v = n1 + n2 - k
n1 = 36, n2= 40 and k=2
v = 36 + 40 - 2
v = 74
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.
A T test is used to find the probability of a scenario given a specific average and the number of degrees of freedom. You are free to use as few degrees of freedom as you wish, but you must have at least 1 degree of freedom. The formula to find the degrees of freedom is "n-1" or the population sample size minus 1. The minus 1 is because of the fact that the first n is not a degree of freedom because it is not an independent data source from the original, as it is the original. Degrees of freedom are another way of saying, "Additional data sources after the first". A T test requires there be at least 1 degree of freedom, so there is no variability to test for.
It is not negative. it is positively skewed, and it approaches a normal distribution as the degrees of freedom increase. Its shape is NEVER based on the sample size.
The data sets determine the degrees of freedom for the F-test, nit the other way around!
The t-distribution and the normal distribution are not exactly the same. The t-distribution is approximately normal, but since the sample size is so small, it is not exact. But n increases (sample size), degrees of freedom also increase (remember, df = n - 1) and the distribution of t becomes closer and closer to a normal distribution. Check out this picture for a visual explanation: http://www.uwsp.edu/PSYCH/stat/10/Image87.gif
v = n1 + n2 - k n1 = 36, n2= 40 and k=2 v = 36 + 40 - 2 v = 74
If the sample consisted of n observations, then the degrees of freedom is (n-1).
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.
There are 24 df.
A T test is used to find the probability of a scenario given a specific average and the number of degrees of freedom. You are free to use as few degrees of freedom as you wish, but you must have at least 1 degree of freedom. The formula to find the degrees of freedom is "n-1" or the population sample size minus 1. The minus 1 is because of the fact that the first n is not a degree of freedom because it is not an independent data source from the original, as it is the original. Degrees of freedom are another way of saying, "Additional data sources after the first". A T test requires there be at least 1 degree of freedom, so there is no variability to test for.
the participants are representative of the population they are interested in studying
It is not negative. it is positively skewed, and it approaches a normal distribution as the degrees of freedom increase. Its shape is NEVER based on the sample size.
The number of degrees of freedom decreases, from 150, by 1 for every parameter that is estimated using the data. If the parameter is known from some other source then there is no effect on the df.
The estimated standard deviation goes down as the sample size increases. Also, the degrees of freedom increase and, as they increase, the t-distribution gets closer to the Normal distribution.
population
The data sets determine the degrees of freedom for the F-test, nit the other way around!
The t-distribution and the normal distribution are not exactly the same. The t-distribution is approximately normal, but since the sample size is so small, it is not exact. But n increases (sample size), degrees of freedom also increase (remember, df = n - 1) and the distribution of t becomes closer and closer to a normal distribution. Check out this picture for a visual explanation: http://www.uwsp.edu/PSYCH/stat/10/Image87.gif