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What is the difference between a chi-square and t-distribution?
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.
Is a type of data that is likely to be normally distributed?
The means of repeated samples from any population.
Comparing the means of two normal distribution?
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.
What are characteristics of a normal distribution?
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.
What is the characteristic of a normal distribution?
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.
What is the difference between a chi-square and t-distribution?
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.
Is a type of data that is likely to be normally distributed?
The means of repeated samples from any population.
When is the sample mean over repeated samples from the same population or process not normally distributed?
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.
Lets assume you have taken 100 samples of size 36 each from a normally distributed population Calculate the standard deviation of the sample means if the populations variance is 25?
square (25/36) = 5/6 = .833
Will sample means be nearly normally distributed if the distribution of the measurement among the individuals are not from a normal distribution?
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.
How many linear feet is twenty thousand square feet?
Linear means length, square means area. You are trying to compare the incomparable.
What statistical test should be used to compare the means of two groups: t test or chi-square?
A t-test should be used to compare the means of two groups, while a chi-square test is used to compare frequencies or proportions between groups.
Why is the central limit theorem an important idea for dealing with a population not normally distributed?
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.
What conditions are necessary in order to use a test to test the differences between two population means?
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.
What conditions are necessary in order to use a t-test to test the differences between two population means?
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.
How do you tell if two samples are signficantly different?
Statistical tests like t-tests or ANOVA can be used to determine if two samples are significantly different. These tests compare means of the samples, account for sample size, and calculate a p-value to determine if the difference is significant. A p-value below a chosen significance level (commonly 0.05) indicates that the samples are significantly different.
Is natural gas distributed by a pipeline?
It can be but that is no the only means by which it is distributed.
