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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. Of a group of numbers is the center point of all those number values.






2. Some commonly used symbols for population parameters






3. 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






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






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






6. 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.






7. 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.






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






9. 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.






10. Var[X] :






11. E[X] :






12. Is a sample and the associated data points.






13. Is the probability of some event A - assuming event B. Conditional probability is written P(A|B) - and is read 'the probability of A - given B'






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






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






16. In particular - the pdf of the standard normal distribution is denoted by






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






18. Given two jointly distributed random variables X and Y - the marginal distribution of X is simply the probability distribution of X ignoring information about Y.






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






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






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






22. Probability of rejecting a true null hypothesis.






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






24. Cov[X - Y] :






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






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. 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).






28. Is the function that gives the probability distribution of a random variable. It cannot be negative - and its integral on the probability space is equal to 1.






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






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






31. Is a function that gives the probability of all elements in a given space: see List of probability distributions






32. Failing to reject a false null hypothesis.






33. 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.






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






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






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






37. ?






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






39. 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.






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






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






42. The probability of the observed value or something more extreme under the assumption that the null hypothesis is true.






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






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






45. 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






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






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






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






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






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