Chapter 5 Flashcards

1
Q

Sample Statistics

A

Summarizes sample characteristics

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2
Q

Parameters

A

Summarize population characteristics

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3
Q

Sample Mean

A

Mean of the sample,

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4
Q

Sample Proportion

A

Proportion of sample,

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5
Q

Population Mean

A

Mean of the population,

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6
Q

Population Proportion

A

True proportion, p

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7
Q

Statistic as a RV

A

Statistic is a random variable and will have a distribution assigning probabilities to different samples

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8
Q

Sampling Distribution

A

Probability distribution of sample statistics, tells us how much a statistic would vary from sample to sample

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9
Q

Mean of Sample Proportion

A

Mean p

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10
Q

Standard Deviation of Sample Proportion

A

SD

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11
Q

When is distribution approximately normal?

A

When for a sample size n with true proportion p, both np and n(1-p) are greater than or equal to 15

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12
Q

Normal Distribution of Sample Proportion

A
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13
Q

Standard Error

A

Estimate of standard deviation of sampling distribution

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14
Q

What happens to standard deviation as sample size n increases?

A

Standard deviation decreases

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15
Q

Smaller the standard deviation…

A

…closer the sample proportion is to the population proportion

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16
Q

Empirical Rule

A

Nearly all sample proportions for size n with true proportion p will lie between

17
Q

Probability with Normal Distribution and pHat

A
18
Q

Sample mean distribution centered around…

A

True population mean mu

19
Q

Sample mean distribution standard deviation

A
20
Q

Sample Mean Normal Distribution

A
21
Q

What does increasing sample size n do to sample means?

A

Vary less and less tending toward true population mean

22
Q

Central Limit Theorem

A

As sample size increases, sampling distribution of sample mean tends to a normal distribution, even if population distribution far from bell-shaped

23
Q

Ensuring normal distribution for standard mean

A

n greater than or equal to 30

24
Q

Statistical Inference

A

Making decisions about parameters using statistics

25
Q

Point Estimation

A

Single estimate for population parameter, usually sample mean

26
Q

Interval Estimation

A

Form a confidence interval of population parameter within which true parameter value is believed to lie at a certain confidence level

27
Q

Significance Test

A

Yields decision on whether claim about value of parameter is supported by data observed from a random sample

28
Q

C + E Model

A

Center + Error ; usually C is the sample mean and E is the margin of error

29
Q

Confidence Level

A

Level of confidence with which method produces an interval that contains the true parameter value

30
Q

100% Confidence Level

A

Not relevant, would be all possible values

31
Q

Margin of Error

A

How accurately the statistic estimates unknown parameter

32
Q

General form of C + E Confidence Interval

A
33
Q

Robust

A

A procedure that performs adequately even when a particular assumption is violated

34
Q

Standard Error and T-Score as sample size increases (or, if CI decreases)

A

The two decrease and the margin of error likewise decreases, so the CI gets shorter

35
Q

How CI changes with fixed sample size if CI increases…

A

T-Score and margin of error increase so the CI gets wider

36
Q

How to think about CI

A
  1. We are X% confident that the true population mean lies between the lower and upper bounds
  2. Of 100 X% intervals calculated the same way, we expect X of them to contain the true population mean
  3. In a long series of repeated trials, X% of the intervals will contain the true population mean