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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. 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.






2. Given two jointly distributed random variables X and Y - the conditional probability distribution of Y given X (written 'Y | X') is the probability distribution of Y when X is known to be a particular value.






3. 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.






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






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






6. 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.






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






8. Is inference about a population from a random sample drawn from it or - more generally - about a random process from its observed behavior during a finite period of time.






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






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






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






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






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






14. 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






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






16. 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






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






18. Var[X] :






19. 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.






20. Some commonly used symbols for population parameters






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. Rejecting a true null hypothesis.






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






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

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






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






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






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






29. 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.






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






31. 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






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






33. Have no meaningful rank order among values.






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






35. 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.






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






37. The standard deviation of a sampling distribution.






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






39. ?r






40. Is a sample and the associated data points.






41. Any specific experimental condition applied to the subjects






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






43. E[X] :






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






45. Occurs when a subject receives no treatment - but (incorrectly) believes he or she is in fact receiving treatment and responds favorably.






46. 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






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






48. 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).






49. A subjective estimate of probability.






50. 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.