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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. Is a sample and the associated data points.






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






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






5. Is the probability of some event A - assuming event B. Conditional probability is written P(A|B) - and is read 'the probability of A - given B'






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






7. 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






8. 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.






9. Have both a meaningful zero value and the distances between different measurements defined; they provide the greatest flexibility in statistical methods that can be used for analyzing the data






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






11. Failing to reject a false null hypothesis.






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






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






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






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






16. Have no meaningful rank order among values.






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






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. 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






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






21. Probability of accepting a false null hypothesis.






22. Planning the research - including finding the number of replicates of the study - using the following information: preliminary estimates regarding the size of treatment effects - alternative hypotheses - and the estimated experimental variability. Co






23. Gives the probability of events in a probability space.






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






25. The standard deviation of a sampling distribution.






26. ?






27. 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).






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






29. Cov[X - Y] :






30. 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.






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






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






33. 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).






34. 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.






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






36. A subjective estimate of probability.






37. 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






38. 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






39. 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.






40. 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






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






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






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






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






46. Any specific experimental condition applied to the subjects






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






48.






49. 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






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







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