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43 Cards in this Set
- Front
- Back
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Subjective |
Based on feeling or opinion |
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Empirical |
Based on experience |
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Theoretical |
Based on assumptions |
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Relative frequency |
a= number of times an even occurs n= number of trials =a/n |
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Complement rule |
P(Ac)=1-P(A) |
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Intersection of two events |
P(A and B) |
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Union |
P(A or B) |
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General Addition rule |
P(A or B)= P(A) + P(B) - P(A and B) |
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Disjoint events |
P(A and B)=0 |
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Multiplication rule for independent trials |
P(A and B)= P(A) * P(B) |
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Conditional Probability |
P(A/B) |
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P(A/B): |
The probability of A occurring given B has occurred |
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P(A and B)/P(B) |
P(A/B) |
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two events are independent if |
P(A)=P(A/B)=P(A/Bc) P(B)=P(B/A)=P(B/Ac) P(A and B)=P(A)*P(B) |
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Random phenomenon |
any even for which the outcome is uncertain |
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random variable |
a numerical value associated with the outcome of a random phenomenon |
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Discrete random variable |
A distinct set of numerical values associated with random phenomenon with countable outcomes. |
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mean of a discrete random variable. |
u=(sum)[xP(x)] x= expected variables P(x)=probability of expected variable |
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Binomial Random Variable conditions |
n= number of trials that has two possible outcomes each trial has a probability of success=p n trials are independent |
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Parameter of binomial random variable |
are values that completely summarize the behavior of random variables n=number of trials p=probability of success |
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n!= |
1x2x3x(n-1)xn |
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0!= |
1 |
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mean of binomial random variable |
u=np |
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std dev of binomial random variable |
o=(root)np(1-p) |
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z= |
(x-u)/o |
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standardizing allows for |
comparison |
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Models for data distribution |
shape center variability |
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Shapes can be |
unimodal symmetric bell-shaped |
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rule for normal models |
1 std dev=68% 2 std dev= 95% 3 std dev = 99.7% |
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something is unusual when |
something has a z-score of above 3 |
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Population |
Group you want to collect information from. |
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Parameter |
summary of information wanted from population. (proportion) |
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sample |
smaller group selected from population that we obtain information from. |
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Statistic |
Summary of information collected from sample. (p(hat)=proportion of sample members) |
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p(hat) infers to what? |
value of p |
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Sampling variabliltiy |
the variability in a sample statistic |
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Sampling distribution |
Many possible samples Each sample gives a sample proportion These sample proportions are quantitative values |
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n in sampling distribution is |
sample size |
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as sample size increase the sampling distribution changes in what ways |
mean: stays the same Std dev: decrease Shape: the same |
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std dev of p(hat)= |
(root)(p(1-p)/n) |
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how to tell if success or failure |
both np and n(1-p) are greater then 15 |
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N in sampling distributions are |
number of times samples are taken and analysed. |
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SD(x(hat))= |
o/(root)n |
