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






2. 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}.






3. 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.






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. 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






6. Is a measure of the 'peakedness' of the probability distribution of a real-valued random variable. Higher kurtosis means more of the variance is due to infrequent extreme deviations - as opposed to frequent modestly sized deviations.






7. The objects described by a set of data: person (animal) - place - and - thing. (SUBJECTS)






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






9. Some commonly used symbols for population parameters






10. Any specific experimental condition applied to the subjects






11. 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.






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






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






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






15. Statistics involve methods of using information from a sample to draw conclusions regarding the population.






16. Are simply two different terms for the same thing. Add the given values






17. Some commonly used symbols for sample statistics






18. 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






19. 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).






20. 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






21. 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






22. Changes over time that show a regular periodicity in the data where regular means over a fixed interval; the time between repetitions is called the period.






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






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






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






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






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






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






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






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

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31. Is a parameter that indexes a family of probability distributions.






32. S^2






33. A subjective estimate of probability.






34. 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.






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






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






37. 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.






38. Is a set of entities about which statistical inferences are to be drawn - often based on random sampling. One can also talk about a population of measurements or values.






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






40. 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).






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






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






43. 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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44. Is inference about a population from a random sample drawn from it or - more generally - about a random process from its observed behavior during a finite period of time.






45. Is a sample and the associated data points.






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






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






49. To find the median value of a set of numbers: Arrange the numbers in numerical order. Locate the two middle numbers in the list. Find the average of those two middle values.






50. Rejecting a true null hypothesis.