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. The standard deviation of a sampling distribution.






2. Failing to reject a false null hypothesis.






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






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






5. E[X] :






6. A list of individuals from which the sample is actually selected.






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






8. 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).






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






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






11. A pairwise independent collection of random variables is a set of random variables any two of which are independent.






12. A measure that is relevant or appropriate as a representation of that property.






13. 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.






14. 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.






15. A subjective estimate of probability.






16. 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






17. 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






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






19. 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.






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






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






22. 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






23. 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






24. 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






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






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






27. 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.






28. 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.






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






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






31. 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






32. 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.






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






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






35. 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






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






37. (or multivariate random variable) is a vector whose components are random variables on the same probability space.






38. 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






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






40. 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






41. 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






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






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






44. 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.






45. Have both a meaningful zero value and the distances between different measurements defined; they provide the greatest flexibility in statistical methods that can be used for analyzing the data






46. S^2






47. Is one that explores the correlation between smoking and lung cancer. This type of study typically uses a survey to collect observations about the area of interest and then performs statistical analysis. In this case - the researchers would collect o






48. The objects described by a set of data: person (animal) - place - and - thing. (SUBJECTS)






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






50. A measurement such that the random error is small