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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. (cdfs) are denoted by upper case letters - e.g. F(x).






2. Some commonly used symbols for population parameters






3. 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).






4. 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






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






6. 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.






7. (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






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






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






10. Probability of rejecting a true null hypothesis.






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






12. In particular - the pdf of the standard normal distribution is denoted by






13. 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






14. 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






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






16. Another name for elementary event.






17. 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






18. Is a sample and the associated data points.






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






20. 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.






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






22. Given two jointly distributed random variables X and Y - the conditional probability distribution of Y given X (written 'Y | X') is the probability distribution of Y when X is known to be a particular value.






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






25. Cov[X - Y] :






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






27. ?r






28. 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}.






29. A pairwise independent collection of random variables is a set of random variables any two of which are independent.






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






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






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






33. Is the result of applying a statistical algorithm to a data set. It can also be described as an observable random variable.






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






35. 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.






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






37. 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






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






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






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






41. The standard deviation of a sampling distribution.






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






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






44. 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.






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






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






47. 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.






48. Are two related but separate academic disciplines. Statistical analysis often uses probability distributions - and the two topics are often studied together. However - probability theory contains much that is of mostly of mathematical interest and no






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






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