According to the Central Limit Theorem if the sample size is large enough then the means will tend towards a normal distribution regardless of the distribution of the actual sample.
Chi-square is a distribution used to analyze the standard deviation of two samples. A t-distribution on the other hand, is used to compare the means of two samples.
The means of repeated samples from any population.
You can compare the means of two dependent or independent samples. You can also set up confidence intervals. For independent samples you test the claim that the two means are not equal; the null hypothesis is mean1 equals mean2. The alternative hypothesis is mean1 does not equal mean2. For dependent (paired) samples you test the claim that the mean of the differences are not equal; the null hypothesis is the difference equals zero; the alternative hypothesis is the difference does not equal zero.
The Normal (or Gaussian) distribution is a member of the exponential family of probability distributions. It is symmetrical function whose shape is determined by two parameters: the mean and variance (or standard deviation). The distribution s additive so that if two variables, X and Y are normally distributed then, even if their means and variances are different, their sum (and difference) are normally distributed with parameters that are simply related to the separate ones.It is not an easy distribution to calculate and so it has been necessary to tabulate key values.According to the law of large numbers, if you take repeated independent samples from any distribution, the means of those samples are distributed approximately normally. The greater the size of each sample, or the greater the number of samples, the more closely the results will match the normal distribution. This characteristic makes the Normal distribution central to statistical theory.
The Normal (or Gaussian) distribution is a symmetrical probability function whose shape is determined by two values: the mean and variance (or standard deviation).According to the law of large numbers, if you take repeated independent samples from any distribution, the means of those samples are distributed approximately normally. The greater the size of each sample, or the greater the number of samples, the more closely the results will match the normal distribution. This characteristic makes the Normal distribution central to statistical theory.
Chi-square is a distribution used to analyze the standard deviation of two samples. A t-distribution on the other hand, is used to compare the means of two samples.
The means of repeated samples from any population.
Provided the samples are independent, the Central Limit Theorem will ensure that the sample means will be distributed approximately normally with mean equal to the population mean.
square (25/36) = 5/6 = .833
Yes, as you keep drawing more and more samples and the number of samples become sufficiently large. This is known as the Central Limit Theorem.
Linear means length, square means area. You are trying to compare the incomparable.
According to the Central Limit Theorem, even if a variable has an underlying distribution which is not Normal, the means of random samples from the population will be normally distributed with the population mean as its mean.
The samples must be randomly selected, independent, and normally distributed. The following are necessary to use a t-test for small independent samples. 1. The samples must be randomly selected. 2. The samples must be independent. 3. Each population must have a normal distribution.
The samples must be randomly selected, independent, and normally distributed. The following are necessary to use a t-test for small independent samples. 1. The samples must be randomly selected. 2. The samples must be independent. 3. Each population must have a normal distribution.
It can be but that is no the only means by which it is distributed.
You can compare the means of two dependent or independent samples. You can also set up confidence intervals. For independent samples you test the claim that the two means are not equal; the null hypothesis is mean1 equals mean2. The alternative hypothesis is mean1 does not equal mean2. For dependent (paired) samples you test the claim that the mean of the differences are not equal; the null hypothesis is the difference equals zero; the alternative hypothesis is the difference does not equal zero.
distributed data base means noting