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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. S^2






2. A subjective estimate of probability.






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






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






5. 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}.






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. 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






8. 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.






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






10. 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






11. 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.






12. Cov[X - Y] :






13. Is a measure of its statistical dispersion - indicating how far from the expected value its values typically are. The variance of random variable X is typically designated as - - or simply s2.






14. 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






15. Of a group of numbers is the center point of all those number values.






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






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






19. (pdfs) and probability mass functions are denoted by lower case letters - e.g. f(x).






20. A numerical measure that assesses the strength of a linear relationship between two variables.






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






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






23. 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.






24. (cdfs) are denoted by upper case letters - e.g. F(x).






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






26. 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






27. 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






28. Have meaningful distances between measurements defined - but the zero value is arbitrary (as in the case with longitude and temperature measurements in Celsius or Fahrenheit)






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






30. 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.






31. 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.






32. 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






33. 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.






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






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






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






37. 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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38. A numerical measure that describes an aspect of a population.






39. 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).






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






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






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

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43. (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






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






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






46. 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






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






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






49. Is a sample and the associated data points.






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