Test your basic knowledge |

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






2. Var[X] :






3. There are four main levels of measurement used in statistics: Each of these have different degrees of usefulness in statistical research.






4. Some commonly used symbols for sample statistics






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






6. Probability of accepting a false null hypothesis.






7. 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).






8. Is used to describe probability in a continuous probability distribution. For example - you can't say that the probability of a man being six feet tall is 20% - but you can say he has 20% of chances of being between five and six feet tall. Probabilit






9. 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.






10. Some commonly used symbols for population parameters






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






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






13. A measurement such that the random error is small






14. Any specific experimental condition applied to the subjects






15. Another name for elementary event.






16. 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






17. 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






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






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






21. Rejecting a true null hypothesis.






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






23. Occurs when a subject receives no treatment - but (incorrectly) believes he or she is in fact receiving treatment and responds favorably.






24. 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






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






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






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






28. 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.






29. To prove the guiding theory further - these predictions are tested as well - as part of the scientific method. If the inference holds true - then the descriptive statistics of the new data increase the soundness of that






30. 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






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






32. 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.






33. (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






34. 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.






35. 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.






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

Warning: Invalid argument supplied for foreach() in /var/www/html/basicversity.com/show_quiz.php on line 183


37. When there is an even number of values...






38. 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).






39.






40. (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






41. S^2






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






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






44. Is a sample and the associated data points.






45. 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)






46. 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.






47. 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.






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






49. A subjective estimate of probability.






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