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The purpose of this paper was to introduce the calculation formulas and the SAS implementation of the analysis of variance of the univariate quantitative data with the Latin square design. The Latin square design could be divided into two categories： the general Latin square design and the Greek Latin square design. The former could be used for the experimental situation with one experimental factor and two block factors， the latter could be used for the experimental situation with two experimental factors and two block factors. In fact， Latin square designs could be further subdivided by whether or not the repeated experiments were performed and whether the block factor was a single individual type. Generally speaking， in addition to satisfying the requirements of "independence， normality and homogeneity of variance"， the interaction between all factors was required to be non-existent or negligible when performing an analysis of variance on the quantitative data with Latin square design. When the quantitative data did not meet the preconditions mentioned above， it was recommended to use a mixed-effects model to build the model and solve it， or to solve the estimated values of the parameters in the ANOVA model based on the generalized estimating equation method.
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The purpose of the paper was to introduce the calculation formulas and the SAS implementation of the analysis of variance for the quantitative data of the crossover design. In the calculation， three test statistics were involved， namely Ftreatment， Fstage and Findividual. They were three test statistics used to evaluate the statistical significance of the effect of the treatment factor， the stage factor， and the individual factor on the quantitative outcome variable， respectively. In general， it was assumed that there was no or negligible interaction among the three factors in a crossover design， so there was no need to evaluate whether the interaction term was statistically significant. With the help of SAS software， this paper conducted the univariate analysis of variance for the quantitative data of crossover designs for three examples of 2×2 crossover design， 3×3 crossover design and three-stage crossover design， and presented the calculation results and drew the statistical and professional conclusions.
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The purpose of this paper was to introduce the model， calculation formulas and the SAS implementation of the analysis of variance for the quantitative data with balanced incomplete block design. In the calculation， two test statistics were involved， namely FA and FB. Among them， the subscript "A" represented the experimental factor， and the subscript "B" represented the block factor B （i.e.， the important non-experimental factor）. In general， it was assumed that there was no or negligible interaction between the two factors in a balanced incomplete block design， so there was no need to evaluate whether the interaction term was statistically significant. Therefore， it was not necessary to do repeated experiments under each combination of two factors. With the help of SAS software， this paper conducted the analysis of variance for the quantitative data with balanced incomplete block design on two examples， and presented the calculation results and made the statistical and professional conclusions.
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The purpose of this paper was to introduce the model， calculation formulas and the SAS implementation of the univariate analysis of variance for the quantitative data with randomized complete block design. In the calculation， two test statistics were involved， namely FA and FB. Among them， the subscript "A" represented the experimental factor， and the subscript "B" represented the block factor （i.e.， the important non-experimental factor）. In general， it was assumed that there was no or negligible interaction between the two factors in a randomized block design， so there was no need to assess whether the interaction term was statistically significant. Therefore， it was not necessary to do repeated experiments under each combination of two factors. With the help of SAS software， this paper conducted the analysis of variance for the quantitative data with randomized complete block design for two instances without and with repeated experiments， gave the calculation results， and made the statistical and professional conclusions.
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Blending uniformity is essential to ensure the homogeneity of Chinese medicine formula particles within each batch. This study was based on the blending process of ebony spray dried powder and dextrin(the proportion of dextrin was 10%),in which the analysis of near infrared (NIR) diffuse reflectance spectra was collected from six different sampling points in combination with moving window F test method in order to assess the blending uniformity of the blending process.The method was validated by the changes of citric acid content determined by the HPLC. The results of moving window F test method showed that the ebony spray dried powder and dextrin was homogeneous during 200-300 r and was segregated during 300-400 r. An advantage of this method is that the threshold value is defined statistically, not empirically and thus does not suffer from threshold ambiguities in common with the moving block standard deviatiun (MBSD). And this method could be employed to monitor other blending process of Chinese medicine powders on line.
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Abstract Introduction The main drawback of a Brain-computer Interface based on Steady-State Visual Evoked Potential (SSVEP-BCI) that detects the emergence of visual evoked potentials (VEP) in reaction to flickering stimuli is its muscular dependence due to users must redirect their gaze to put the target stimulus in their field of view. In this work, a novel setup is evaluated in which two stimuli are placed together in the center of users' field of view, but with dissimilar distances from them, so that the target selection is performed by focus shifting instead of head, neck and/or eyeball movements. Methods A model of VEP generation for the novel setup was developed. The Spectral F-test based on Bartett periodogram was used to evaluate the null hypothesis of absence of effects of the non-focused stimulus (NFS) within the VEP elicited by the focused stimulus (FS). To reinforce that there is not statistical evidence to support the presence of NFS effects, the PSDA detection method was employed to find the frequency of FS. Electroencephalographic signals of nine subjects were recorded. Results Approximately in 80% of the tests, the null hypothesis with 5% level of significance was non-rejected at the fundamental frequency of NFS. The average of the accuracy rate attained with PSDA detection method was 79.4%. Conclusion Results of this work become further evident to state that if the focused stimulus (FS) will be able to elicit distinguishable VEP pattern regardless the non-focused stimulus (NFS) is also present.
