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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. 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.






2. Another name for elementary event.






3. 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.






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






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






6. 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.






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






8. 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.






9. 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.






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






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






12. 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






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






14. Any specific experimental condition applied to the subjects






15. 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.






16. (pdfs) and probability mass functions are denoted by lower case letters - e.g. f(x).






17. 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






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






19. 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.






20. A common goal for a statistical research project is to investigate causality - and in particular to draw a conclusion on the effect of changes in the values of predictors or independent variables on dependent variables or response.






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






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






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


24. 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.






25. ?r






26. 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.


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






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






29. 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).






30. Var[X] :






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






32. Rejecting a true null hypothesis.






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






34. E[X] :






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






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






37. 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






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






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






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






41. Cov[X - Y] :






42. 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






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






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






45. 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.






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






47. Probability of accepting a false null hypothesis.






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






49. 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)






50.