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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. 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






2. 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.






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






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






5. Is denoted by - pronounced 'x bar'.






6. Probability of accepting a false null hypothesis.






7. 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.






8. S^2






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






10. 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.






11. The probability distribution of a sample statistic based on all the possible simple random samples of the same size from a population.






12. 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






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 a sample and the associated data points.






15. 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.






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






17. 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.






18. Describes a characteristic of an individual to be measured or observed.






19. Working from a null hypothesis two basic forms of error are recognized:






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






21. 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






22. Another name for elementary event.






23. Is a parameter that indexes a family of probability distributions.






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






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






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






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






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

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






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






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






32. (or just likelihood) is a conditional probability function considered a function of its second argument with its first argument held fixed. For example - imagine pulling a numbered ball with the number k from a bag of n balls - numbered 1 to n. Then






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






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






35. Is a typed measurement - it can be a boolean value - a real number - a vector (in which case it's also called a data vector) - etc.






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. A pairwise independent collection of random variables is a set of random variables any two of which are independent.






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






39. Statistical methods can be used for summarizing or describing a collection of data; this is called






40. 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).






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






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






43. E[X] :






44. 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.






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






46. 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






47. A numerical measure that describes an aspect of a sample.






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






49. Are usually written with upper case calligraphic (e.g. F for the set of sets on which we define the probability P)






50. The standard deviation of a sampling distribution.







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