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CLEP General Mathematics: Probability And Statistics

Subjects : clep, math
Instructions:
  • Answer 50 questions in 15 minutes.
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  • 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. 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






2. 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).






3. 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.






4. (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.






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






6. 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






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






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






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






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






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






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






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






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






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






16. 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.






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






18. 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.






19. Another name for elementary event.






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






21. Rejecting a true null hypothesis.






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. 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.






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






25. Is that part of a population which is actually observed.






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






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

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28. A data value that falls outside the overall pattern of the graph.






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






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






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






32. Is one that explores the correlation between smoking and lung cancer. This type of study typically uses a survey to collect observations about the area of interest and then performs statistical analysis. In this case - the researchers would collect o






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






34. Any specific experimental condition applied to the subjects






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






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






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






38. Descriptive statistics and inferential statistics (a.k.a. - predictive statistics) together comprise






39. Have no meaningful rank order among values.






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






41. 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.






42. 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.






43. ?






44. When you have two or more competing models - choose the simpler of the two models.






45. 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






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






47. 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.






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






49. 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






50. 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.