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78 Cards in this Set
- Front
- Back
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statistics
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science of collecting, summarizing, analyzing, and interpreting data.
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descriptive statistics
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summarizing data
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inferential statistics
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analyzing and interpreting
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probablility
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determining relative frequency of events
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deductive thought process
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known facts imply new facts
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inductive thought process
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observations infer properties
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frequency distribitution
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2 column table including values of the variable and its frequency
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stem and leaf plot
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displays distribution of values of quantitative variables.
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skewness
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direction of longest tail
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histogram
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graphs values of quantitative variables on x-axis and frequency on Y-axis
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Sample mean (formula)
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X/=Exi/n
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variance (symbol)
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s^2, distance from mean
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sample Standard deviation (symbol)
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s, square root of variance
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scatterplot
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plot elements as a point on 2-d axis
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linear correlation coefficient
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stregnth of relationship between x and y (r)
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probability experiment
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random process, distinct outcomes
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outcome
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result of probability experiment
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sample space
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S, set of all possible outcomes of probability experiment
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event
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collection of outcomes from S
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null event
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no outcomes
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simple event
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one outcome
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union event (U)
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or
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Intersection event ()
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and
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mutually exclusive
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no outcomes in common
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formula for equally likely outcomes
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P(A)=# of outcomes in A/# of outcomes in S
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Independent events
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knowing whether one event occurs doesn't change the probability that the other event occurs
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complement rule (formula)
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P(A)=1-P(A')
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addition rule (formula)
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P(AUB)=P(A)+P(B)
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multiplication rule (formula)
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P(AB)=P(A)P(B)
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condtional probability rule (formula)
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P(BlA)=P(AB)/P(A)
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random variable
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assign numbers to each outcome of a probability experiment.
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discrete random variable
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random variable that takes finite number of values
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probability distribution of a discrete random variable
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assigns probabilities to each value of a drv
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mean of a discrete random variable (formula)
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E(all y)yp(y)
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continuous random variable
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random variable whose value can be any number in 1 or more intervals
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probability density funtion of a continuous random variable
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f(y) likelihood
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standard normal random variable (symbol)
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Z, table-zx
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standardizing random variable (formula)
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z=Y-u/e
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population
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large set of elements of interest
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parameter
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characteristic of a population
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sample
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draw random with replacement from a population
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statistic
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characteristic of a sample
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point estimator
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random process used to obtain guess for a parameter
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estimate
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number obtained from point estimator
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unbiased estimator
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statistic used to estimate a parameter if the mean of its sampling distribution is equal to its parameter
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sampling distribution X/(formula)
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N(u,e/SRn)
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interval estimator
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point estimator +- MoE
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MoE
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table value x standard error
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hypothesis testing
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decision making process between two alternative statements about a population
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type 1 error
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rejecting true null hypothesis
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type 2 error
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failing to reject false null hypothesis
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level of significance
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compares severity of errors
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test statistic
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describes Ho
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rejection region
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values of the test statistic that reject Ho and support Ha
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S.v. of T.s.
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computed value of the test statistic using data collected
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p-value
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probablity of test statistic being more supportive of Ha
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critical value
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cut off between rejection region and acceptance region
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independent random samples
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random samples taken from each popuation with none of the elements in either sample related to any element in other samples
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pooled estimator
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weighted average of 2 sample variances
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sampling distribution X/(formula)
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N(u,e/SRn)
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interval estimator
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point estimator +- MoE
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MoE
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table value x standard error
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hypothesis testing
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decision making process between two alternative statements about a population
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type 1 error
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rejecting true null hypothesis
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type 2 error
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failing to reject false null hypothesis
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level of significance
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compares severity of errors
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test statistic
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describes Ho
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rejection region
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values of the test statistic that reject Ho and support Ha
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S.v. of T.s.
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computed value of the test statistic using data collected
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p-value
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probablity of test statistic being more supportive of Ha
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critical value
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cut off between rejection region and acceptance region
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independent random samples
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random samples taken from each popuation with none of the elements in either sample related to any element in other samples
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pooled estimator
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weighted average of 2 sample variances
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response variable
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random variable whose value is determined by a random process
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explanatory variable
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variable controlled by experimenter
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goals of regression analysis
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determine relationship between the explanatory variable and response variable and find the best fit line
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outlier (formula)
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Q1-1.5 x IQR, Q3+1.5 x IQR
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sample proportion (formula)
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p^, x1+x2+x3.../n
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