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Introduction : This paper presents a detection method for upper limb movement intention as part of a brain-machine interface using EEG signals, whose final goal is to assist disabled or vulnerable people with activities of daily living. Methods EEG signals were recorded from six naïve healthy volunteers while performing a motor task. Every volunteer remained in an acoustically isolated recording room. The robot was placed in front of the volunteers such that it seemed to be a mirror of their right arm, emulating a Brain Machine Interface environment. The volunteers were seated in an armchair throughout the experiment, outside the reaching area of the robot to guarantee safety. Three conditions are studied: observation, execution, and imagery of right arm’s flexion and extension movements paced by an anthropomorphic manipulator robot. The detector of movement intention uses the spectral F test for discrimination of conditions and uses as feature the desynchronization patterns found on the volunteers. Using a detector provides an objective method to acknowledge for the occurrence of movement intention. Results When using four realizations of the task, detection rates ranging from 53 to 97% were found in five of the volunteers when the movement was executed, in three of them when the movement was imagined, and in two of them when the movement was observed. Conclusions Detection rates for movement observation raises the question of how the visual feedback may affect the performance of a working brain-machine interface, posing another challenge for the upcoming interface implementation. Future developments will focus on the improvement of feature extraction and detection accuracy for movement intention using EEG data.
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A triploid (2n = 3x = 36) rice plant was obtained by screening a twin seedling population in which each seed germinated to two or three sprouts that were then crossed with diploid plants. One diploid plant was chosen among the various F1 progenies and developed into an F2 population via self-pollination. Compared with the control variety Shanyou 63, this F2 population had a stable agronomical performance in field trials, as confirmed by the F-test. The stability of the F2 population was further substantiated by molecular analysis with simple sequence repeat markers. Specifically, of 160 markers assayed, 37 (covering all 12 chromosomes) were polymorphic between the parental lines. Testing the F1 hybrid individually with these markers showed that each PCR product had only a single band instead of two bands from each parent. The bands were identical to either maternal (23 markers) or paternal (eight markers) bands or distinct from both parents (six markers). The amplified bands of all 60 randomly selected F2 plants were uniform and identical to those of the F1 hybrid. These results suggest that the F1 plant is a non-segregating hybrid and that a stable F2 population was obtained. This novel system provides an efficient means for shortening the cycle of hybrid rice seed production.
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P. falciparum malaria is a severe form of disease which requires urgent diagnosis and treatment to save the life of patient. Blood smear examination is the commonest method used for diagnosis. The present study was done to evaluate ParaSight F test in patients of P. falciparum infection.The study was performed on 100 patients who where clinically diagnosed as cases of P. falciparum infection. ParaSight F test and Leishman stained blood smear examination was done in all 100 patients (50 patients of cerebral malaria + 50 patients of acute malaria).ParaSight F test was positive in 45 patients and blood smear positive in 28 patients of cerebral malaria. 35 patients of acute malaria were positive by ParaSight F test while blood smear was positive in 15 patients. Sensitivity, specificity, positive and negative predictive values of ParaSight F test are 95.7%, 100%, 100%, 100%, 60% in cerebral malaria and 100% each in acute malaria. ParaSight F test can be used as diagnostic tool in cases of P. falciparum infections, where blood smear is negative.
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Objective To study a statistical method about agreement testing to continuous variable.Methods Two measurements was carried out to the same subject,or the same two instruments were used to measure many paired individuals.Results We provided a statistical method(scatter gram,F-test)to repeated data and non-repeated data.Conclusion Correct choice of statistical method relies on the purpose and condition of actual problem.
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Background: Paracheck F.test is rapid diagnostic test that use to determine presence of histidine rich protein 2-HPR-2, a component of Plasmodium falciparum in total blood. Paracheck F.test producted by India\r\n', u'Objective: To study the sensitivity, specificity and diagnosed conformity of Rapid Diagnosis Test (Paracheck F test) compared with microscopic method for detecting P.falciparum \r\n', u'Subject and method: The study was conducted in 5 communes of Quang Tri province and 1 commune of Quang Binh province from September to November in 2006. A total of 470 blood samples were collected and examined with 2 methods for detecting P falciparum (Microscopy and Rapid Diagnosis Test- Paracheck). \r\n', u'Results:Among these blood samples, there were 438 samples having complete conformity in both two methods. 29 samples were negative with microscopy but positive with Paracheck. 3 samples were positive with Paracheck but negative with microscopy.\r\n', u'Conclusion: the sensitivity and specificity of Paracheck is 99% and 82%, respectively. Both microscopic and Paracheck methods have a high conformity in detecting P falciparum with the Kappa index of 0.84. \r\n', u'