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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 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






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






3. 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).






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






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






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






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






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






9. 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.






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






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






12. 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.






13. 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.






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






15. Is the length of the smallest interval which contains all the data.






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






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






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






19. 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






20. Any specific experimental condition applied to the subjects






21. 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.






22. 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.






23. A group of individuals sharing some common features that might affect the treatment.






24. 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.






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






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






27. 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.






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






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






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






31. Probability of accepting a false null hypothesis.






32. 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'






33. 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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34. Working from a null hypothesis two basic forms of error are recognized:






35. Another name for elementary event.






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






37. Can refer either to a sample not being representative of the population - or to the difference between the expected value of an estimator and the true value.






38. Rejecting a true null hypothesis.






39. 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.






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






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






42. Failing to reject a false null hypothesis.






43. 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






44. Is used to describe probability in a continuous probability distribution. For example - you can't say that the probability of a man being six feet tall is 20% - but you can say he has 20% of chances of being between five and six feet tall. Probabilit






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






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






47. Var[X] :






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






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






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