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CLEP General Mathematics: Probability And Statistics

Subjects : clep, math
Instructions:
  • Answer 50 questions in 15 minutes.
  • If you are not ready to take this test, you can study here.
  • 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. 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).






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

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3. A subjective estimate of probability.






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






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






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






7. 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






8. When you have two or more competing models - choose the simpler of the two models.






9. To prove the guiding theory further - these predictions are tested as well - as part of the scientific method. If the inference holds true - then the descriptive statistics of the new data increase the soundness of that






10. 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






11. Cov[X - Y] :






12. Have no meaningful rank order among values.






13. 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






14. A data value that falls outside the overall pattern of the graph.






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






16. 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






17. 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).






18. Is used in 'mathematical statistics' (alternatively - 'statistical theory') to study the sampling distributions of sample statistics and - more generally - the properties of statistical procedures. The use of any statistical method is valid when the






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






20. Given two random variables X and Y - the joint distribution of X and Y is the probability distribution of X and Y together.






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






22. 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






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






24. Is the exact middle value of a set of numbers Arrange the numbers in numerical order. Find the value in the middle of the list.






25. Probability of rejecting a true null hypothesis.






26. The proportion of the explained variation by a linear regression model in the total variation.






27. Another name for elementary event.






28. 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






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






30. (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.






31.






32. 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.






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






34. (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






35. There are two major types of causal statistical studies: In both types of studies - the effect of differences of an independent variable (or variables) on the behavior of the dependent variable are observed. The difference between the two types lies






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






37. 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






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. 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.






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






41. Involves taking measurements of the system under study - manipulating the system - and then taking additional measurements using the same procedure to determine if the manipulation has modified the values of the measurements.






42. S^2






43. Is a set of entities about which statistical inferences are to be drawn - often based on random sampling. One can also talk about a population of measurements or values.






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






45. 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






46. A common goal for a statistical research project is to investigate causality - and in particular to draw a conclusion on the effect of changes in the values of predictors or independent variables on dependent variables or response.






47. The probability of the observed value or something more extreme under the assumption that the null hypothesis is true.






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






49. 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.






50. 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.