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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. Any specific experimental condition applied to the subjects






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






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






4. 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






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






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






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






8. 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.






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






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






11. Cov[X - Y] :






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






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






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






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






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






17. Some commonly used symbols for sample statistics






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






19. ?r






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






21. Is one that explores the correlation between smoking and lung cancer. This type of study typically uses a survey to collect observations about the area of interest and then performs statistical analysis. In this case - the researchers would collect o






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






23. Probability of accepting a false null hypothesis.






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






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






26. 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.






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






28. Gives the probability distribution for a continuous random variable.






29. 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






30. 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.






31. 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.






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






33. (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






34. 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.






35. 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






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






37. Consists of a number of independent trials repeated under identical conditions. On each trial - there are two possible outcomes.






38. 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.






39. Rejecting a true null hypothesis.






40. 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






41. Is the most commonly used measure of statistical dispersion. It is the square root of the variance - and is generally written s (sigma).






42. 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






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






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






45. Is a sample and the associated data points.






46. 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.






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






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






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






50. 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.