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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. Var[X] :






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






3. 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.






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






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






6. ?






7. 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






8. (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






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






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






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






12. 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)






13. Is a sample and the associated data points.






14. 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






15. Some commonly used symbols for population parameters






16. 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.






17. 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.






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






19. 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






20. A measurement such that the random error is small






21. 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






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






23. 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.






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






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






26. The standard deviation of a sampling distribution.






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






28. 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.






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






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






31. ?r






32. 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.






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






34. 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






35. 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.






36. 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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37. Involves taking measurements of the system under study - manipulating the system - and then taking additional measurements using the same procedure to determine if the manipulation has modified the values of the measurements.






38. Cov[X - Y] :






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






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






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






42. 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






43. Patterns in the data may be modeled in a way that accounts for randomness and uncertainty in the observations - and are then used for drawing inferences about the process or population being studied; this is called






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






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






46. A variable describes an individual by placing the individual into a category or a group.






47. S^2






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. 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.






50. The collection of all possible outcomes in an experiment.