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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.
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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 the probability of two events occurring together. The joint probability of A and B is written P(A and B) or P(A - B).






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






3. 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.






4. 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






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






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






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






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






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






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






11. 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






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






13. Cov[X - Y] :






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






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

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16. 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






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






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






20. 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






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






22. Probability of accepting a false null hypothesis.






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






24. The standard deviation of a sampling distribution.






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






26. Is a measure of the asymmetry of the probability distribution of a real-valued random variable. Roughly speaking - a distribution has positive skew (right-skewed) if the higher tail is longer and negative skew (left-skewed) if the lower tail is longe






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






28. Is a sample and the associated data points.






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






30. Some commonly used symbols for population parameters






31. 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.






32. 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






33. To find the average - or arithmetic mean - of a set of numbers:






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






35. 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.






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






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






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






39. 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.






40. 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.






41. 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'






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






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






44. Any specific experimental condition applied to the subjects






45. Probability of rejecting a true null hypothesis.






46. 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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47. 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.






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






49. (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






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