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






2. 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






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






4. 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






5. ?






6. Is that part of a population which is actually observed.






7. A sample selected in such a way that each individual is equally likely to be selected as well as any group of size n is equally likely to be selected.






8. A consistent - repeated deviation of the sample statistic from the population parameter in the same direction when many samples are taken.






9. 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)






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






12. Cov[X - Y] :






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






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






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






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






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






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






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






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






21. Any specific experimental condition applied to the subjects






22. 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






23. Probability of accepting a false null hypothesis.






24. 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






25. S^2






26. 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).






27. 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






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






29. 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.

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


30. 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.






31. 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.






32. To find the average - or arithmetic mean - of a set of numbers:






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






34. Some commonly used symbols for sample statistics






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






36.






37. Have no meaningful rank order among values.






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






39. 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






40. Given two jointly distributed random variables X and Y - the conditional probability distribution of Y given X (written 'Y | X') is the probability distribution of Y when X is known to be a particular value.






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






42. 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.






43. Is a sample and the associated data points.






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






45. Is a measure of its statistical dispersion - indicating how far from the expected value its values typically are. The variance of random variable X is typically designated as - - or simply s2.






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






47. Is a sample space over which a probability measure has been defined.






48. 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






49. The objects described by a set of data: person (animal) - place - and - thing. (SUBJECTS)






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