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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 subjective estimate of probability.






2. Given two jointly distributed random variables X and Y - the marginal distribution of X is simply the probability distribution of X ignoring information about Y.






3. Failing to reject a false null hypothesis.






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






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






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






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






8. To find the average - or arithmetic mean - of a set of numbers:






9. 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.






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






11. 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.






12. 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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13. Some commonly used symbols for population parameters






14. In particular - the pdf of the standard normal distribution is denoted by






15. 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.






16. 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






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






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






19. Cov[X - Y] :






20. 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






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






22. 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.






23. 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






24. 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.






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






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






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






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






29. 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.






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






31. 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.






32. Have no meaningful rank order among values.






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






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






35. 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






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






37. The probability of correctly detecting a false null hypothesis.






38. Ratio and interval measurements which can be either discrete or continuous - due to their numerical nature are grouped together as






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






40. Are usually written in upper case roman letters: X - Y - etc.






41. E[X] :






42. 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






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






44. ?






45. 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)






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






47. Where the null hypothesis fails to be rejected and an actual difference between populations is missed giving a 'false negative'.






48. The errors - or difference between the estimated response y^i and the actual measured response yi - collectively






49. 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}.






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







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