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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. Design of experiments - using blocking to reduce the influence of confounding variables - and randomized assignment of treatments to subjects to allow unbiased estimates of treatment effects and experimental error. At this stage - the experimenters a






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






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






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. (or atomic event) is an event with only one element. For example - when pulling a card out of a deck - 'getting the jack of spades' is an elementary event - while 'getting a king or an ace' is not.






6. A measurement such that the random error is small






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






8. 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






9. 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






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






11. 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).






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






13.






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






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






16. 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)






17. Var[X] :






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






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






20. 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'






21. (or multivariate random variable) is a vector whose components are random variables on the same probability space.






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






23. 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






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






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






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






27. Any specific experimental condition applied to the subjects






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






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






30. (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






31. 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.






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






33. 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






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






35. 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.






36. Is the length of the smallest interval which contains all the data.






37. 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






38. Rejecting a true null hypothesis.






39. 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.


40. Is a process of selecting observations to obtain knowledge about a population. There are many methods to choose on which sample to do the observations.






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






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






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






44. 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.






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






46. 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.






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






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






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






50. The result of a Bayesian analysis that encapsulates the combination of prior beliefs or information with observed data