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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 defined as the expected value of random variable (X -






2. E[X] :






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






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






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






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

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






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






10. 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.






11. 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.






12. 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.






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. 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






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






17. Samples are drawn from two different populations such that there is a matching of the first sample data drawn and a corresponding data value in the second sample data.






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






19. 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






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






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






23. 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






24. S^2






25. Another name for elementary event.






26. 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.






27. 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






28. Some commonly used symbols for population parameters






29. 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






30. 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.






31. 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






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






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






34. Have no meaningful rank order among values.






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






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






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






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






39. 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






40. 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






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






42. 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






43. A subjective estimate of probability.






44. 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.






45. Any specific experimental condition applied to the subjects






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






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






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






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






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