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






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






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






4. ?r






5. Rejecting a true null hypothesis.






6. 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






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






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






9. ?






10. (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






11. 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






12. Failing to reject a false null hypothesis.






13. 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.






14. 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






15. The standard deviation of a sampling distribution.






16. 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).






17. When info. in a contingency table is re-organized into more or less categories - relationships seen can change or reverse.


18. 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.






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






20. Many statistical methods seek to minimize the mean-squared error - and these are called






21. Some commonly used symbols for population parameters






22. Probability of rejecting a true null hypothesis.






23. 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






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






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






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






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






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






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






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






31. 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






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






33. 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.






34. Performing the experiment following the experimental protocol and analyzing the data following the experimental protocol. 4. Further examining the data set in secondary analyses - to suggest new hypotheses for future study. 5. Documenting and present






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






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






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






38. 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.






39. 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






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






41.






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






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






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






45. (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






46. 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.






47. 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.






48. Any specific experimental condition applied to the subjects






49. Is defined as the expected value of random variable (X -






50. 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)