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






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






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






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






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






6.






7. 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






8. 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.






9. Planning the research - including finding the number of replicates of the study - using the following information: preliminary estimates regarding the size of treatment effects - alternative hypotheses - and the estimated experimental variability. Co






10. 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






11. (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






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






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






14. Some commonly used symbols for population parameters






15. 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






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






17. A data value that falls outside the overall pattern of the graph.






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






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






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






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






22. A subjective estimate of probability.






23. Ratio and interval measurements which can be either discrete or continuous - due to their numerical nature are grouped together as






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






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






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






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






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






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






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






31. Is the result of applying a statistical algorithm to a data set. It can also be described as an observable random variable.






32. 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.






33. 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.






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






35. 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).






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






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






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






39. Patterns in the data may be modeled in a way that accounts for randomness and uncertainty in the observations - and are then used for drawing inferences about the process or population being studied; this is called






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






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






42. Failing to reject a false null hypothesis.






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






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






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






46. Is the probability of some event A - assuming event B. Conditional probability is written P(A|B) - and is read 'the probability of A - given B'






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






48. A measurement such that the random error is small






49. 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






50. Have no meaningful rank order among values.