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






2. 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.






3. 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.






4. 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.






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






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






7. 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






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






10. 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.






11. 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.






12. 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






13. 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.






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






15. 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






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






17. 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






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






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






20. Failing to reject a false null hypothesis.






21. A sample selected in such a way that each individual is equally likely to be selected as well as any group of size n is equally likely to be selected.






22. 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.






23. 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)






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






25. 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).






26. Because variables conforming only to nominal or ordinal measurements cannot be reasonably measured numerically - sometimes they are grouped together as






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






28. Another name for elementary event.






29. 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.






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






31. ?






32. Rejecting a true null hypothesis.






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






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






35. In particular - the pdf of the standard normal distribution is denoted by






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






37. Var[X] :






38. Patterns in the data may be modeled in a way that accounts for randomness and uncertainty in the observations - and are then used for drawing inferences about the process or population being studied; this is called






39. Any specific experimental condition applied to the subjects






40. Two events are independent if the outcome of one does not affect that of the other (for example - getting a 1 on one die roll does not affect the probability of getting a 1 on a second roll). Similarly - when we assert that two random variables are i






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






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






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






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






45. 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'






46. 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.






47. 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






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






49. Some commonly used symbols for sample statistics






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