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






2. Is a parameter that indexes a family of probability distributions.






3.






4. 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.






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






6. (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






7. Rejecting a true null hypothesis.






8. 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






9. 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






10. The standard deviation of a sampling distribution.






11. 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






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






13. 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).






14. Is the length of the smallest interval which contains all the data.






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






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






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






18. Is a function that gives the probability of all elements in a given space: see List of probability distributions






19. Are usually written with upper case calligraphic (e.g. F for the set of sets on which we define the probability P)






20. Changes over time that show a regular periodicity in the data where regular means over a fixed interval; the time between repetitions is called the period.






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






22. A measurement such that the random error is small






23. Samples are drawn from two different populations such that the sample data drawn from one population is completely unrelated to the selection of sample data from the other population.






24. 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.






25. 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






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






27. 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






28. 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.






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






31. Cov[X - Y] :






32. Data are gathered and correlations between predictors and response are investigated.






33. Another name for elementary event.






34. Have no meaningful rank order among values.






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






36. 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.






37. 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






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






39. 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






40. 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






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






42. There are four main levels of measurement used in statistics: Each of these have different degrees of usefulness in statistical research.






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






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






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






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






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






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






49. Probability of rejecting a true null hypothesis.






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