EDA MIDTERM Flashcards

1
Q

Means possibility. A branch of mathematics that deals with the occurrence of a random event. The value is expressed from zero to one.

A

Probability

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2
Q

4 Key Probability Terms

A

Experiment, Outcome, Sample Space, Event

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3
Q

2 types of Event

A

Simple Event and Compound Event

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4
Q

process where specific results are obtained

A

Experiment

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5
Q

single result of a probability experiment

A

Outcome

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6
Q

set of all possible outcomes for a probability experiment

A

Sample Space

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7
Q

set of outcomes of a probability experiment; a subset of the sample space

A

Event

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8
Q

Rolling a pair of dice has a sample space of 36 outcomes [(1,1), (1,2),
(1,3), (1,4) …etc. or just simply 6 x 6). TRUE OR FALSE?

A

TRUE

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9
Q

one outcome (rolling 4 in one die)

A

simple event

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10
Q

two or more outcomes (rolling an even number on a die then getting a head in a toin coss)

A

Compound Event

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11
Q

3 MainTypes of Probability

A

Classical, Empirical, Subjective

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12
Q

each outcome in a sample space is equally likely to occur

A

Classical Probability

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13
Q

each outcome in a sample space is NOT equally likely to occur

A

Empirical Probability

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14
Q

an e d u c a t e d guess or estimate

A

Subjective Probability

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15
Q

Key rules in Probability T or F:
The probability of a n event is always between 0 and 1. No negative probabilities or
greater than 1.

A

TRUE

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16
Q

Key rules in Probability T or F: The sum of all outcomes in a sample space is always equal to 1.

A

TRUE

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17
Q

Key rules in Probability T or F: It is impossible for an event to occur, the probability of that event is 0.

A

TRUE

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18
Q

Key rules in Probability T or F: If the event is certain to occur, the probability is 1.

A

TRUE

19
Q

Types of Probability Distribution

A

Discrete Probability Distribution and Continuous Probability Distribution

20
Q

Types of Discrete Probability Distribution

A

Binomial, Poisson,Hypergeometric

21
Q

Types of Continuous Probability Distribution

A

Normal and Exponential

22
Q

is the number of successes that result from a hypergeometric
experiment.

A

hypergeometric random variable

23
Q

The probability distribution of a hypergeometric random variable is called
a

A

Hypergeometric Distribution

24
Q

is a process that uses sample statistics to test a claim about the value of a population parameter

A

Hypothesis Test

25
Q

A verbal statement, or claim, about a
population parameter is called a

A

Statistical Hypothesis

26
Q

is a statistical hypothesis that contains a
statement of equality such as ≤ ,=, or ≥ .

A

Null Hypothesis (Ho)

27
Q

is the complement of the null
hypothesis. It is a statement that must be true if H0 is false and contains a statement of inequality such as >,≠ ,or <.

A

Alternative Hypothesis (Ha)

28
Q

divides the nonrejection region from the rejection region.

A

Critical Value

29
Q

occurs if the null hypothesis isrejected when it istrue and should not be rejected. The probability of a type Ierror occurring is α.

A

TYPE I ERROR

30
Q

occurs if the null hypothesis isnot rejected when it isfalse and should be rejected. The probability of a type IIerror occurring is β .

A

TYPE II ERROR

31
Q

No matter which hypothesis represents the claim,always begin the hypothesis test assuming that the null hypothesis is true. At the end of the test, one of two decisions will be made:

A
  1. Reject the null hypothesis
  2. Do not reject the null hypothesis
32
Q

can be used when the population is normal and variance is known , or for any population when the sample size n is at least 30.

A

Z-TEST

33
Q

will be used when the population is normal and variance is unknown , or for any population when the sample size n is less than 30.

A

T-TEST

34
Q

for proportion is a statistical test for a
population proportion.

A

The z-test

35
Q

means never having to say you are certain.

A

Statistics

36
Q

Types of Estimates

A

Point and Interval Estimate

37
Q

is a single value (statistics) used to estimate a population value (parameter).

A

Point Estimate

38
Q

States the range within which a population parameter probably lies

A

Interval Estimate

39
Q

symbolized by (1 – α) x 100%, where α is the
proportion in the tails of the distribution that is
outside the confidence interval

A

Confidence Level

40
Q

symbolized by α = 1 – (confidence level), it is the
proportion in the tails of the distribution that is
outside the confidence interval

A

Level of Significance (a)

41
Q

value of Z needed for constructing a
confidence interval

A

Critical Value

42
Q

is a range of possible values for an
unknown parameter and is associated
with confidence level
(Point Estimate + Margin of Error)

A

Confidence Interval

43
Q

is the mathematical estimation of the
number of subjects/units to be included in
a study

A

Sample Size Determination