 1. Exploratory Data Analysis
1.4. EDA Case Studies
1.4.2. Case Studies
1.4.2.3. Random Walk

## 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 table of summary
statistics.
```
```   3. Generate a linear fit to detect
drift in location.

```
```   4. Detect drift in variation by
dividing the data into quarters and
computing Levene's test for equal
standard deviations.

```
```   5. Check for randomness by generating
a runs test.

```
```
```
``` 1. Based on the 4-plot, there are shifts
in location and scale and the data are not
random.
```
``` 2. The summary statistics table displays
25+ statistics.
```
``` 3. The linear fit indicates drift in
location since the slope parameter
is statistically significant.
```
``` 4. Levene's test indicates significant
drift in variation.

```
``` 5. The runs test indicates significant
non-randomness.

```
```3. Generate the randomness plots.
```
```   1. Generate an autocorrelation plot.

```
```   2. Generate a spectral plot.

```
```
```
``` 1. The autocorrelation plot shows
significant autocorrelation at lag 1.
```
``` 2. The spectral plot shows a single dominant
low frequency peak.
```
```4. Fit Yi = A0 + A1*Yi-1 + Ei
and validate.
```
```   1. Generate the fit.

```
```   2. Plot fitted line with original data.

```
```   3. Generate a 4-plot of the residuals
from the fit.

```
```   4. Generate a uniform probability plot
of the residuals.

```
```

```
``` 1. The residual standard deviation from the
fit is 0.29 (compared to the standard
deviation of 2.08 from the original
data).

```
``` 2. The plot of the predicted values with
the original data indicates a good fit.

```
``` 3. The 4-plot indicates that the assumptions
of constant location and scale are valid.
The lag plot indicates that the data are
random.  However, the histogram and normal
probability plot indicate that the uniform
disribution might be a better model for
the residuals than the normal
distribution.

```
``` 4. The uniform probability plot verifies
that the residuals can be fit by a
uniform distribution.

``` 