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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.
  • Don't refresh. All questions and answers are randomly picked and ordered every time you load a test.

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. 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.






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






3. 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.






4. 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.






5. 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).






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






7. Two events are independent if the outcome of one does not affect that of the other (for example - getting a 1 on one die roll does not affect the probability of getting a 1 on a second roll). Similarly - when we assert that two random variables are i






8. 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.






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






10. A measurement such that the random error is small






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






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


13. A subjective estimate of probability.






14. Some commonly used symbols for population parameters






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






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






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






18. Failing to reject a false null hypothesis.






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






20. 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.






21. Var[X] :






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






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






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






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






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






27. E[X] :






28. (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.






29. 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.






30. 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






31. Cov[X - Y] :






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






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






34. (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






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






36. Is the probability of some event A - assuming event B. Conditional probability is written P(A|B) - and is read 'the probability of A - given B'






37. 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.






38. Is a function of the known data that is used to estimate an unknown parameter; an estimate is the result from the actual application of the function to a particular set of data. The mean can be used as an estimator.






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






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






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






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






43. 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)






44. 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






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






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






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






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






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