Scoring and Interpretation

Now that you have finished and scored the diagnostic exam, it is time for you to learn what it all means. First, note any area where you had difficulty. This should not be limited to unfamiliar material. You should do this even if you got the correct answer. Determine where this material is covered in the book. Plan to spend additional time on the chapter in question. There is material you may not recognize because you have not gotten that far in class. 

The following relation determines the baseline score. N represents the number of answers. Ignore those you left blank. 

There are no free-response questions on this diagnostic exam such questions are not useful at this point. There will be examples of free-response questions later in this book. We will use the multiple-choice questions to provide an estimate of your preparation. This is a simplified approach based on these questions. Do not try to do more than use these results as a guide. 

If you did better than you expected—great Be careful not to become overconfident. Much more will need to be done before you take the AP Chemistry exam. 

If you did not do as well as you liked, don t panic. There is plenty of time for you to prepare for the exam. This is a guide to allow you to know which path you need to follow. 

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Torrance, E. P. (1990). Torrance test of creative thinking. Manual for scoring and interpreting results. Verbal, Forms A and B. Bensenville, IL Scholastic Testing Service, Inc. 

Please feel free to contact Dr. Charyton if you would like additional information or training, scoring, and interpretation of the CEDA. Dr. Charyton can provide onsite training for your research, educational program, industry, NASA, or military project. 

Once the bone mineral density report is available, T-scores and Z-scores are useful tools in interpreting the data. The T-score is the number of standard deviations from the mean bone mineral density in healthy young white women. Osteoporosis is defined as a T-score at least -2.5 standard deviations below the mean (Table 53-3). Osteopenia, or low bone mass that eventually may lead to osteoporosis, is defined as a T-score between -2.5 and -1.0 standard deviations below the mean. The International Society for Clinical Densitometry recommends use of the WHO definition and T-scores for diagnosis of osteoporosis in postmenopausal women and men... 

It is interpreted in the same way as a z-score, with the same values used to indicate questionable and unsatisfactory performance. The z-score and z -score are related as shown in the following equation ... 

PLS with a matrix Y instead of a vector y is called PLS2. The purpose of data evaluation can still be to create calibration models for a prediction of the y-variables from the x-variables in PLS2 the models for the various y-variables are connected. In a geometric interpretation (Figure 4.25), the m-dimensional x-space is projected on to a small number of PLS-x-components (summarizing the x-variables), and the -dimensional y-space is projected on to a small number of PLS-y-components (summarizing the y-variables). The x- and the y-components are related pairwise by maximum covariance of the scores, and represent a part of the relationship between X and Y. Scatter plots with the x-scores or the y-scores are projections of... 

Then using these 91 peaks only, the original data set was reexamined by principal components analysis. Eigenvalues greater than one were plotted to determine how many factors should be retained. After variraax rotation, the factor scores were plotted and interpreted. 

The PLS scores are interpreted in the same way as PCA scores since they are the sample coordinates along the model components. The additional feature in PLS is that two different sets of components are considered, summarizing variations in the X space or Y space. PLS loadings express the relatedness of each X and Y variable to the model component. T scores are the coordinates of data points located in the X space that describe the part of structure in X which is most predictive for... 

Figure 8.8 provides a scatter plot of the first two PC loadings, which can be used to roughly interpret the two new compressed variables in terms of the original four variables. In this case, it appears that the first PC is a descriptor between the sepal width and the other three X-variables, while the second PC is a descriptor of the two sepal measurements only. The plot also shows that the petal width and petal length are highly correlated with one another for the 150 irises used in this study. Further discussion on the interpretation of PC scores and loadings is found in Section 8.6.1. 

The hypothesis of causes, by interpretation of the factor loading matrix, can be proved by computation of the factor scores and their graphical representation. 

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