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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 the most commonly used measure of statistical dispersion. It is the square root of the variance - and is generally written s (sigma).






2. 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






3. A list of individuals from which the sample is actually selected.






4. Failing to reject a false null hypothesis.






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






6. 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






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






8. 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.






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






10. 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.






11. 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






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






13. 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.






14. Is a sample space over which a probability measure has been defined.






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






16. ?






17. Any specific experimental condition applied to the subjects






18. 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.






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






20. The collection of all possible outcomes in an experiment.






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






22. E[X] :






23. Probability of accepting a false null hypothesis.






24. (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






25. 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)






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






27. 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.






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






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






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






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






32. Is used in 'mathematical statistics' (alternatively - 'statistical theory') to study the sampling distributions of sample statistics and - more generally - the properties of statistical procedures. The use of any statistical method is valid when the






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






34. Gives the probability distribution for a continuous random variable.






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






36. 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'






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. 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.






39. Cov[X - Y] :






40. Probability of rejecting a true null hypothesis.






41. 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.






42. Another name for elementary event.






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






44. A subjective estimate of probability.






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






46. 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






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






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






49. 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.






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