicted values. 6. Do a 4-plot of the residuals. 7. Refer to the numerical output from the fit.	1. The structure in the plot indicates a quadratic model would better describe the data. 2. The quadratic fit was carried out. Remember to do the graphical residual analysis before trying to interpret the numerical output. 3. The superposition of the predicted and observed values again suggests the model is ok. 4. The residuals appear random, suggesting the quadratic model is ok. 5. The plot of the residuals vs. the predicted values also suggests the quadratic model is ok. 6. None of these plots indicates a problem with the model. 7. The small lack-of-fit F statistic (<1) confirms that the quadratic model fits the data.
4. Use the model to make a calibrated measurement. 1. Observe a new deflection value. 2. Determine the associated load. 3. Compute the uncertainty of the load estimate.	1. The new deflection is associated with an unobserved and unknown load. 2. Solving the calibration equation yields the load value without having to observe it. 3. Computing a confidence interval for the load value lets us judge the range of plausible load values, since we know measurement noise affects the process.