MATCHING TYPEChoices:• 3rd decile• level of significance• continuous• critical region• probability sampling• sample survey• sample space• census• mean• grouped data• inferential statistics• statistic• sample• Type II error• stratified sampling• skewness•ratio scale• simple random sampling.• parameter• interval scale• alternative hypothesis• seventh decile• Type I error• kurtosis• mode• median• pie chart• experiment method• null hypothesis• coefficient of variation Questions:41. a conclusion that two populations are not differentfrom each other when in fact they are42. a value which divides an array of observations intotwo equal parts43. the region where the null hypothesis is rejected44. usually represents the inverse of one's researchhypothesis45. a random variable which takes on uncountablyinfinite number of points46. a graph used in showing the component parts of awhole47. designed for data collection under controlledconditions48. a sampling scheme where heterogeneous populationis divided into homogeneous groups49. method of collecting data from every element in thepopulation50. measure of relative dispersion 50. measure of relative dispersion51. a conclusion that there is a true difference betweentwo populations when in fact they are not52. collection of all possible outcomes in a statisticalexperiment53. a measure of asymmetry54. 70 percent of the observations in the data set isabove this value55. a value computed from the population56. a measurement scale with a true or absolute zero57. set of techniques that deals with generalizations to alarger group where the sample came from58. a subset of the population59. Given that your null hypothesis is that you do nothave coronavirus, it is an error made when the testresult says you COVID negative, but you actually do60. Given that your null hypothesis is that you do nothave coronavirus, it is an error made when the testresult says you are COVID positive, but you actually don't

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Answered: 41. a conclusion that two populations…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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MATCHING TYPE Choices: • 3rd decile • level of significance • continuous • critical region • probability sampling • sample survey • sample space • census • mean • grouped data • inferential statistics • statistic • sample • Type II error • stratified sampling • skewness •ratio scale • simple random sampling. • parameter • interval scale • alternative hypothesis • seventh decile • Type I error • kurtosis • mode • median • pie chart • experiment method • null hypothesis • coefficient of variation

50. measure of relative dispersion
51. a conclusion that there is a true difference between
two populations when in fact they are not
52. collection of all possible outcomes in a statistical
experiment
53. a measure of asymmetry
54. 70 percent of the observations in the data set is
above this value
55. a value computed from the population
56. a measurement scale with a true or absolute zero
57. set of techniques that deals with generalizations to a
larger group where the sample came from
58. a subset of the population
59. Given that your null hypothesis is that you do not
have coronavirus, it is an error made when the test
result says you COVID negative, but you actually do
60. Given that your null hypothesis is that you do not
have coronavirus, it is an error made when the test
result says you are COVID positive, but you actually don't

Definition Definition Measure of central tendency that is the value that occurs most frequently in a data set. A data set may have more than one mode if multiple categories repeat an equal number of times. For example, in a data set with five item—3, 5, 5, 29, 473—the mode is 5 because it occurs twice and no other value occurs more than once. On a histogram or bar chart, the element with the highest bar represents the mode. Therefore, the mode is sometimes considered the most popular option. The mode is useful for nominal or categorical data (e.g., the most common color car that users purchase), but it is problematic for continuous data because it is more likely not to have any value that is more frequent than the other.

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