1. Exploratory Data Analysis
1.4. EDA Case Studies
1.4.2. Case Studies
1.4.2.5. Beam Deflections

1.4.2.5.5.

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 . It is required that you have already downloaded and installed Dataplot and configured your browser. to run Dataplot. 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. Invoke Dataplot and read data. 1. Read in the data.	1. You have read 1 column of numbers into Dataplot, variable Y.
2. Validate assumptions. 1. 4-plot of Y. 2. Generate a run sequence plot. 3. Generate a lag plot. 4. Generate an autocorrelation plot. 5. Generate a spectral plot. 6. Generate a table of summary statistics. 7. Generate a linear fit to detect drift in location. 8. Detect drift in variation by dividing the data into quarters and computing Levene's test statistic for equal standard deviations. 9. Check for randomness by generating a runs test.	1. Based on the 4-plot, there are no obvious shifts in location and scale, but the data are not random. 2. Based on the run sequence plot, there are no obvious shifts in location and scale. 3. Based on the lag plot, the data are not random. 4. The autocorrelation plot shows significant autocorrelation at lag 1. 5. The spectral plot shows a single dominant low frequency peak. 6. The summary statistics table displays 25+ statistics. 7. The linear fit indicates no drift in location since the slope parameter is not statistically significant. 8. Levene's test indicates no significant drift in variation. 9. The runs test indicates significant non-randomness.
3. Fit Yi = C + A*SIN(2*PI*omega*ti+phi). 1. Generate a complex demodulation phase plot. 2. Generate a complex demodulation amplitude plot. 3. Fit the non-linear model.	1. Complex demodulation phase plot indicates a starting frequency of 0.3025. 2. Complex demodulation amplitude plot indicates an amplitude of 390 (but there is a short start-up effect). 3. Non-linear fit generates final parameter estimates. The residual standard deviation from the fit is 155.85 (compared to the standard deviation of 277.73 from the original data).
4. Validate fit. 1. Generate a 4-plot of the residuals from the fit. 2. Generate a nonlinear fit with outliers removed. 3. Generate a 4-plot of the residuals from the fit with the outliers removed.	1. The 4-plot indicates that the assumptions of constant location and scale are valid. The lag plot indicates that the data are random. The histogram and normal probability plot indicate that the residuals that the normality assumption for the residuals are not seriously violated, although there is a bend on the probablity plot that warrants attention. 2. The fit after removing 3 outliers shows some marginal improvement in the model (a 5% reduction in the residual standard deviation). 3. The 4-plot of the model fit after 3 outliers removed shows marginal improvement in satisfying model assumptions.