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






2. 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.






3. Planning the research - including finding the number of replicates of the study - using the following information: preliminary estimates regarding the size of treatment effects - alternative hypotheses - and the estimated experimental variability. Co






4. 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






5. 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.






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






7. 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






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






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






10. Cov[X - Y] :






11. Rejecting a true null hypothesis.






12. Failing to reject a false null hypothesis.






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






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






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






16. 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






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






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






20. 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).






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






22. 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.






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






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






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






27. 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






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






29. 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.






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






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






32. 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






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






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






35. 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).






36. ?






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






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






39. 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.






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. E[X] :






42. Statistics involve methods of organizing - picturing - and summarizing information from samples or population.






43. 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.






44. Is one that explores the correlation between smoking and lung cancer. This type of study typically uses a survey to collect observations about the area of interest and then performs statistical analysis. In this case - the researchers would collect o






45. 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






46. 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.






47. 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.






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






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






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