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






2. A numerical measure that assesses the strength of a linear relationship between two variables.






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






4. To find the median value of a set of numbers: Arrange the numbers in numerical order. Locate the two middle numbers in the list. Find the average of those two middle values.






5. 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.






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






7. (or just likelihood) is a conditional probability function considered a function of its second argument with its first argument held fixed. For example - imagine pulling a numbered ball with the number k from a bag of n balls - numbered 1 to n. Then






8. Is a function of the known data that is used to estimate an unknown parameter; an estimate is the result from the actual application of the function to a particular set of data. The mean can be used as an estimator.






9. A measurement such that the random error is small






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






11. Is data arising from counting that can take only non-negative integer values.






12. Are usually written in upper case roman letters: X - Y - etc.






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






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






15. 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.






16. 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.






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. Some commonly used symbols for sample statistics






19. Is data that can take only two values - usually represented by 0 and 1.






20. 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.






21. 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)






22. 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.






23. 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).






24. 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.






25. 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






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






27. 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.






28. 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






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






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






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






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






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






34. 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






35. Rejecting a true null hypothesis.






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






37. Any specific experimental condition applied to the subjects






38. A group of individuals sharing some common features that might affect the treatment.






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






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






41.






42. Is the function that gives the probability distribution of a random variable. It cannot be negative - and its integral on the probability space is equal to 1.






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






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






45. Have no meaningful rank order among values.






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






47. 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






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






49. 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






50. Is the probability distribution - under repeated sampling of the population - of a given statistic.