##
Example 3-1: Women’s Health Survey (Graphing)
Section* *

Let us take a look again at the nutrition data. In 1985, the USDA commissioned a study of women’s nutrition. Nutrient intake was measured for a random sample of 737 women aged 25-50 years. The following variables were measured:

- Calcium(mg)
- Iron(mg)
- Protein(g)
- Vitamin A(μg)
- Vitamin C(mg)

We can read the data from the SAS file below. Various transformed variables are also created at this step for inspection. Here are some different ways we could take a look at this data graphically using SAS (and Minitab).

Download the SAS program: nutrient2.sas

##
Univariate Cases
Section* *

Using * Histograms *we can:

- Assess Normality
- Find Normalizing Transformations
- Detect Outliers

Here we have a histogram (produced in SAS) for daily intake of calcium. Note that the data appear to be skewed to the right, suggesting that calcium is not normally distributed. This suggests that a normalizing transformation should be considered.

Common transformations include:

- Square Root (often used with counts data)
- Quarter Root
- Log (either natural or base 10)

The square root transformation is the weakest of the above transformations, while the log transformation is the strongest. In practice, it is generally a good idea to try all three transformations to see which appears to yield the most symmetric distribution.

The following shows histograms for the raw data (calcium), square-root transformation (S_calciu), quarter-root transformation (S_S_calc), and log transformation (L_calciu). With increasingly stronger transformations of the data, the distribution shifts from being skewed to the right to being skewed to the left. Here, the square-root transformed data is still slightly skewed to the right, suggesting that the square-root transformation is not strong enough. In contrast, the log-transformed data are skewed to the left, suggesting that the log transformation is too strong. The quarter-root transformation results in the most symmetric distribution, suggesting that this transformation is most appropriate for this data.

In practice, histograms should be plotted for each of the variables, and transformations should be applied as needed. There is no 'best' transformation for all datasets.

##
Bivariate Cases
Section* *

Using* Scatter Plots *we can:

- Describe relationships between pairs of variables
- Assess linearity
- Find Linearizing Transformations
- Detect Outliers

Here we have a scatterplot (produced in Minitab) in which calcium is plotted against iron. This plot suggests that daily intake of calcium tends to increase with the increasing daily intake of iron. If the data have a bivariate normal distribution, then the scatterplot should be approximately elliptical in shape. However, the points appear to fan out from the origin, suggesting that the data are not bivariate normal.

After applying quarter-root transformations to both calcium and iron, we obtain a scatter of points that appear to be more elliptical in shape. Moreover, it appears that the relationship between the transformed variables is approximately linear. The point in the lower left-hand corner appears to be an unusual observation or outlier. Upon closer examination, it was found that this woman reported zero daily intake of iron. Since this is very unlikely to be correct, we might justifiably remove this observation from the data set.

#### Outliers

**Note!**It is not appropriate to remove an observation from the data just because it is an outlier. Consider, for example, the ozone hole in the Antarctic. For years, NASA had been flying polar-orbiting satellites designed to measure ozone in the upper atmosphere without detecting an ozone hole. Then, one day, a scientist visiting the Antarctic pointed an instrument straight-up into the sky and found evidence of an ozone hole. What happened? It turned out that the software used to process the NASA satellite data had a routine for automatically removing outliers. In this case, all observations with unusually low ozone levels were automatically removed by this routine. A close review of the raw, preprocessed data confirmed that there was an ozone hole.

The above is a special case, where the outliers themselves are the most interesting observations. In general, outliers are removed only if there is a compelling reason to believe that something is wrong with the individual observations; e.g., if the observation is deemed to be impossible, as in the case of zero daily intake of iron. This underscores the need to have a good field or lab notes with details on data collection process. Lab notes may indicate that something may have gone wrong with an individual observation; e.g., a laboratory sample may have been dropped on the floor leading to contamination. If such a sample results in an outlier, then that sample may legitimately be removed from the data.

Outliers often have a greater influence on the results of data analyses than the remaining observations. For example, outliers have a strong influence on the calculation of the sample mean. If outliers are detected, and there is no corroborating evidence to suggest that they should be removed, then resistant statistical techniques should be applied. Here, by resistant techniques, we mean techniques or processes that are not easily influenced by outliers. For example, the sample median is not sensitive to outliers, and so may be calculated in place of the sample mean, if we believe that there is a possibility that sample mean may give a wrong picture. Outlier resistant methods go well beyond the scope of this course. If outliers are detected, then you should consult with a statistician.

##
Trivariate Cases
Section* *

Using* Rotating Scatter Plots *we can:

- Describe relationships among three variables
- Detect Outliers

#### Using Technology

Using rotating scatter plots in SAS

By rotating a 3-dimensional scatterplot, the illusion of three dimensions can be achieved. Here, we are looking to see if the cloud of points is approximately elliptical in shape.

#### Using Minitab

Creating a 3D Scatter plot in Minitab for L_calc, L_iron and L_prot.

- Select
`Graph`>`3D Scatter Plot` - The default is already Simple, so click
`OK`. - In Z, enter L_iron. In Y, enter L_prot. In X, enter L_calc.
- Click
`OK`.

Note: The plot (shown below) can be rotated using the 3D Graph tools that appear with the plot. If it does not appear, choose Tools > Toolbars and check 3D Graph Tools.

View the video to walk through what this process looks like in Minitab.

##
Multivariate Cases
Section* *

Using a* Matrix of Scatter Plots *we can:

- Look at all of the relationships between pairs of variables in one group of plots
- Describe relationships among three or more variables

Here, we have a matrix of scatterplots for quarter-root transformed data on all variables. Note that each variable appears to be positively related to the remaining variables. However, the strength of that relationship depends on which pair of variables is considered. For example, quarter-root iron is strongly related to quarter-root protein, but the relationship between calcium and vitamin C is not very strong.

#### Using Technology

#### Using SAS

Matrix of scatterplots generated using SAS.

proc sgscatter data=nutrient;

title "Scatterplot Matrix for Nutrition Data";

matrix S_S_calc S_S_iron S_S_prot S_S_vitA S_S_vitC;

run;

#### Using Minitab

Creating a matrix of scatterplots for S_S_calc, S_S_iron, S_S_protein, S_S_vitA, and S_S_vitC in Minitab.

- Select
`Graph`>`Matrix Plot` - The default is already Simple, so click
`OK`. - Under Graph variables, enter S_S_calc, S_S_iron, S_S_prot, S_S_vitA, and S_S_vitC.
- Click
`OK`.

A matrix plot for S_S_calc, S_S_iron, S_S_protein, S_S_vitA, and S_S_vitC

View the video to walk through what this process looks like in Minitab.