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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. Is a sample and the associated data points.






2. Is denoted by - pronounced 'x bar'.






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






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






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






6. Another name for elementary event.






7. Are usually written with upper case calligraphic (e.g. F for the set of sets on which we define the probability P)






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






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






10. Probability of rejecting a true null hypothesis.






11. 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






12. ?r






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






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






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






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






17. 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






18. 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.

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19. 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






20. A consistent - repeated deviation of the sample statistic from the population parameter in the same direction when many samples are taken.






21. 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






22. Two variables such that their effects on the response variable cannot be distinguished from each other.






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






24. ?






25. 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






26. 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.






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






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






29. A numerical measure that describes an aspect of a population.






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






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






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






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






34. S^2






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






36. A numerical measure that describes an aspect of a sample.






37. 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.






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






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






40. Failing to reject a false null hypothesis.






41. 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






42. 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.






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






44. 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






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






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






47. 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.






48. Have no meaningful rank order among values.






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






50. Working from a null hypothesis two basic forms of error are recognized: