what are the significance of Lucas Test?
408078
The Lucas test is used to help distinguish between primary, secondary and tertiary alcohols.  Add a couple of drops of cold ZnCl2 solution, followed by a couple of drops of concentrated HCl and the following happens:\n\n1, With a primary alcohol - no reaction\n2, With a secondary alcohol - slow precipitate is formed\n3, With a tertiary alcohol a precipitate is formed very quickly.\n\nThe ZnCl2/HCl reacts with the alcohols, which are soluble in water, to form chloroalkanes which are insoluble in water.  The reaction is faster on tertiary alcohols than secondary than primary.  \n\nThere are disadvantages to the test also, since a negative reaction could also mean no alcohol is present.  Not all chloroalkanes are solids so you may see oils being formed, and rate of formation is based on standardised solutions and open to your own determination of what is "slow" or "fast".  Not used really except at school or college.\n\nThere are many chemical tests which were used many years ago before the advent of techniques such as NMR, Mass-spec etc which were developed to help identify the presence of functional groups within molecules.  You've got to admire the chemists from many years ago who relied on these techniques to confirm structures of compounds, which we now take for granted.
288137
(m)\n\nThe level of significance (alpha) is the probability of a Type I error that an investigator is willing to risk in rejecting a null hypothesis. Generally, it refers to the probability of the event occurring due to chance. If alpha = .01, it is likely that one time out of a hundred the event could occur due to chance. If you lower the significance level from .05 to .01, you decrease the probability of rejecting a true hypothesis but increase the probability of accepting a false hypothesis. A Type II error (beta) occurs when an investigator fails to accept the alternative hypothesis when in fact the alternative hypothesis was true. In other words, the null hypothesis was accepted when it was not true.
78690
We need to know the topic first.\nUse an If... Then... statement\n\n\nLike IF I test whatever THEN this will happen.
890026
it depends on the test results
197838
In the experiment of testing for the number appearing on the spinner \n\nTotal number of possible choices\n= Number of ways in which a number can appear on the spinner \n= C(15,1)\n=15\n[Any one of the 15 numbers may appear]\n\nLet "A" be the event of the spinner landing on 7\n\nFor Event "A"\n\nNo. of favorable choices \n= No. of ways in which the spinner would stop at 7\n==> m(a) = 1\n\nProbability of the spinner landing on 7\n==> Probability of occurance of event "A" = m/n\n==> P(A) = 1/15\n=0.067 (approximately)\n\nhttp://www.futureaccountant.com/probability/study-notes/\n\nhttp://www.schoolingkids.com/
67844
I think it sounds very interesting.  Have you thought about how you will test this question and then how you will present your findings?  Good luck to you!
938158
Get a test and prove it.
120818
These are simple fast tests to detect the presence of carbohydrates in a given sample. These can be modified a little and by using a spec it would be possible to quantify the sugars in a given sample.\nQuestion if more precise would help in giving a specific answer
1076565
confidence intervals are about what % confident you are that a given person's score falls between x score and x score on any given day.  formulas: \n\n                                   68% CI=X+/-1(Standard error of measurement)\n                                   95%CI=X+/-1.96(SEM)\n                                   98%CI=X+/-2.58(SEM)\n\nto get the SEM:  SD(standard deviation)times the square root of 1 minus the reliability coefficient(rtt)\n\nI might be wrong about the margin of error, but I think it's the SEM(the index of the amount of error that can be expected in obtained test scores due to the unreliability of the test) \n\nhope this helps--statistics suck!
916981
actually a real life distribution approaches normal distribution as n approaches infinity.\n\nthat's why z-test got its name. it is used for large number of n.\nit approximate the normal distribution.\n\nactually, the formulas for these two test are the same.\nthe only difference is the the latter uses a parameter while the other uses statistic or estimate.\n\n\nwhen should you use each of them\n\nUSE Z-TEST IF:\n*n>30\n*the population mean is given\n*the population standard deviation is also given\n\n\nUSE T-TEST IF:\n*n<30\n*the population mean is not given\n*the population standard deviation is not also given
402618
I have no idea about the math, but I'm thinking the assumption is that the data are relatively correlated linearly.  There's a lot of math behind the wiki link.
586725
The answer would be C.\n\nIn a two tailed test, you're looking to see if the mean is either significantly greater or less than k which is why it is called two-tailed.  The null hypothesis would be that mu = k and the alternate, not equal.  Thus, c
598935
Used to set insurance premiums, the probability of getting into accidents based on your car type/age\n\nFor evaluating worker performance, ie you're on time 95% of the time or only 80%\n\nHow can you actually tell when two sets of data are different? Need statistics.\n\n A lot of jobs in stats!!
685395
Depends on the situation.\n\nFor example, you want to decide whether a batch of products is defective.  If your test come out to be significant but actually not (type I error), you have thrown away a perfectly fine batch of products.\n\nOn the other hand, for the same batch of products, if your test come out to be not significant but actually the batch is defective (type II error), then you have accepted a bad lot of products. \n\nSo in summary, type I error wastes money, type II error saves money but consumers don't like it.  Use similar analysis in your case.
878741
There are certain steps to perform a hypothesis test.\n\n1. Construct a null hypothesis Ho .\n2. Construct a alternate hypothesis H1.\n3. Decide test statistics and parameters [this includes degrees of freedom, significance value] [inmost cases its denoted by Z or t].\n4. Compute the p-value.\n5. Check with the available table.\n6. make inferences wether the hypothesis is accepted or not.\n\nThere is no fixed fomulae for this, but the choice of null hypothesis and test statistics depends on the problem. \n\nThe type of null hypothesis depends on the probelm(wether certain parameters are given or certain distributions are followed by given data etc) and the test statistics depends on parameters.\n\nI hope now you know it.\n\nAll the best
158808
Test Determines Your Risk of Death.\nyou can read it here
1022395
The Z-test is a statistical test used in inference which determines if the difference between a sample mean and the population mean is large enough to be statistically significant.\n\nIn order for the Z-test to be reliable, certain conditions must be met. The most important is that since the Z-test uses the population mean and population standard deviation, these must be known. The sample must be a simple random sample of the population. If the sample came from a different sampling method, a different formula must be used. It must also be known that the population varies normally (i.e., the sampling distribution of the probabilities of possible values fits a standard normal curve). If it is not known that the population varies normally, it suffices to have a sufficiently large sample, generally agreed to be ≥ 30 or 40.\n\nIn actuality, knowing the true σ of a population is unrealistic except for cases such as standardized testing in which the entire population is known. In cases where it is impossible to measure every member of a population it is more realistic to use a t-test, which uses the standard error obtained from the sample along with the t-distribution.