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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. 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.






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






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






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






5. 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






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






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






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






9. A numerical measure that describes an aspect of a population.






10. Rejecting a true null hypothesis.






11. There are two major types of causal statistical studies: In both types of studies - the effect of differences of an independent variable (or variables) on the behavior of the dependent variable are observed. The difference between the two types lies






12. 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.






13. E[X] :






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






15. Probability of accepting a false null hypothesis.






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






17. 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.






18. S^2






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






20. Any specific experimental condition applied to the subjects






21. 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.






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






23. 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






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






25. Samples are drawn from two different populations such that the sample data drawn from one population is completely unrelated to the selection of sample data from the other population.






26. A subjective estimate of probability.






27. 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






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






29. (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






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






31. 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.






32. Gives the probability of events in a probability space.






33. 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






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






35. Some commonly used symbols for population parameters






36. ?r






37. The objects described by a set of data: person (animal) - place - and - thing. (SUBJECTS)






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






39. 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.






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






41. 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.






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






43. The standard deviation of a sampling distribution.






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






45. 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).






46. 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.






47. Is the set of possible outcomes of an experiment. For example - the sample space for rolling a six-sided die will be {1 - 2 - 3 - 4 - 5 - 6}.






48. Another name for elementary event.






49. 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.






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