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






2. When there is an even number of values...






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






4. 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






5. A measurement such that the random error is small






6. 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.

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7. Uses patterns in the sample data to draw inferences about the population represented - accounting for randomness. These inferences may take the form of: answering yes/no questions about the data (hypothesis testing) - estimating numerical characteris






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






9. 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)






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






11. Is the most commonly used measure of statistical dispersion. It is the square root of the variance - and is generally written s (sigma).






12. Is a sample and the associated data points.






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






14. 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.






15. 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.






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






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






18. 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.






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






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






21. The probability distribution of a sample statistic based on all the possible simple random samples of the same size from a population.






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






23. 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).






24. Any specific experimental condition applied to the subjects






25. 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.






26. (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.






27. ?






28. 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.






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






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






31. Is denoted by - pronounced 'x bar'.






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






33. (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






34. 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}.






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






36. 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.






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






38. 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






39. 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.






40. A data value that falls outside the overall pattern of the graph.






41. Some commonly used symbols for sample statistics






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






43. Rejecting a true null hypothesis.






44. A subjective estimate of probability.






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






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






47. Samples are drawn from two different populations such that there is a matching of the first sample data drawn and a corresponding data value in the second sample data.






48. 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.






49. 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).






50. 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.