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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 process of selecting observations to obtain knowledge about a population. There are many methods to choose on which sample to do the observations.






2. Uses patterns in the sample data to draw inferences about the population represented - accounting for randomness. These inferences may take the form of: answering yes/no questions about the data (hypothesis testing) - estimating numerical characteris






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






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






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






6. 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.


7. The standard deviation of a sampling distribution.






8. Var[X] :






9. Summarize the population data by describing what was observed in the sample numerically or graphically. Numerical descriptors include mean and standard deviation for continuous data types (like heights or weights) - while frequency and percentage are






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






11. 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






12. 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






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






14. 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.






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






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






17. A subjective estimate of probability.






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






19. Is the exact middle value of a set of numbers Arrange the numbers in numerical order. Find the value in the middle of the list.






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






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






22. Probability of accepting a false null hypothesis.






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






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






25. Some commonly used symbols for population parameters






26. (or atomic event) is an event with only one element. For example - when pulling a card out of a deck - 'getting the jack of spades' is an elementary event - while 'getting a king or an ace' is not.






27. 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.






28. 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






29. 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






30. Is often denoted by placing a caret over the corresponding symbol - e.g. - pronounced 'theta hat'.






31. 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






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






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






34. 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






35. Have no meaningful rank order among values.






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






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






38. 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






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






40. 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.






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






42. (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






43. 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.






44. 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.






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






46. 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.






47. Some commonly used symbols for sample statistics






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






49. 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






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