Studied by 5 people

5.0(1)

get a hint

hint

1

Unbiased Estimator

when the center of the sampling distribution of the statistic is equal to the population parameter

New cards

2

Conditions for a 1 or 2 sample z-test or interval, one or two sample t test or t interval for mean or difference in means

Random, 10% condition, large counts

New cards

3

Interpreting P-Value

the probability of obtaining a test statistic as extreme or more extreme than the observed test statistic when the null hypothesis is assumed to be true

New cards

4

Population Distribution

distribution of responses for every individual of the population

New cards

5

Sample Distribution

Distribution of responses for a single sample

New cards

6

Sampling Distribution

distribution of all values for the statistic for all possible samples of a given size from a population

New cards

7

Interpret Confidence Interval

“We are C% confident that the confidence interval from *to* captures the [population parameter in context]

New cards

8

How to find the point estimate given confidence interval

Suppose confidence interval is (A,B)

The point estimate is the average of A and B.

New cards

9

How to find the MoE given confidence interval

Suppose confidence interval is (A,B)

MoE = B - (Point Estimate)

New cards

10

Calc function for one sample Z-test for p

#5: 1-PropZTest

New cards

11

Calc function for one sample t-test for µ

#2: T-Test

New cards

12

Degrees of Freedom (df) NOT for chi-squared

n-1

New cards

13

Test statistic formula One sample t-test for µ

t=(X BAR)-µ₀ / s/√n

New cards

14

Test statistic formula two-sample t-test for µ₁-µ₂

t=(X BAR₁ - X BAR₂) / √s₁²/n₁ + s₂²/n₂ OR 2-samp T-Test

New cards

15

Power of a Test

the probability a test will correctly reject the null hypothesis, given the alternate hypothesis is true

New cards

16

P(Type II Error)

P(Type II error) = 1-P(Power)

New cards

17

Test statistic formula for One-Sample z-test for p

z=(P HAT) - p₀ / √p₀(1-p₀)/n

New cards

18

Calc function for one sample t-interval for µ

#8: TInterval

New cards

19

CI formula for one sample z-interval for p

(P HAT) ± z* √ (P HAT) (1-P HAT)/n

New cards

20

CI Formula for one sample t-interval for µ

(X BAR) ± t* s/√n

New cards

21

Calc function for one sample z-interval for p

A: 1-PropZInt

New cards

22

What factors affect confidence interval width

Decreases as n increases, increases as conf level increases

New cards

23

Two sample z-interval for p₁-p₂ confidence interval

2-propZInt

New cards

24

Why do we check the condition for random?

So we can generalize to the population from which the sample was selected

New cards

25

Why do we check the 10% condition

so sampling without replacement is okay, and we can use the stated formula for standard deviation

New cards

26

Why do we check the normal/large sample condition

so the sampling distribution is approximately normal

New cards

27

What does rejecting the null mean?

There is statistcial evidence to support the alternative hypothesis

New cards

28

What does failing to reject the null mean

there is not convincing statistical evidence to support the alternative hypothesis

New cards

29

Confidence interval for two sample t-interval for µ₁-µ₂

2-sampTInt on calc

New cards

30

Type I error

when the null hypothesis is true, but is rejected

New cards

31

Type II erorr

when the alternate hypothesis is true, and the null is not rejected

New cards

32

How to choose the right inference procedure?

does the scenario describe mean(s), proportion(s), counts, or slope?

Does the scenario describe one sample, 2 sample, or paired data

Does the scenario describe a test or confidence interval?

New cards

33

P HAT Combined

X₁+X₂/n₁+n₂

New cards

34

Test statistic for two sample z-test for p₁-p₂

2-propZTest on calc

New cards

35

Interpret the confidence level

In repeated random sampling with the same sample size, approximately C% of confidence intervals created will capture the [population parameter in context].

New cards

36

Parameter

a number that describes the population

New cards

37

Statistic

number that describes the sample

New cards

38

test statistic formulat-test for slope

b-β₀/SE(sub b) (df=n-2)

New cards

39

Conditions for Chi-square test

Random, 10%, large counts (all expected counts ≥5)

New cards

40

CI formula t-interval for slope

b±t* SE(sub b) (df=n-2)

New cards

41

test statistic formula - chi-square test for goodness of fit

x²=Σ (observed-expected)²/expected (df = #of groups -1)

New cards

42

how to calculate expected counts in a chi-sguare test for homogeneity/independence

expected count = (row total)(column total) / table total

New cards

43

Test statistic formula chi-square test for goodness of fit

X²=Σ (observed-expected)²/expected (df=number of groups-1)

New cards

44

Test statistic formula chi-square test for homogeneity/independence

X²=Σ (observed-expected)²/espected (df= (rows-1)(columns-1))

New cards

45

Significance test conclusion

Our P-value [p=___]__ is LESS/GREATER than the significance level [ALPHA = _]. Therefore we REJECT/FAIL TO REJECT the null. We DO/NOT have statistical evidence for [alternate hypothesis].

New cards

46

How to remember how to reject null or not?

If the P-value is low, reject that hoe. If the P-value is high, the null’s that guy.

New cards

47

GoF Test Hypothesis

The true distribution of [VARIABLE] is equally proportional as [ ] claims

New cards

48

GoF Test Alt Hypothesis

The true distribution of [VARIABLE] is different than claimed.

New cards

49

Chi-Squared test for homogeneity hypothesis

There is no difference in the true distribution of [VAR] for [POP 1] and [POP 2].

New cards

50

Chi-Squared test for homogeneity alternate hypothesis

There is a difference in the true distribution of [VAR] for [POP 1] and [POP 2].

New cards

51

Conditions for T-Test and T-Interval for slope

Truly linear (no pattern in the residual plot), Independent (10%), Normal distribution of residuals at each X (histogram of residuals is normal-ish), Equal std. dev. of resid. for all X (no heteroskedacisity), random sample

New cards

52

10% condition

n ≤ 1/10 N

New cards

53

Large Counts Condition

np ≥ 10 and n(1-p) ≥ 10

New cards

54

Central Limit Theorem

n≥30

New cards