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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. 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






2. Var[X] :






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






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






5. Have meaningful distances between measurements defined - but the zero value is arbitrary (as in the case with longitude and temperature measurements in Celsius or Fahrenheit)






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






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






8. Some commonly used symbols for sample statistics






9. Are usually written in upper case roman letters: X - Y - etc.






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






11. 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.






12. 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.






13. (or multivariate random variable) is a vector whose components are random variables on the same probability space.






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


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






16. 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






17. 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.






18. 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.






19. 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






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






21. 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.






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






23. Involves taking measurements of the system under study - manipulating the system - and then taking additional measurements using the same procedure to determine if the manipulation has modified the values of the measurements.






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






25.






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






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






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






29. 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.






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






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






32. S^2






33. 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.






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






35. 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.






36. The standard deviation of a sampling distribution.






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






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






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






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






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






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






43. 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






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






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






46. Rejecting a true null hypothesis.






47. A group of individuals sharing some common features that might affect the treatment.






48. Is data arising from counting that can take only non-negative integer values.






49. 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.






50. 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