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






2. Can be - for example - the possible outcomes of a dice roll (but it is not assigned a value). The distribution function of a random variable gives the probability of different results. We can also derive the mean and variance of a random variable.






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






4. 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.






5. Var[X] :






6. 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






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






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






9. 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.






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






11. Is often denoted by placing a caret over the corresponding symbol - e.g. - pronounced 'theta hat'.






12. A numerical facsimilie or representation of a real-world phenomenon.






13. Failing to reject a false null hypothesis.






14. 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.


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






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






17. 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.






18. 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






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






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






21. 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






22. When there is an even number of values...






23. (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.






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






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






26. 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






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






28. 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.






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






30. 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.






31. Some commonly used symbols for sample statistics






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






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






34. 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.






35. 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






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






37. 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.






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






39. 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






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






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






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






43. Statistical methods can be used for summarizing or describing a collection of data; this is called






44. Two events are independent if the outcome of one does not affect that of the other (for example - getting a 1 on one die roll does not affect the probability of getting a 1 on a second roll). Similarly - when we assert that two random variables are i






45. Samples are drawn from two different populations such that there is a matching of the first sample data drawn and a corresponding data value in the second sample data.






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






47. 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)






48. 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.






49. A measurement such that the random error is small






50. 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