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






3. 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.






4. Two events are independent if the outcome of one does not affect that of the other (for example - getting a 1 on one die roll does not affect the probability of getting a 1 on a second roll). Similarly - when we assert that two random variables are i






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






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






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






8. The result of a Bayesian analysis that encapsulates the combination of prior beliefs or information with observed data






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






10. In number theory - scatter plots of data generated by a distribution function may be transformed with familiar tools used in statistics to reveal underlying patterns - which may then lead to






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






12. Is denoted by - pronounced 'x bar'.






13. The standard deviation of a sampling distribution.






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

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






16.






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






18. To find the median value of a set of numbers: Arrange the numbers in numerical order. Locate the two middle numbers in the list. Find the average of those two middle values.






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






20. 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.






21. 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






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






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






24. 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






25. Where the null hypothesis is falsely rejected giving a 'false positive'.






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






27. Ratio and interval measurements which can be either discrete or continuous - due to their numerical nature are grouped together as






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






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






30. 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






31. To prove the guiding theory further - these predictions are tested as well - as part of the scientific method. If the inference holds true - then the descriptive statistics of the new data increase the soundness of that






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






33. E[X] :






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






35. ?r






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






37. Var[X] :






38. 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.






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






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






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






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






43. Have no meaningful rank order among values.






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






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






46. Many statistical methods seek to minimize the mean-squared error - and these are called






47. 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






48. 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}.






49. Probability of rejecting a true null hypothesis.






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