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H_0:μ_1-μ_2=d assuming heterogeneity t=((x ̅_1-x ̅_2 )-(μ_1-μ_2 ))/√((s_1^2)/n_1 +(s_2^2)/n_2 ) Student t(υ)
ν=((s_1^2)/n_1 +(s_2^2)/n_2 )^2/[((s_1^2)/n_1 )^2/(n_1-1)+((s_2^2)/n_2 )^2/(n_2-1)]
H_0:μ_1-μ_2=d assuming homogeneity t=((x ̅_1-x ̅_2 )-(μ_1-μ_2 ))/(s_p √(1/n_1 +1/n_2 )) Student t(υ) ν=n_1+n_2-2
H_0:μ_d=0 t=(d ̅-μ_d)/(s_d⁄√n) Student t(υ) ν=n-1
H_0:p_1-p_2=0 z=((p ̂_1-p ̂_2 )-(p_1-p_2 ))/√((p ̅(1-p ̅))/n_1 +(p ̅(1-p ̅))/n_2 ) N(0,1) NA
H_0:σ_1^2=σ_2^2 F=(s_Larger^2)/(s_Smaller^2 ) F_(ν_1,ν_2 ) ν_i=n_i
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What does it means if the standard deviation is large?
that you have a large variance in the population and/or your sample size is too small
What are adverse variances?
Adverse variances means unfavourable variance which is actual expenses are more than budgted variance.
An analysis of variance differs from a t test for independent means in that an analysis of variance?
Analysis of Variance (ANOVA) compares 3 or more means. The t-test would only compare 2 means.
What is the difference between heterogeneity and homogeneity in statistics?
Homogeneity means that the statistical properties of the variable which is being studied remain the same across the population. Heterogeneity means that they do not: it could be that the mean changes between different subsets of the population or the variance does.
What does homogeneity of variance mean?
It means that the variance remains the same across the range of values of the variable.
What does it mean to say that the sample variance provides an unbiased estimate of the population variance?
It means you can take a measure of the variance of the sample and expect that result to be consistent for the entire population, and the sample is a valid representation for/of the population and does not influence that measure of the population.
What does it means if the standard deviation is large?
that you have a large variance in the population and/or your sample size is too small
What are adverse variances?
Adverse variances means unfavourable variance which is actual expenses are more than budgted variance.
An analysis of variance differs from a t test for independent means in that an analysis of variance?
Analysis of Variance (ANOVA) compares 3 or more means. The t-test would only compare 2 means.
(b)For a set of numbers 1,4,7. Find population mean and Variance. Draw up frequency distributions of means of all possible samples of size 3 when sampling is carried out with replacement. Find mean and variance of distribution?
lowest
What is the difference between heterogeneity and homogeneity in statistics?
Homogeneity means that the statistical properties of the variable which is being studied remain the same across the population. Heterogeneity means that they do not: it could be that the mean changes between different subsets of the population or the variance does.
The fact that probabilities are proportions means that they?
The fact that probabilities are proportions means that they are less than or equal to 1.
What does homogeneity of variance mean?
It means that the variance remains the same across the range of values of the variable.
Why is the variance of a distrubtion of means smaller than the original popluation variance?
It is a result of the Central Limit Theorem.
Why is the standard deviation of a distribution of means smaller than the standard deviation of the population from which it was derived?
The reason the standard deviation of a distribution of means is smaller than the standard deviation of the population from which it was derived is actually quite logical. Keep in mind that standard deviation is the square root of variance. Variance is quite simply an expression of the variation among values in the population. Each of the means within the distribution of means is comprised of a sample of values taken randomly from the population. While it is possible for a random sample of multiple values to have come from one extreme or the other of the population distribution, it is unlikely. Generally, each sample will consist of some values on the lower end of the distribution, some from the higher end, and most from near the middle. In most cases, the values (both extremes and middle values) within each sample will balance out and average out to somewhere toward the middle of the population distribution. So the mean of each sample is likely to be close to the mean of the population and unlikely to be extreme in either direction. Because the majority of the means in a distribution of means will fall closer to the population mean than many of the individual values in the population, there is less variation among the distribution of means than among individual values in the population from which it was derived. Because there is less variation, the variance is lower, and thus, the square root of the variance - the standard deviation of the distribution of means - is less than the standard deviation of the population from which it was derived.
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
What is a positive or negative variance of a budget?
A variance is the difference between the projected budget and the actual performance for a particular account. A negative variance means that the budgeted amount was greater than the actual amount spent. A positive variance means that the budgeted amount was less than the actual amount spent. Note there is some debate over whether a negative variance means an underrun or an overrun. The Project Management Institute, however, endorses the accepted convention that a negative variance is a bad thing, and a positive variance a good thing.
