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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. 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






2. 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.






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






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






5. 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.






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






7. 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






8. 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).






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






10. (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.






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






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






13. Given two random variables X and Y - the joint distribution of X and Y is the probability distribution of X and Y together.






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






15. Two events are independent if the outcome of one does not affect that of the other (for example - getting a 1 on one die roll does not affect the probability of getting a 1 on a second roll). Similarly - when we assert that two random variables are i






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






17. 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.






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






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






20. 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






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






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






23. 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).






24. 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.






25. 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






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






27. Any specific experimental condition applied to the subjects






28. Is one that explores the correlation between smoking and lung cancer. This type of study typically uses a survey to collect observations about the area of interest and then performs statistical analysis. In this case - the researchers would collect o






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

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






31. Rejecting a true null hypothesis.






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






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






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 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.






36. 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}.






37. A scale that represents an ordinal scale such as looks on a scale from 1 to 10.






38. 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






39. Ratio and interval measurements which can be either discrete or continuous - due to their numerical nature are grouped together as






40. 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)






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






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






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






44. Is often denoted by placing a caret over the corresponding symbol - e.g. - pronounced 'theta hat'.






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






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






47. Probability of accepting a false null hypothesis.






48. 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.






49. Statistics involve methods of using information from a sample to draw conclusions regarding the population.






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







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