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CLEP General Mathematics: Probability And Statistics

Subjects : clep, math
Instructions:
  • Answer 50 questions in 15 minutes.
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  • Match each statement with the correct term.
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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 a function that gives the probability of all elements in a given space: see List of probability distributions






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






3. 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






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






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






6. (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.






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






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






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






10. 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.






11. 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.






12. Cov[X - Y] :






13. 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.






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






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






16. 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






17. 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






18. 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.






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






20. 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






21. S^2






22. 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






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






24. Have no meaningful rank order among values.






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






26. Is its expected value. The mean (or sample mean of a data set is just the average value.






27. 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.






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






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






30. A subjective estimate of probability.






31. A scale that represents an ordinal scale such as looks on a scale from 1 to 10.






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. Because variables conforming only to nominal or ordinal measurements cannot be reasonably measured numerically - sometimes they are grouped together as






34. 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.






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






36. 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.






37. 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






38. The standard deviation of a sampling distribution.






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






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






41. Is inference about a population from a random sample drawn from it or - more generally - about a random process from its observed behavior during a finite period of time.






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






43. 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.






44. A measurement such that the random error is small






45. E[X] :






46. 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.






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






48. (pdfs) and probability mass functions are denoted by lower case letters - e.g. f(x).






49. 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






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







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