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






2. 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.






3. The standard deviation of a sampling distribution.






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






5. Occurs when a subject receives no treatment - but (incorrectly) believes he or she is in fact receiving treatment and responds favorably.






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






7. Cov[X - Y] :






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






9. 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






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






11. There are four main levels of measurement used in statistics: Each of these have different degrees of usefulness in statistical research.






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






13. 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






14. 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






15. (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






16.






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






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






19. 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.






20. 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.






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






22. Probability of rejecting a true null hypothesis.






23. Is a measure of the asymmetry of the probability distribution of a real-valued random variable. Roughly speaking - a distribution has positive skew (right-skewed) if the higher tail is longer and negative skew (left-skewed) if the lower tail is longe






24. 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.






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






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






27. 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






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






29. 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.






30. Statistics involve methods of organizing - picturing - and summarizing information from samples or population.






31. 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






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






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






34. (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






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






36. Can be a population parameter - a distribution parameter - an unobserved parameter (with different shades of meaning). In statistics - this is often a quantity to be estimated.

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

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38. 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.






39. 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).






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






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






42. Another name for elementary event.






43. 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.






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






45. 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






46. Rejecting a true null hypothesis.






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






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






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