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






2. Cov[X - Y] :






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






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






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






6. 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.






7. Is data arising from counting that can take only non-negative integer values.






8. 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






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






10. 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.






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






12. A variable that has an important effect on the response variable and the relationship among the variables in a study but is not one of the explanatory variables studied either because it is unknown or not measured.






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






14. 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






15. Is the most commonly used measure of statistical dispersion. It is the square root of the variance - and is generally written s (sigma).






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






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






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






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






20. 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






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






22. (or expectation) of a random variable is the sum of the probability of each possible outcome of the experiment multiplied by its payoff ('value'). Thus - it represents the average amount one 'expects' to win per bet if bets with identical odds are re






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






24. 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






25. Is a sample and the associated data points.






26. 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






27. 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)






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






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






30. 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.






31. 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.






32. Can be - for example - the possible outcomes of a dice roll (but it is not assigned a value). The distribution function of a random variable gives the probability of different results. We can also derive the mean and variance of a random variable.






33. A measurement such that the random error is small






34. Have no meaningful rank order among values.






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






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






37. 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.






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






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






40. Probability of rejecting a true null hypothesis.






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






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






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






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






45. 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.






46. (or atomic event) is an event with only one element. For example - when pulling a card out of a deck - 'getting the jack of spades' is an elementary event - while 'getting a king or an ace' is not.






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






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






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






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