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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. 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.






2. Var[X] :






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






4. Any specific experimental condition applied to the subjects






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






6. Cov[X - Y] :






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






8. 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.






9. Is data that can take only two values - usually represented by 0 and 1.






10. A subjective estimate of probability.






11. Is the set of possible outcomes of an experiment. For example - the sample space for rolling a six-sided die will be {1 - 2 - 3 - 4 - 5 - 6}.






12. 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.






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






14. Rejecting a true null hypothesis.






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






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






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






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






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






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






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






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






23. The objects described by a set of data: person (animal) - place - and - thing. (SUBJECTS)






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






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






26. A scale that represents an ordinal scale such as looks on a scale from 1 to 10.






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






28. 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






29. 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.






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






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






32. (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






33. 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.






34. 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






35. 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






36. Is the study of the collection - organization - analysis - and interpretation of data. It deals with all aspects of this - including the planning of data collection in terms of the design of surveys and experiments.






37. Is a sample and the associated data points.






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






39. 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






40. A measurement such that the random error is small






41. Another name for elementary event.






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






43. 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.






44. 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.






45. 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).






46. 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






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






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






49. Some commonly used symbols for population parameters






50. 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.