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






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






3. 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






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






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






6. 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.






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






8. 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






9. Var[X] :






10. 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






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






12. Some commonly used symbols for sample statistics






13. ?






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






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






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






17. ?r






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






19. 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.






20. Another name for elementary event.






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






22. 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).






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






24. Error also refers to the extent to which individual observations in a sample differ from a central value - such as






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






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






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






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

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29. 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.






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






31. To find the average - or arithmetic mean - of a set of numbers:






32. A subjective estimate of probability.






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






34. Cov[X - Y] :






35. Any specific experimental condition applied to the subjects






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






37. 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.






38. Is a measure of the 'peakedness' of the probability distribution of a real-valued random variable. Higher kurtosis means more of the variance is due to infrequent extreme deviations - as opposed to frequent modestly sized deviations.






39. (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






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






41. 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






42. 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.






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






44. 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






45. 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






46. Are simply two different terms for the same thing. Add the given values






47. 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.






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






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. 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