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In the handicapping community, there are a lot of things to consider. Miami is coming off two emotional wins over Virginia Tech and Notre Dame in primetime contests. Now they have a noon kickoff against what they probably perceive to be a lowly UVA squad.Unfortunately, the two most precise methods are not easy to use and require a good deal of "experimentation" with the data. Therefore you could:Exploratory Examination of Correlation Matrices.
A common first step of many data analyses that involve more than a very few variables is to run a correlation matrix of all variables and then examine it for expected (and unexpected) significant relations. For example, by definition, a coefficient significant at the.
There is no "automatic" way to weed out the "true" correlations. This issue is general and it pertains to all analyses that involve "multiple comparisons and statistical significance. Pairwise Deletion of Missing Data.
Only this way will you get a "true" correlation matrix, where all correlations are obtained from the same set of observations. However, if missing data are randomly distributed across cases, you could easily end up with no "valid" cases in the data set, because each of them will have at least one missing data in some variable. The most common solution used in such instances is to use so-called pairwise deletion of missing data in correlation matrices, where a correlation between each pair of variables is calculated from all cases that have valid data on those two variables.
However, it may sometimes lead to serious problems. For example, a systematic bias may result from a "hidden" systematic distribution of missing data, causing different correlation coefficients in the same correlation matrix to be based on different subsets of subjects.
In addition to the possibly biased conclusions that you could derive from such "pairwise calculated" correlation matrices, real problems may occur when you subject such matrices to another analysis (e. Thus, if you are using the pairwise method of deleting the missing data, be sure to examine the distribution of missing data across the cells of the matrix for possible systematic "patterns.
If the pairwise deletion of missing data does not introduce any systematic bias to the correlation matrix, then all those pairwise descriptive statistics for one variable should be very similar. However, if they differ, then there are good reasons to suspect a bias. For example, if the mean (or standard deviation) of the values of variable A that were taken into account in calculating its correlation with variable B is much lower than the mean (or standard deviation) of those values of variable A that were used in calculating its correlation with variable C, then we would have good reason to suspect that those two correlations (A-B and A-C) are based on different subsets of data, and thus, that there is a bias in the correlation matrix caused by a non-random distribution of missing data.
Pairwise Deletion of Missing Data vs. Another common method to avoid loosing data due to casewise deletion is the so-called mean substitution of missing data (replacing all missing data in a variable by the mean of that variable).
Mean substitution offers some advantages and some disadvantages as compared to pairwise deletion. Its main advantage is that it produces "internally consistent" sets of results ("true" correlation matrices).
The main disadvantages are:Spurious Correlations. There is a third variable (the initial size of the fire) that influences both the amount of losses and the number of firemen.
If you "control" for this variable (e.
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