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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. Another name for elementary event.






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






3. In the long run - as the sample size increases - the relative frequencies of outcomes approach to the theoretical probability.






4. Samples are drawn from two different populations such that there is a matching of the first sample data drawn and a corresponding data value in the second sample data.






5. The objects described by a set of data: person (animal) - place - and - thing. (SUBJECTS)






6. Is the function that gives the probability distribution of a random variable. It cannot be negative - and its integral on the probability space is equal to 1.






7. 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.






8. Is the probability of two events occurring together. The joint probability of A and B is written P(A and B) or P(A - B).






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






10. A sample selected in such a way that each individual is equally likely to be selected as well as any group of size n is equally likely to be selected.






11. A variable has a value or numerical measurement for which operations such as addition or averaging make sense.






12. 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






13. 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






14. 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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15. Probability of accepting a false null hypothesis.






16. Is the most commonly used measure of statistical dispersion. It is the square root of the variance - and is generally written s (sigma).






17. A subjective estimate of probability.






18. Can be - for example - the possible outcomes of a dice roll (but it is not assigned a value). The distribution function of a random variable gives the probability of different results. We can also derive the mean and variance of a random variable.






19. 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}.






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






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






22. 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






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






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






25. Is data that can take only two values - usually represented by 0 and 1.






26. E[X] :






27. Gives the probability distribution for a continuous random variable.






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






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. Is a process of selecting observations to obtain knowledge about a population. There are many methods to choose on which sample to do the observations.






31. Consists of a number of independent trials repeated under identical conditions. On each trial - there are two possible outcomes.






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






33. 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






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






35. Is inference about a population from a random sample drawn from it or - more generally - about a random process from its observed behavior during a finite period of time.






36. Uses patterns in the sample data to draw inferences about the population represented - accounting for randomness. These inferences may take the form of: answering yes/no questions about the data (hypothesis testing) - estimating numerical characteris






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






38. 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






39. Rejecting a true null hypothesis.






40. Used to reduce bias - this measure weights the more relevant information higher than less relevant info.






41. Is data arising from counting that can take only non-negative integer values.






42. The standard deviation of a sampling distribution.






43. To find the median value of a set of numbers: Arrange the numbers in numerical order. Locate the two middle numbers in the list. Find the average of those two middle values.






44. Any specific experimental condition applied to the subjects






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






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






47. 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






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






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






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