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stats ch 21-22 vocab (1)
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stats ch 21-22 vocabVariances of Independent Random Variables Add - CORRECT ANSWER-The
variance of a sum or difference of independent random variables is the sum of the
variances of those variables
(Never subtract variances)
Sampling Distribution of the Difference Between Two Proportions - CORRECT
ANSWER-The sampling distribution of p1 - p2 is under the appropriate assumptions,
modeled by a Normal model with mean = p1-p2 and the standard deviation = square
root of (p1q1/n1) + (p2q2/n)
Two-Proportion Z-Interval - CORRECT ANSWER-Gives a confidence interval for the
true difference in proportions, p1 - p2, in two independent groups
The confidence interval is (p1-p2) +- z* x SE(p1-p2)
Pooling - CORRECT ANSWER-When we believe a proportion is the same in two
different groups, we can get a better estimate of the common proportion by
combining the data from our two samples. p= x1+x2/n1+n2 The resulting standard
error is based on more data and hence more reliable (if the null hypothesis is true).
Two-Proportion Z-Test - CORRECT ANSWER-Test the null hypothesis H0: p1 - p2 =
0 by refereeing the statistic z = (p1 - p2) / SE(pooled) to a standard Normal model
Student's t - CORRECT ANSWER-A family of distributions indexed by its degrees of
freedom. They are unimodal, symmetric, and bell-shaped, but generally have fatter
tails and a narrower center than the normal model. As degrees of freedom increase,
they approach the normal model
Degrees of freedom for Student's t-distribution - CORRECT ANSWER-equal to n-1,
where n is the sample size
One Sample T-Interval - CORRECT ANSWER-A one-sample t-interval for the
population mean is mean =- t* x SE(mean) when SE = s/square root(n)
Nearly Normal Condition - CORRECT ANSWER-Using a t-model requires the
Normality Assumption. We look at a histogram of the sample data to see if it's
plausible that our sample could have come from a population that's normally
distributed. It may appear only roughly unimodal and symmetric, but we should not
proceed with inference if the sample is strongly skewed or there are outliers present.
This concern is very critical in small samples, but less important with sample sizes of
30 to 40 or more.
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