Stats And Prob Q4

Front 1

- Decision-making process for evaluating claims about a population based on characteristics of a sample purposedly coming from that population

- decision whether the characteristics is acceptable or not

Back 1

Hypothesis testing

Front 2

2 types of hypothesis

Back 2

Null hypothesis H0 and alternative hypothesis H1

Front 3

- Type of hypothesis

- statement that there's no difference between a parameter or a specific value or that there is no difference between two parameters

Back 3

Null hypothesis

Front 4

- type of hypothesis

- statement that there is a difference between a parameter or a specific value or that there is a difference between two parameters

Back 4

Alternative hypothesis

Front 5

Null hypothesis symbols

Back 5

Two tailed: =

Right-tailed: ≤

Left-tailed: ≥

Front 6

Alternative hypothesis symbols (rejection region always depends on H1)

Back 6

Two-tailed: ≠

Right-tailed: >

Left-tailed: <

Front 7

Non-directional symbols:

Directional symbols:

Back 7

: = and ≠

: > and ≤ (right-tailed bc greater than ang H1)

< and ≥ (left-tailed bc less than ang H1)

Front 8

A non-directional test is also called _____.

A directional test may either be ___ or ___.

Back 8

Two-tailed

Left-tailed or right-tailed

Front 9

Increases, greater, efficient, improves, effective

Back 9

Right-tailed direction > or ≤

Front 10

Less than, decrease, smaller

Back 10

Left-tailed < or ≥

Front 11

H0 is true - rejected

H0 is false - rejected

Back 11

:Type 1 error

:Correct decision

Front 12

H0 is true - accept or do not reject

H0 is false - accept or do not reject

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:correct decision

:Type II error

Front 13

5 steps in hypothesis testing

Back 13

1. Identify claim (μ) and formulate null and alternative hypo (H⁰ and H¹)

2. •level of significance (a - alpha)

•one/two-tailed (based on alternative hypo sign)

•test statistics (z or t-test)

•critical value (base on z or t-test

•draw rejection region (base on critical value)

3. Computation of test value

z/t = [(X̄ - μ)√n]/σ

4. Decision: accept or reject H⁰

5. Conclusion: answer the question

Front 14

Formula of test statistics (z or t-test)

Back 14

z/t = [(X̄ - μ)√n]/σ

X̄ - sample mean

μ - population mean

n - sample size (number of samples)

σ - standard dev

Front 15

Rejection region symbol:

Back 15

H0: ≥ or = (depending on claim)

H1: < (less than so left-tailed)

Front 16

Rejection region symbol:

Back 16

H0: ≤ or = (depends on claim)

H1: > (greater than so right-tailed)

Front 17

Rejection region symbol:

Back 17

H0: =

H1: ≠

Front 18

= words

Back 18

is, equal to, the same as, not changed from

Front 19

≠ words

Back 19

not equal, different from, changed from, not the same as

Front 20

> words

Back 20

increased, at least, greater than, higher than, bigger than, longer than

Front 21

< words

Back 21

decreased, at most, less than, reduced from, smaller than, lower than, shorter than,

Front 22

3 Illustration of rejection region

Back 22

Rejection/critical region

Non-rejection/acceptance region

Critical value

Front 23

- set of all values of the test statistic that causes us to reject the null hypothesis

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Critical or rejection region

Front 24

- set of all values of the test statistic that causes us to fail to reject a null hypothesis

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Non-rejection or acceptance region

Front 25

- point or boundary on the test distribution that is compared to the test statistic to determine if the null hypothesis would be rejected

Back 25

Critical value

Front 26

Level of significance:

0.10 and 0.05 is used for ___

0.01 and 0.001 is used for __

Back 26

:social teat

:medical test

Front 27

one-tailed:

Back 27

H⁰: μX̄ ≤ μ

H¹: μX̄ > μ

or

H⁰: μX̄ ≥ μ

H¹: μX̄ < μ

Front 28

Point estimator formula

Back 28

p̂ = x/n

x - # of data with same characteristics

n - sample size

Front 29

2 criteria for z-test or p (population)

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np > 5 & nq > 5

p - population mean

q - (1-p)

n - sample size

p̂ - sample proportion

Front 30

-test statistic is appropriate to use in testing hypothesis involving population proportion

Back 30

z-test

Front 31

Formula for test of population proportion

Back 31

Front 32

Formula for test of significance (z or t test)

Back 32

Front 33

All ___ can cause correlation but not all correlation can cause ___

Back 33

causation

Front 34

- association between 2 variables

- direct relationship

- points to right /

- one variable increases, the other also increases

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positive correlation

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- association between 2 variables

- indirect/inverse relationship

- points to left \

- one variable increases, the other decreases

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negative correlation

Front 36

- this is indicated by closeness of points to the trend line

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strength of correlation

Front 37

6 strengths of correlation (p.v.m.m.v.z.)

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perfect

very high (strong)

moderately high

moderately low

very low (weak)

and zero correlation

Front 38

- used if they can assure a normal distribution

Back 38

data r parametrics

Front 39

What do you need to determine in data r parametrics?

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Σ: x, y, xy, x^2, y^2, and n

Front 40

negative sign indicates what in correlation

Back 40

inverse correlation \

Front 41

- parametric statistics which measures the degree of correlation between 2 variables

- states what type of relationship exists

Back 41

pearson r

Front 42

When other variables are controlled or manipulated just like in an experiment, this shows what type of correlation?

Back 42

perfect correlation

Front 43

shape or form of scatterplot can be describes as __ or __

Back 43

linear or non-linear

Front 44

3 steps in test of population proportion

Back 44

1. determine given

2. get np nq, check criteria if np and nq > 5

3. solve for z-value

- identify hypothesis/claims (H0 H1)

- determine direction, critical value, draw rr

- solve p̂

- compute z-value

- decision (accept or reject H0)

- conclusion

Front 45

Formula for pearson r

Back 45

Front 46

- data involving only a single variable

Back 46

univariate data

Front 47

- data involving two variables

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bivariate data

Front 48

2 variables in bivariate data

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independent and dependent

Front 49

- visual display/graphical representation of the relationship of variables in bivariate data

- series of points plotted on a rectangular coordinate plane

Back 49

scatterplot

Front 50

where is the IV in rectangular coordinate plane?

where is the DV in rectangular coordinate plane?

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x-axis

y-axis

Front 51

- "stand alone"

- value can change, be controlled, manipulated and can influence other variable

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independent variable

Front 52

- influenced or affected by independent variable

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dependent variable

Front 53

- statistical procedure used to determine and describe the relationship between 2 variables

- compare relationship between 2 variables

- what type of relationship

Back 53

correlation analysis

Front 54

- diagonal line closest to the point

- tells direction of correlation that exists between the variables

Back 54

trend line

Front 55

3 directions of correlation

Back 55

positive, negative, and zero

Front 56

if population standard deviation ( σ ) is given, use what test statistics?

Back 56

z-test

Front 57

if sample standard deviation ( s ) is given, use what test statistics?

Back 57

t-test

Front 58

- an entire group of people, objects, or events which all have at least one characteristic in common and must be defined specifically and unambiguously

Back 58

population

Front 59

- refers to any part of a population regardless of whether it is a representative or not

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sample

Front 60

- refers to a part of a population with a particular attribute, expressed as a fraction, decimal, or percentage of the whole population

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population proportion

Front 61

-proportion of individuals in a sample sharing a certain trait

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sample proportion

Front 62

For a large size of sample proportions, use...

(test of population proportion)

Back 62

Central Limit Theorem