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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. 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






3. To prove the guiding theory further - these predictions are tested as well - as part of the scientific method. If the inference holds true - then the descriptive statistics of the new data increase the soundness of that






4. 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'






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






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






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






8. 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






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






10. 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.






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






12. 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.


13. 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






14. 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.






15. 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.






16. 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






17. Is the set of possible outcomes of an experiment. For example - the sample space for rolling a six-sided die will be {1 - 2 - 3 - 4 - 5 - 6}.






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






19. E[X] :






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






21. 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.






22. (or expectation) of a random variable is the sum of the probability of each possible outcome of the experiment multiplied by its payoff ('value'). Thus - it represents the average amount one 'expects' to win per bet if bets with identical odds are re






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






24. 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.






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






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






27. Data are gathered and correlations between predictors and response are investigated.






28. (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






29. Probability of accepting a false null hypothesis.






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






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






32. 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.






33. 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.






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






35. Descriptive statistics and inferential statistics (a.k.a. - predictive statistics) together comprise






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






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






38. 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.






39. A numerical measure that describes an aspect of a population.






40. 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






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






42. 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






43. 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






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






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






46. 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.






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






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






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