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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.
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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 measure of its statistical dispersion - indicating how far from the expected value its values typically are. The variance of random variable X is typically designated as - - or simply s2.






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






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






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






5. 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






6. 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.






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






8. Involves taking measurements of the system under study - manipulating the system - and then taking additional measurements using the same procedure to determine if the manipulation has modified the values of the measurements.






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






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. Describes a characteristic of an individual to be measured or observed.






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






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






14. ?






15. Are two related but separate academic disciplines. Statistical analysis often uses probability distributions - and the two topics are often studied together. However - probability theory contains much that is of mostly of mathematical interest and no






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






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






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






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






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






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






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






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






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






25. 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.






26.






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






28. 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.






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






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






31. (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






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






33. Have meaningful distances between measurements defined - but the zero value is arbitrary (as in the case with longitude and temperature measurements in Celsius or Fahrenheit)






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






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






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

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






38. A collection of events is mutually independent if for any subset of the collection - the joint probability of all events occurring is equal to the product of the joint probabilities of the individual events. Think of the result of a series of coin-fl






39. Cov[X - Y] :






40. Any specific experimental condition applied to the subjects






41. S^2






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






43. 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.






44. 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






45. Probability of rejecting a true null hypothesis.






46. Another name for elementary event.






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






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. E[X] :






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







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