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Test the Hypothesis that the variance of two populations are the same at 90 confidence level. The two sample variance are 8 with 40 degree of freedom and 12 with 50 degree of freedom.?
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Does the variance of t distributions become smaller as degrees of freedom increase?
Yes.
Does t-distribution rely on degree of freedom?
Yes. The parameters of the t distribution are mean, variance and the degree of freedom. The degree of freedom is equal to n-1, where n is the sample size. As a rule of thumb, above a sample size of 100, the degrees of freedom will be insignificant and can be ignored, by using the normal distribution. Some textbooks state that above 30, the degrees of freedom can be ignored.
How do you find the confidence intervals?
See: http://en.wikipedia.org/wiki/Confidence_interval Includes a worked out example for the confidence interval of the mean of a distribution. In general, confidence intervals are calculated from the sampling distribution of a statistic. If "n" independent random variables are summed (as in the calculation of a mean), then their sampling distribution will be the t distribution with n-1 degrees of freedom.
When testing for differences between two means the sample population are?
You are testing the difference between two means of independent sample and the population variance are not known. from those population you take two samples of two different size n1and n2. what degrees of freedom is appropriate to consider in this case
Why does a chi square test work?
A very superficial argument goes like this: You have a null hypothesis under which your variable has some distribution. On the basis of this distribution you expect certain values (frequencies) in certain intervals. The intervals may be numeric or categoric. But what you observe are different values. You could look at the differences between your observed and expected values but then, in total, they would all cancel out. So you look at their squares. Also, an observed value of 15 where you expected 10 (difference = 5) is, relatively speaking, much bigger than an observed value of 1005 where you were expecting 1000 (diff still = 5). So you divide by the expected value. Thus, for each interval you have (O-E)2/E. You add all these together and that is your chi-square test statistic. Call it C. If your data are consistent with the null hypothesis, then the observed values will be close to the expected values so that the absolute value of (O-E) and therefore its square will be small. So under the null hypothesis, the test statistic will be small. If C is small, the likelihood is that the observations are consistent with the null hypothesis. And in that case you accept the null hypothesis. As C gets larger, the chance of observing that large a value (or larger) when the null hypothesis is true decreases. Finally, for really large values of C, the chances of getting that big a value (or bigger), still under the null hypothesis, are so smaller than some pre-determined limit that you set - for example less than 5% for 95% confidence or 1% for 99% confidence etc. At that stage you decide that there is so little chance that the data are cnsistent with the null hypothesis that you must reject it and accept the alternative. Rather than calculate the probability of observing a value of C or larger, you would look up tables of critical values of C at the 5%, 1% etc levels. Finally, a word about degrees of freedom. If the data are classified one-way into n categories, the sum of the n expected values and the n observed values is the same. So, once you have n-1 of these the nth is determined. So you only have n-1 degrees of freedom. Similar arguments apply to 2-way, 3-way etc classifications. For more detail I suggest you get hold of a decent textbook. Actually
Does the variance of t distributions become smaller as degrees of freedom increase?
Yes.
What do children represent?
Happiness Innocence Freedom Confidence!! xx
What is 'freedom and liberty' when translated from English to Italian?
Libertà, although both freedom and liberty have in some cases different meanings. Examples: Libertà 1 - freedom of trade, freedom of the seas, freedom fighter, franchise, freedom of the city. Liberta 2 - Liberty of the press, liberty of thought, liberty of contract, at liberty (they set the prisoners at liberty), privileges, confidence (to take the confidence with someone.
What connection can be made between treatment of colonial native populations and the character of Caliban?
Europeans took away the native populations' freedom like Prospero took Caliban's freedom.
What connection can be made between Europe's treatment of colonial native populations and the character of Caliban?
Caliban, like the colonized native populations, is at first grateful for new ideas and goods but then becomes resentful at his unfair treatment.
Does t-distribution rely on degree of freedom?
Yes. The parameters of the t distribution are mean, variance and the degree of freedom. The degree of freedom is equal to n-1, where n is the sample size. As a rule of thumb, above a sample size of 100, the degrees of freedom will be insignificant and can be ignored, by using the normal distribution. Some textbooks state that above 30, the degrees of freedom can be ignored.
Why is there a need of self confidence?
Without confidence you tend to think in terms of fear and failure. With confidence you become fearless, self assured , energetic and happy. Living in confidence applies to all aspects of your life. It can stem from financial freedom to talking to an attractive women who end up begin your wife. Confidence is all a state of mind which you can posses with the right tools! Learn more those tool in the related link on how to build self confidence
Why do some people have low confidence?
One of the reasons why people lack self-confidence is because they grew up in an environment that restricted their freedom of expression.
What is the sample variance and the estimated standard error for a sample of n 4 scores with SS 300?
The sample variance is obtained by dividing SS by the degrees of freedom (n-1). In this case, the sample variance is SS/(n-1) = 300/(4-1) = 300/3 = 100 In order to get the standard error, you can do one of two things: a) divide the variance by n and get the square root of the result: square.root (100/4) = square.root(25) = 5, or b) get the standard deviation and divide it by the square root of n. 10/square.root(4) = 10/2 = 5
What is Douglass's tone in my bondage my freedom?
Douglass's tone in "My Bondage and My Freedom" is often assertive and defiant, showcasing his resilience against oppression. He also displays a sense of determination and intellectual self-confidence throughout the narrative.
When do you use n-2 degrees of freedom?
With n observations, it could be when 2 distributional parameters have been estimated from the data. Often this may be the mean and variance (or standard deviation( when they are both unknown.
How do you find the confidence intervals?
See: http://en.wikipedia.org/wiki/Confidence_interval Includes a worked out example for the confidence interval of the mean of a distribution. In general, confidence intervals are calculated from the sampling distribution of a statistic. If "n" independent random variables are summed (as in the calculation of a mean), then their sampling distribution will be the t distribution with n-1 degrees of freedom.
