Use LEFT and RIGHT arrow keys to navigate between flashcards;
Use UP and DOWN arrow keys to flip the card;
H to show hint;
A reads text to speech;
23 Cards in this Set
- Front
- Back
|
Population Mean and Standard Deviation |
ū= mean; Ō= standard deviation |
|
|
Sample Mean and Standard Deviation |
ȳ= mean; s= standard deviation
|
|
|
Sampling Distribution |
ō÷√n
|
|
|
Required Assumptions |
-Randomization -10% condition -Nearly Normal |
|
|
How large does N need to be for a symmetric distribution? |
Usually 10-20 is large enough |
|
|
How large does N need to be for a skewed distribution? |
25-40 |
|
|
As the sample size gets larger, what characteristics change to the distribution model? |
-Shape gets closer to a normal model -The mean stays approximately equal to the population mean -The standard deviation decreased based on the sampling distribution formula |
|
|
If the population distribution is normal, then the sample distribution and sampling distribution will be? |
Normal |
|
|
Ifyou know the population distribution is not normal butsymmetric,the sample distribution will have a...? |
Similar shape as the population, and the sampling distribution will likely be normal if the sample size is at least 10-20. If the sample size is less than 10-20, it is possible that the sampling distribution will be symmetric but not quite normal. |
|
|
Ifyou know the population distribution is skewed, thesample distribution will be..? |
Skewed, and the sampling distribution will likely be normal if the sample size is at least 25-40. Ifthe sample size is less than 25-40, it is likely that thesampling distribution will be skewed but not as skewed as the population. |
|
|
Sampling Distributions: Statistics- Proportions vs. Means |
Proportions: use p hat; Assumptions: randomization, success/failure, and 10% Means: use y bar; Assumptions: randomization, 10%, and nearly normal condition. |
|
|
Sampling Distributions: Standard Deviation- Proportions vs. Means |
Proportions: √p(1- p)/n Means: ō/√n |
|
|
How to standardize a sampling distribution |
Y bar- mean/ standard deviation/√n |
|
|
DF |
(n- 1) |
|
|
Confidence Interval for Mean |
Y bar ± t∗ (s/√n) |
|
|
As the CI increases does the t∗ increase or decrease? |
Increases |
|
|
In order to increase the width and the margin of error, what happens to t∗? |
It also increases |
|
|
As the sample size increases, t∗ decreases |
True |
|
|
Example |
1) Random sample 2) 10%: A sample of _____ is less than 10% of the population size. 3) Nearly Normal Confidence: Sample size is sufficiently large or not sufficiently large enough for the sampling distribution to be approximately normal 4) df 6) Y bar ± t∗ (s/√n) 7) We are __% confident that the population mean number of ________ is between ______ and ______ |
|
|
Hypothesis Tests |
H₀: µ= µ₀
HA: µ< µ₀ HA: µ > µ₀ HA: µ ≠ µ₀ |
|
|
Hypothesis Tests for µ |
Assumptions are the same for CI: Assumptions: Randomization Condition, 10%, and Nearly Normal Condition |
|
|
If the null hypothesis is true... |
t= Y bar - µ₀/(s/√n) |
|
|
Rejecting and Failing to Reject the Null |
If p-value is < alpha, reject the null: there is evidence that the population mean is ________ is less than/ greater than/ or different than the null. If p-value is > alpha, fail to reject the null: there is not evidence that the population mean is _______ is less than/ greater than/ or not equal to the null. |
