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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. 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.






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






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






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






5. Interpretation of statistical information in that the assumption is that whatever is proposed as a cause has no effect on the variable being measured can often involve the development of a






6. Is defined as the expected value of random variable (X -






7. 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






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






9. 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.






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






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






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






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






14. A collection of events is mutually independent if for any subset of the collection - the joint probability of all events occurring is equal to the product of the joint probabilities of the individual events. Think of the result of a series of coin-fl






15. Of a group of numbers is the center point of all those number values.






16. Probability of accepting a false null hypothesis.






17. 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






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






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






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






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






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






23. 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.






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






25. 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






26. Some commonly used symbols for population parameters






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






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






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






30. Var[X] :






31. 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






32.






33. The collection of all possible outcomes in an experiment.






34. Have no meaningful rank order among values.






35. 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.






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






37. 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






38. 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






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






42. 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






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






44. 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






45. 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






46. 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.






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






48. 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.






49. 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






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