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### 78 Cards in this Set

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