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






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






4. Probability of rejecting a true null hypothesis.






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






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






7. Two variables such that their effects on the response variable cannot be distinguished from each other.






8. 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.






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






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






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






12. Some commonly used symbols for sample statistics






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






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






15. Another name for elementary event.






16. (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.






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






18. Var[X] :






19. Failing to reject a false null hypothesis.






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






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






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






23. Is a sample and the associated data points.






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






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






26. 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.






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






28. A measurement such that the random error is small






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






30. 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






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






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






33. A subjective estimate of probability.






34. 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






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






36. (also called statistical variability) is a measure of how diverse some data is. It can be expressed by the variance or the standard deviation.






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






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






39. 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






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






41. 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)






42. 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.






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






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






45. Changes over time that show a regular periodicity in the data where regular means over a fixed interval; the time between repetitions is called the period.






46. 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






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






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






49. 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.






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