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CLEP General Mathematics: Probability And Statistics

Subjects : clep, math
Instructions:
  • Answer 50 questions in 15 minutes.
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  • Match each statement with the correct term.
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This is a study tool. The 3 wrong answers for each question are randomly chosen from answers to other questions. So, you might find at times the answers obvious, but you will see it re-enforces your understanding as you take the test each time.
1. The probability distribution of a sample statistic based on all the possible simple random samples of the same size from a population.






2. Is the probability of some event A - assuming event B. Conditional probability is written P(A|B) - and is read 'the probability of A - given B'






3. A subjective estimate of probability.






4. (or expectation) of a random variable is the sum of the probability of each possible outcome of the experiment multiplied by its payoff ('value'). Thus - it represents the average amount one 'expects' to win per bet if bets with identical odds are re






5. Are simply two different terms for the same thing. Add the given values






6. Many statistical methods seek to minimize the mean-squared error - and these are called






7. Can be a population parameter - a distribution parameter - an unobserved parameter (with different shades of meaning). In statistics - this is often a quantity to be estimated.

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8. The errors - or difference between the estimated response y^i and the actual measured response yi - collectively






9. (or multivariate random variable) is a vector whose components are random variables on the same probability space.






10. (also called statistical variability) is a measure of how diverse some data is. It can be expressed by the variance or the standard deviation.






11. A numerical facsimilie or representation of a real-world phenomenon.






12. A numerical measure that describes an aspect of a population.






13. In number theory - scatter plots of data generated by a distribution function may be transformed with familiar tools used in statistics to reveal underlying patterns - which may then lead to






14. Is the probability of an event - ignoring any information about other events. The marginal probability of A is written P(A). Contrast with conditional probability.






15. Summarize the population data by describing what was observed in the sample numerically or graphically. Numerical descriptors include mean and standard deviation for continuous data types (like heights or weights) - while frequency and percentage are






16. Is the study of the collection - organization - analysis - and interpretation of data. It deals with all aspects of this - including the planning of data collection in terms of the design of surveys and experiments.






17. Gives the probability of events in a probability space.






18. Occurs when a subject receives no treatment - but (incorrectly) believes he or she is in fact receiving treatment and responds favorably.






19. The probability of correctly detecting a false null hypothesis.






20. The collection of all possible outcomes in an experiment.






21. Is defined as the expected value of random variable (X -






22. In Bayesian inference - this represents prior beliefs or other information that is available before new data or observations are taken into account.






23. Long-term upward or downward movement over time.






24. Design of experiments - using blocking to reduce the influence of confounding variables - and randomized assignment of treatments to subjects to allow unbiased estimates of treatment effects and experimental error. At this stage - the experimenters a






25. Also called correlation coefficient - is a numeric measure of the strength of linear relationship between two random variables (one can use it to quantify - for example - how shoe size and height are correlated in the population). An example is the P






26. A variable describes an individual by placing the individual into a category or a group.






27. Changes over time that show a regular periodicity in the data where regular means over a fixed interval; the time between repetitions is called the period.






28. Descriptive statistics and inferential statistics (a.k.a. - predictive statistics) together comprise






29. A variable that has an important effect on the response variable and the relationship among the variables in a study but is not one of the explanatory variables studied either because it is unknown or not measured.






30. Two variables such that their effects on the response variable cannot be distinguished from each other.






31. Patterns in the data may be modeled in a way that accounts for randomness and uncertainty in the observations - and are then used for drawing inferences about the process or population being studied; this is called






32. Is the set of possible outcomes of an experiment. For example - the sample space for rolling a six-sided die will be {1 - 2 - 3 - 4 - 5 - 6}.






33. Planning the research - including finding the number of replicates of the study - using the following information: preliminary estimates regarding the size of treatment effects - alternative hypotheses - and the estimated experimental variability. Co






34. Interpretation of statistical information in that the assumption is that whatever is proposed as a cause has no effect on the variable being measured can often involve the development of a






35. Are two related but separate academic disciplines. Statistical analysis often uses probability distributions - and the two topics are often studied together. However - probability theory contains much that is of mostly of mathematical interest and no






36. Performing the experiment following the experimental protocol and analyzing the data following the experimental protocol. 4. Further examining the data set in secondary analyses - to suggest new hypotheses for future study. 5. Documenting and present






37. Is a sample space over which a probability measure has been defined.






38. Where the null hypothesis fails to be rejected and an actual difference between populations is missed giving a 'false negative'.






39. (e.g. ? - b) are commonly used to denote unknown parameters (population parameters).






40. Given two jointly distributed random variables X and Y - the marginal distribution of X is simply the probability distribution of X ignoring information about Y.






41. Is the probability distribution - under repeated sampling of the population - of a given statistic.






42. Can refer either to a sample not being representative of the population - or to the difference between the expected value of an estimator and the true value.






43. Another name for elementary event.






44. When info. in a contingency table is re-organized into more or less categories - relationships seen can change or reverse.

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45. Var[X] :






46. Have both a meaningful zero value and the distances between different measurements defined; they provide the greatest flexibility in statistical methods that can be used for analyzing the data






47. Is a measure of the asymmetry of the probability distribution of a real-valued random variable. Roughly speaking - a distribution has positive skew (right-skewed) if the higher tail is longer and negative skew (left-skewed) if the lower tail is longe






48. The result of a Bayesian analysis that encapsulates the combination of prior beliefs or information with observed data






49. A measure that is relevant or appropriate as a representation of that property.






50. Is used to describe probability in a continuous probability distribution. For example - you can't say that the probability of a man being six feet tall is 20% - but you can say he has 20% of chances of being between five and six feet tall. Probabilit