Test your basic knowledge |

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. 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






2. 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






3. 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






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






5. 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.






6. The result of a Bayesian analysis that encapsulates the combination of prior beliefs or information with observed data






7. 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






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






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






10. 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.






11. 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






12. Var[X] :






13. Some commonly used symbols for population parameters






14. Failing to reject a false null hypothesis.






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






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






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






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






19. Probability of rejecting a true null hypothesis.






20. 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.






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






22. Have no meaningful rank order among values.






23. The standard deviation of a sampling distribution.






24. Is the set of possible outcomes of an experiment. For example - the sample space for rolling a six-sided die will be {1 - 2 - 3 - 4 - 5 - 6}.






25. 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.






26. (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






27. ?r






28. S^2






29. Used to reduce bias - this measure weights the more relevant information higher than less relevant info.






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






31. 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






32. 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.






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






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






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






36. A subjective estimate of probability.






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






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






39.






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






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






42. 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.






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






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






45. Cov[X - Y] :






46. Is a measure of the asymmetry of the probability distribution of a real-valued random variable. Roughly speaking - a distribution has positive skew (right-skewed) if the higher tail is longer and negative skew (left-skewed) if the lower tail is longe






47. 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






48. 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.






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






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







Sorry!:) No result found.

Can you answer 50 questions in 15 minutes?


Let me suggest you:

Browse all subjects

Browse all tests

Most popular tests



Major Subjects

Tests & Exams


AP
CLEP
DSST
GRE
SAT
GMAT

Browse all subjects

Browse all tests

Most popular tests