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CLEP General Mathematics: Probability And Statistics

Subjects : clep, math
Instructions:
  • Answer 50 questions in 15 minutes.
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  • Match each statement with the correct term.
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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 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






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






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






4. Cov[X - Y] :






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






6. 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






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






8. 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






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






10. Var[X] :






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






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






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






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






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






16. (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






17. 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.






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






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






20. Is data arising from counting that can take only non-negative integer values.






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






22. Are usually written with upper case calligraphic (e.g. F for the set of sets on which we define the probability P)






23. 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






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






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






27. 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






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






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






30. A subjective estimate of probability.






31. 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).






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






33. 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






34. Is a set of entities about which statistical inferences are to be drawn - often based on random sampling. One can also talk about a population of measurements or values.






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






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






37. Consists of a number of independent trials repeated under identical conditions. On each trial - there are two possible outcomes.






38. 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}.






39. 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.






40. 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






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






42. 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.






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






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






45. 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.






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






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






48. Rejecting a true null hypothesis.






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






50. Have imprecise differences between consecutive values - but have a meaningful order to those values







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