Detailed information about the coin 2 Hryvni (Dmytro Lutsenko), Ukraine, with pictures and collection and swap management: mintage, descriptions, metal, weight, size, value and other numismatic data.

Through testing conducted by an independent, U.S. EPA-certified laboratory, it was determined that Adya, Inc.’s water purifying solution, Adya Clarity, reduces up to 99.9% of the chemical 4-Methylcyclohexanemethanol (MCHM) from water. The chemical, 4-Methylcyclohexanemethanol (MCHM) was spilled into the Elk River just upstream from the Kanawha County municipal water intake in Charleston, West Virginia on January 9, 2014, contaminating the water supply to over 300,000 West Virginia residents. EPA-certified laboratory, Envirotek Laboratories, Inc. Of Mullica Hill, New Jersey, conducted the MCHM Reduction Test using Adya’s water purifying solution, Adya Clarity.

The following procedures were performed: Three liters of tap water were spiked with 400 μg of 4-Methyl-1-cyclohexanemethanol (MCHM) in a flask. Added 12 mL (4mLper liter) of Adya Clarity solution to the spiked solution, the flask was closed, mixed well and let sit for 12, 24, and 48 hours inside a fume hood. One liter of the solution was filtered through a 0.45 micron paper after 12 hours, a second liter of the solution was filtered through a 0.45 micron paper after 24 hours, the final liter of the solution was filtered through a 0.45 micron paper after 48 hours, the initial spiked solution and the filtered solutions were tested following the EPA method 525 for drinking water. The results are summarized below: FILTERED WATER RESULTS Time Before Filtering MCHM concentration in Adya Clarity Filtered Water% Reduction 12 hours. 7.5 µg/L 94.4% 24 hours 3.5 µg/L 97.4% 48 hours.

A Chi-square test is a common test for nominal (categorical) data. One application of a Chi-square test is a test for independence. In this case, the null hypothesis is that the occurrence of the outcomes for the two groups is equal. For example, you have two user groups ( e.g., male and female, or young and elderly). And you have nominal data for each group, for example, whether they use mobile devices or which OS they use.

So, your data look like this. If your data of the two groups came from the same participants ( i.e., the data were paired), you should use the McNemar's test. Let's use the examples above. First, prepare the data. Data X^2) Likelihood Ratio 7.7592 1 0.0053440 Pearson 7.5000 1 0.0061699 Phi-Coefficient: 0.354 Contingency Coeff.: 0.333 Cramer's V: 0.354 For a 2×2 table, you can also calculate the odds ratio. The odds ratio is how the probability of the phenomena is affected by the dependent variable. This can be calculated as ad / bc.

Running a McNemar's exact test is pretty similar to Chi-square. Question 2 (post-treatment) Yes No Question 1 Yes a b (pre-treatment) No c d More precisely, you need to use a binomial test rather than McNemar's test if b+ c in the 2×2 table is small. However, in R, you can run McNemar's test with continuity correction, so it will cause a big problem because the results of a binmoal test and McNemar's test with continuity correction become similar.

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