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






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






3. 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}.






4. ?r






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






6. A variable has a value or numerical measurement for which operations such as addition or averaging make sense.






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






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






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






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






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






13. 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.






14. E[X] :






15. 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.






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






17. Have meaningful distances between measurements defined - but the zero value is arbitrary (as in the case with longitude and temperature measurements in Celsius or Fahrenheit)






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






19. 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.






20. 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.






21. Are two related but separate academic disciplines. Statistical analysis often uses probability distributions - and the two topics are often studied together. However - probability theory contains much that is of mostly of mathematical interest and no






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






23. The result of a Bayesian analysis that encapsulates the combination of prior beliefs or information with observed data






24. 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.






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






26. Two events are independent if the outcome of one does not affect that of the other (for example - getting a 1 on one die roll does not affect the probability of getting a 1 on a second roll). Similarly - when we assert that two random variables are i






27. Probability of rejecting a true null hypothesis.






28. 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






29. The standard deviation of a sampling distribution.






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






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






32. Is a process of selecting observations to obtain knowledge about a population. There are many methods to choose on which sample to do the observations.






33. When you have two or more competing models - choose the simpler of the two models.






34. (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






35. Performing the experiment following the experimental protocol and analyzing the data following the experimental protocol. 4. Further examining the data set in secondary analyses - to suggest new hypotheses for future study. 5. Documenting and present






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






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






38. Is a function of the known data that is used to estimate an unknown parameter; an estimate is the result from the actual application of the function to a particular set of data. The mean can be used as an estimator.






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






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






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






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






43. 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).






44. 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






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






46. In number theory - scatter plots of data generated by a distribution function may be transformed with familiar tools used in statistics to reveal underlying patterns - which may then lead to






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






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






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






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







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