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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. 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






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






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






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






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






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






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


9. 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.






10. The probability distribution of a sample statistic based on all the possible simple random samples of the same size from a population.






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






12. A measurement such that the random error is small






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






14. Samples are drawn from two different populations such that there is a matching of the first sample data drawn and a corresponding data value in the second sample data.






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






16. 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.






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






18. 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.






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






20. 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).






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






22. 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.






23. A numerical measure that describes an aspect of a population.






24. (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






25. In the long run - as the sample size increases - the relative frequencies of outcomes approach to the theoretical probability.






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






27. Have no meaningful rank order among values.






28. Have both a meaningful zero value and the distances between different measurements defined; they provide the greatest flexibility in statistical methods that can be used for analyzing the data






29. Some commonly used symbols for sample statistics






30. Also called correlation coefficient - is a numeric measure of the strength of linear relationship between two random variables (one can use it to quantify - for example - how shoe size and height are correlated in the population). An example is the P






31. 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






32. 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






33. A list of individuals from which the sample is actually selected.






34. Probability of rejecting a true null hypothesis.






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






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






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






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






39. 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.






40. 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






41. Probability of accepting a false null hypothesis.






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






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






44. Var[X] :






45. 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.






46. (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






47. 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.






48. Failing to reject a false null hypothesis.






49. 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'






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