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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 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






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






3. 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).






4. The standard deviation of a sampling distribution.






5. Describes a characteristic of an individual to be measured or observed.






6. (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.






7. 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






8. 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.






9. Some commonly used symbols for population parameters






10. 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.






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






12. A numerical measure that describes an aspect of a sample.






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






14. 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.






15. 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.






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






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






18. Rejecting a true null hypothesis.






19. Cov[X - Y] :






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






21. Probability of rejecting a true null hypothesis.






22. 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.






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






24. 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.






25. Some commonly used symbols for sample statistics






26. Gives the probability of events in a probability space.






27. Statistics involve methods of using information from a sample to draw conclusions regarding the population.






28. 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






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






30. 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.






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






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






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






34. 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.






35. Any specific experimental condition applied to the subjects






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






37. (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






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






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






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






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






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






43. Statistical methods can be used for summarizing or describing a collection of data; this is called






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. 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.






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






47. 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.






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






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






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