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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.






2. Is the length of the smallest interval which contains all the data.






3. 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






4. The probability of correctly detecting a false null hypothesis.






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






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






7. 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






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






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






10. Descriptive statistics and inferential statistics (a.k.a. - predictive statistics) together comprise






11. 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






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


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






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






15. 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






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






17. 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






18. 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.


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






20. 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).






21. 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.






22. Are usually written with upper case calligraphic (e.g. F for the set of sets on which we define the probability P)






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






24. 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.






25. 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






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






27. 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






28. 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.






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






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






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






32. Gives the probability distribution for a continuous random variable.






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






34. Is a measure of its statistical dispersion - indicating how far from the expected value its values typically are. The variance of random variable X is typically designated as - - or simply s2.






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






36. Data are gathered and correlations between predictors and response are investigated.






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






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






39. 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.






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






41. Where the null hypothesis is falsely rejected giving a 'false positive'.






42. 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.






43. ?r






44. (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






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






46. Probability of rejecting a true null hypothesis.






47. 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.






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






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






50. 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.