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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.






2. A subjective estimate of probability.






3. ?r






4. E[X] :






5. 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






6. In the long run - as the sample size increases - the relative frequencies of outcomes approach to the theoretical probability.






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






8. 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.






9. 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.






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






11. 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.






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






13. Have no meaningful rank order among values.






14. The probability distribution of a sample statistic based on all the possible simple random samples of the same size from a population.






15. Are simply two different terms for the same thing. Add the given values






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






17. A common goal for a statistical research project is to investigate causality - and in particular to draw a conclusion on the effect of changes in the values of predictors or independent variables on dependent variables or response.






18. 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.






19. (cdfs) are denoted by upper case letters - e.g. F(x).






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






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






22. Probability of accepting a false null hypothesis.






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






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






25. Some commonly used symbols for population parameters






26. 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






27. 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






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






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






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






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






32. Cov[X - Y] :






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






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






36. A measurement such that the random error is small






37. A numerical measure that describes an aspect of a population.






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






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






40. Any specific experimental condition applied to the subjects






41. 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






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






43. Var[X] :






44. 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






45. 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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46. Failing to reject a false null hypothesis.






47. (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.






48. 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






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






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