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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. A subjective estimate of probability.






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






3. 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






4. 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.






5. Of a group of numbers is the center point of all those number values.






6. Failing to reject a false null hypothesis.






7. 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






8. 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.






9. Is a subset of the sample space - to which a probability can be assigned. For example - on rolling a die - 'getting a five or a six' is an event (with a probability of one third if the die is fair).






10. Var[X] :






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






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






13. 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.






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






15. Is the exact middle value of a set of numbers Arrange the numbers in numerical order. Find the value in the middle of the list.






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






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






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






19. 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.






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






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






22. 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.






23. 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






24. ?r






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






26. 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}.






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






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






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






30. 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






31. 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.






32. 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.






33. 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.






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






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






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






37. Rejecting a true null hypothesis.






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






39. 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






40. 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.






41. Error also refers to the extent to which individual observations in a sample differ from a central value - such as






42. Given two random variables X and Y - the joint distribution of X and Y is the probability distribution of X and Y together.






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






44. Many statistical methods seek to minimize the mean-squared error - and these are called






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






46. 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






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






48. 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).






49. 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






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