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White Lithium Grease, lb. White Lithium Grease, 3 oz. Motor Assembly Grease, 8 oz. Fogging Oil Spray, 9.Your new online MSDS binder is a place for you to store the material safety data sheets you need to deploy. Other companies are charging thousands of dollars to set up accounts and give you access to their msds online database. Our MSDS database is an easy to use, do it yourself tool, and best of all We've included the most sought after safety data sheets, and the database just keeps growing.
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SDS Sheets - Greases
Follow Us. The brands listed above are trademarks of 3M. Change Location United States - English.However, if the same difference of 2 was obtained from very differentiated scores (e. That is to say, reduction of the within-group variation increases the sensitivity of our test. The t-test for dependent samples helps us to take advantage of one specific type of design in which an important source of within-group variation (or so-called, error) can be easily identified and excluded from the analysis.
Specifically, if two groups of observations (that are to be compared) are based on the same sample of subjects who were tested twice (e. However, if the same sample was tested twice, then we can easily identify (or "subtract") this variation. Specifically, instead of treating each group separately, and analyzing raw scores, we can look only at the differences between the two measures (e.
By subtracting the first score from the second for each subject and then analyzing only those "pure (paired) differences," we will exclude the entire part of the variation in our data set that results from unequal base levels of individual subjects. This is precisely what is being done in the t-test for dependent samples, and, as compared to the t-test for independent samples, it always produces "better" results (i.
If these assumptions are clearly not met, then one of the nonparametric alternative tests should be used. Technically, we can apply the t-test for dependent samples to any two variables in our data set. However, applying this test will make very little sense if the values of the two variables in the data set are not logically and methodologically comparable. Following, is an example of a data set that can be analyzed using the t-test for dependent samples. WCC before WCC after case 1 case 2 case 3 case 4 case 5.
However, the t-test for dependent samples analysis is performed only on the paired differences"ignoring" the raw scores and their potential differentiation. Thus, the size of this particular difference of 1 will be compared not to the differentiation of raw scores but to the differentiation of the individual difference scores, which is relatively small: 0. Compared to that variability, the difference of 1 is extremely large and can yield a highly significant t value.
If there are more than two "correlated samples" (e. The repeated measures ANOVA can be considered a generalization of the t-test for dependent samples and it offers various features that increase the overall sensitivity of the analysis.
The breakdowns analysis calculates descriptive statistics and correlations for dependent variables in each of a number of groups defined by one or more grouping (independent) variables. In the following example data set (spreadsheet), the dependent variable WCC (White Cell Count) can be broken down by 2 independent variables: Gender (values: males and females), and Height (values: tall and short).
SDS - MSDS
GENDER HEIGHT WCC case 1 case 2 case 3 case 4 case 5. The resulting breakdowns might look as follows (we are assuming that Gender was specified as the first independent variable, and Height as the second). For example, in the above example, you see the means for "all males" and "all females" but you do not see the means for "all tall subjects" and "all short subjects" which would have been produced had you specified independent variable Height as the first grouping variable rather than the second.
Statistical Tests in Breakdowns.