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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. 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.






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






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






4. 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.






5. Rejecting a true null hypothesis.






6. 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






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






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






9. 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.






10. Probability of accepting a false null hypothesis.






11. 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.






12. 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.






13. Some commonly used symbols for sample statistics






14. 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






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






16. 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.






17. A subjective estimate of probability.






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






19. 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






20. Is a function that gives the probability of all elements in a given space: see List of probability distributions






21. 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






22. Failing to reject a false null hypothesis.






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






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






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






26. 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.






27. In number theory - scatter plots of data generated by a distribution function may be transformed with familiar tools used in statistics to reveal underlying patterns - which may then lead to






28. 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.






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






30. (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.






31. (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






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






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






34. Probability of rejecting a true null hypothesis.






35. 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.






36. 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






37. 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






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






39. Is the study of the collection - organization - analysis - and interpretation of data. It deals with all aspects of this - including the planning of data collection in terms of the design of surveys and experiments.






40. Also called correlation coefficient - is a numeric measure of the strength of linear relationship between two random variables (one can use it to quantify - for example - how shoe size and height are correlated in the population). An example is the P






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






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






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






44. 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.






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






46. Is the exact middle value of a set of numbers Arrange the numbers in numerical order. Find the value in the middle of the list.






47. Can refer either to a sample not being representative of the population - or to the difference between the expected value of an estimator and the true value.






48. 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






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






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