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1	Analysis of Covariance with Linear Regression Error Model on Antenna Control Unit Tracking Laird, Daniel T. 10 1900 (has links) ITC/USA 2015 Conference Proceedings / The Fifty-First Annual International Telemetering Conference and Technical Exhibition / October 26-29, 2015 / Bally's Hotel & Convention Center, Las Vegas, NV / Over the past several years DoD imposed constraints on test deliverables, requiring objective measures of test results, i.e., statistically defensible test and evaluation (SDT&E) methods and results. These constraints force the tester to employ statistical hypotheses, analyses and perhaps modeling to assess test results objectively, i.e., based on statistical metrics, probability of confidence and logical inference to supplement rather than rely solely on expertise, which is too subjective. Experts often disagree on interpretation. Numbers, although interpretable, are less variable than opinion. Logic, statistical inference and belief are the bases of testable, repeatable and refutable hypothesis and analyses. In this paper we apply linear regression modeling and analysis of variance (ANOVA) to time-space position information (TSPI) to determine if a telemetry (TM) antenna control unit (ACU) under test (AUT) tracks statistically, thus as efficiently, in C-band while receiving both C- and S-band signals. Together, regression and ANOVA compose a method known as analysis of covariance (ANCOVA). In this, the second of three papers, we use data from a range test, but make no reference to the systems under test, nor to causes of error. The intent is to present examples of tools and techniques useful for SDT&E methodologies in testing. Null- and alternative-hypotheses tracking mode TM AGC ACU Scan rate Slew rate TSPI samples GPS INS observables calculated dataframe R inner-product modeling ANOVA ANCOVA F-test and t-test PDF CDF
2	INVERSE SAMPLING PROCEDURES TO TEST FOR HOMOGENEITY IN A MULTIVARIATE HYPERGEOMETRIC DISTRIBUTION Liu, Jun 04 1900 (has links) <p>In this thesis we study several inverse sampling procedures to test for homogeneity in a multivariate hypergeometric distribution. The procedures are finite population analogues of the procedures introduced in Panchapakesan et al. (1998) for the multinomial distribution. In order to develop some exact calculations for critical values not considered in Panchapakesan et al. we introduce some terminologies for target probabilities, transfer probabilities, potential target points, right intersection, and left union. Under the null and the alternative hypotheses, we give theorems to calculate the target and transfer probabilities, we then use these results to develop exact calculations for the critical values and powers of one of the procedures. We also propose a new approximate calculation. In order to speed up some of the calculations, we propose several fast algorithms for multiple summation.</p> <p>N >= 1680000, all the results are the same as those in the multinomial distribution.</p> <p>The computing results showed that the simulations agree closely with the exact results. For small population sizes the critical values and powers of the procedures are different from the corresponding multinomial procedures, but when</p> / Master of Science (MSc) Inverse Sampling Procedures Homogeneity Multivariate Hypergeometric Distribution Target Probabilities Transfer Probabilities Potential Target Points Alternative Hypotheses Null Hypotheses Critical Values Powers Applied Statistics Multivariate Analysis Numerical Analysis and Computation Probability Programming Languages and Compilers Statistical Models Theory and Algorithms Applied Statistics

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Analysis of Covariance with Linear Regression Error Model on Antenna Control Unit Tracking

INVERSE SAMPLING PROCEDURES TO TEST FOR HOMOGENEITY IN A MULTIVARIATE HYPERGEOMETRIC DISTRIBUTION