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2021
In the simplest type of situation considered in this chapter, each observation in a sample is classified as belonging to one of a finite number of categories—for example, blood type could be one of the four categories O, A, B, or AB. With pi denoting the probability that any particular observation belongs in category i, we wish to test a null ...
Jay L. Devore +2 more
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In the simplest type of situation considered in this chapter, each observation in a sample is classified as belonging to one of a finite number of categories—for example, blood type could be one of the four categories O, A, B, or AB. With pi denoting the probability that any particular observation belongs in category i, we wish to test a null ...
Jay L. Devore +2 more
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2012
You learned about the chi-square distribution when we discussed confidence intervals for the variance and standard deviation in Chapter 8. Another contribution of Karl Pearson, the chi-square distribution has proven to be quite versatile. We use chi-square tests for determining goodness of fit and for determining the association or lack thereof for ...
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You learned about the chi-square distribution when we discussed confidence intervals for the variance and standard deviation in Chapter 8. Another contribution of Karl Pearson, the chi-square distribution has proven to be quite versatile. We use chi-square tests for determining goodness of fit and for determining the association or lack thereof for ...
+4 more sources
2012
Previous chapters have presented information on sampling distributions, Central Limit Theorem, confidence intervals, TYPE I error, TYPE II error, and hypothesis testing. This information is useful in understanding how sample statistics are used to test differences between population parameters.
Randall Schumacker, Sara Tomek
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Previous chapters have presented information on sampling distributions, Central Limit Theorem, confidence intervals, TYPE I error, TYPE II error, and hypothesis testing. This information is useful in understanding how sample statistics are used to test differences between population parameters.
Randall Schumacker, Sara Tomek
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1984
The tests in this chapter (with one exception) are concerned with data in the form of frequencies, that is to say with counts of the numbers of items belonging to various groups or classes. The tests may not be used on data in the form of ranks or measurements.
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The tests in this chapter (with one exception) are concerned with data in the form of frequencies, that is to say with counts of the numbers of items belonging to various groups or classes. The tests may not be used on data in the form of ranks or measurements.
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A Guide to Chi-Squared Testing
Journal of the American Statistical Association, 1997J. Best, P. E. Greenwood, M. S. Nikulin
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Combined Neyman–Pearson chi-square: An improved approximation to the Poisson-likelihood chi-square
Nuclear Instruments and Methods in Physics Research, Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 2020Xin Qian, Hanyu Wei, Chao Zhang
exaly