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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. A consistent - repeated deviation of the sample statistic from the population parameter in the same direction when many samples are taken.






2. Is a function that gives the probability of all elements in a given space: see List of probability distributions






3. Is often denoted by placing a caret over the corresponding symbol - e.g. - pronounced 'theta hat'.






4. A list of individuals from which the sample is actually selected.






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






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






7.






8. Two events are independent if the outcome of one does not affect that of the other (for example - getting a 1 on one die roll does not affect the probability of getting a 1 on a second roll). Similarly - when we assert that two random variables are i






9. Where the null hypothesis is falsely rejected giving a 'false positive'.






10. ?r






11. 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.






12. 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






13. Is the result of applying a statistical algorithm to a data set. It can also be described as an observable random variable.






14. 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






15. E[X] :






16. Have no meaningful rank order among values.






17. Have imprecise differences between consecutive values - but have a meaningful order to those values






18. (or atomic event) is an event with only one element. For example - when pulling a card out of a deck - 'getting the jack of spades' is an elementary event - while 'getting a king or an ace' is not.






19. Describes the spread in the values of the sample statistic when many samples are taken.






20. A collection of events is mutually independent if for any subset of the collection - the joint probability of all events occurring is equal to the product of the joint probabilities of the individual events. Think of the result of a series of coin-fl






21. Are written in corresponding lower case letters. For example x1 - x2 - ... - xn could be a sample corresponding to the random variable X.






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






23. 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'






24. There are four main levels of measurement used in statistics: Each of these have different degrees of usefulness in statistical research.






25. Data are gathered and correlations between predictors and response are investigated.






26. A group of individuals sharing some common features that might affect the treatment.






27. Is a subset of the sample space - to which a probability can be assigned. For example - on rolling a die - 'getting a five or a six' is an event (with a probability of one third if the die is fair).






28. Cov[X - Y] :






29. Because variables conforming only to nominal or ordinal measurements cannot be reasonably measured numerically - sometimes they are grouped together as






30. 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






31. Some commonly used symbols for population parameters






32. Is the length of the smallest interval which contains all the data.






33. 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






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






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






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






38. 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.






39. Is a measure of the 'peakedness' of the probability distribution of a real-valued random variable. Higher kurtosis means more of the variance is due to infrequent extreme deviations - as opposed to frequent modestly sized deviations.






40. Statistics involve methods of organizing - picturing - and summarizing information from samples or population.






41. The standard deviation of a sampling distribution.






42. Error also refers to the extent to which individual observations in a sample differ from a central value - such as






43. Is its expected value. The mean (or sample mean of a data set is just the average value.






44. Is that part of a population which is actually observed.






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






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






47. When there is an even number of values...






48. Rejecting a true null hypothesis.






49. Some commonly used symbols for sample statistics






50. 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







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