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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.
  • 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. 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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2. 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






3. Rejecting a true null hypothesis.






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






5. 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.






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






7. 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.






8. 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.






9. 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






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






11. Failing to reject a false null hypothesis.






12. 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).






13. Var[X] :






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






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






16. 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.






17. Some commonly used symbols for sample statistics






18. 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'






19. 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






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






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






22. Where the null hypothesis fails to be rejected and an actual difference between populations is missed giving a 'false negative'.






23. A list of individuals from which the sample is actually selected.






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






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






26. Gives the probability distribution for a continuous random variable.






27. E[X] :






28. When info. in a contingency table is re-organized into more or less categories - relationships seen can change or reverse.

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






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






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






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






34. 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}.






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






36. 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






37. Probability of rejecting a true null hypothesis.






38. Is that part of a population which is actually observed.






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






40. Is one that explores the correlation between smoking and lung cancer. This type of study typically uses a survey to collect observations about the area of interest and then performs statistical analysis. In this case - the researchers would collect o






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






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






43. 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






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






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






46. Is a sample and the associated data points.






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






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






49. (or expectation) of a random variable is the sum of the probability of each possible outcome of the experiment multiplied by its payoff ('value'). Thus - it represents the average amount one 'expects' to win per bet if bets with identical odds are re






50. Is a typed measurement - it can be a boolean value - a real number - a vector (in which case it's also called a data vector) - etc.