A Z-test one proportion is a statistical test for which the distribution of the test statistic under the null hypothesis can be approximated by a normal distribution. Due to the central limit theorem, this test statistic is approximately normally distributed for large samples. So, for the Z-test one proportion to be applicable, certain conditions must be met:
1. Nuisance parameters should be known, or estimated with high accuracy (an example of a nuisance parameter would be the standard deviation in a one-sample location test). Z-test one proportion focus on a single parameter, and treat all other unknown parameters as being fixed at their true values. In practice, due to Slutsky's theorem,
"plugging in" consistent estimates of nuisance parameters can be justified.
2. The test statistic should follow a normal distribution. Generally, one appeals to the central limit theorem to justify assuming that a test statistic varies normally. There is a great deal of statistical research on the question of when a test statistic varies approximately normally. If the variation of the test statistic is strongly non-normal, a Z-test test proportion should not be used.(UCLA, 2000)
With the circumstances above, the researchers with the advice of Statistician, Ms. Yumi Valenzuela, decided to use the Z-test one proportion. Since the main objective of the research is that the readings of SANRice should be at least matched the readings of LCC, the first condition of the Z-test one proportion was met. The statistician also reminded the researchers that the testing should have at least 30 samples in using the Z-test one proportion to be reliable. For the second condition, since the output only varies if it matched the LCC’ readings or not, it is
justifiable to assume that the test statistics varies normally. With the conditions satisfied, it is safe to assume that the said statistical approach will really show the reliability of the research.
3.6.1 Results and Discussion
The tables of the values in the final testing are at the Appendix (see Appendix C) and below are the formula and the output of the statistical test.
no. of samples : 30
success : 24
success rate considering Z-test One Proportion :
Statistical method: Z-test One Proportion
Null hypothesis (Ho): the output of automated device is not significantly different from the manual output.
Decision: reject Ho if computed < 0.67 (critical value by looking at z-table, see Appendix D)
Where: Z-value =
(3.1)
Where:
= (3.2)
P = hypothesized value x –no. of success n – no. of samples
= = 0.8 Z-value =
= 3.2863
The final testing was consisted of 30 leaf samples which were divided in three set with 10 leaf samples each. The leaf samples were manually checked by Mr. Anacleto B. Esplana, a rice specialist in the Department of Agriculture, who’s an expert regarding in Leaf Color Chart. The researchers were also aware that the final testing between LCC and SANRice should be synchronized. So, after Mr. Esplana was done in a specific leaf, the SANRice immediately took picture of that leaf for its own reading so that the testing will be consistent because the leaf’s condition might change in a short time due to many factors.
It can be seen that after the final testing, out of 30 leaf samples 24 of the readings of SANRice matched the readings of Mr. Esplana. The values gathered in the final testing was then set to the formula of Z-test one proportion and the output was 3.2863. Since the test statistic of 3.2863 exceeds the critical value of 0.67, the null hypothesis (Ho) is accepted, thus it can be concluded that the readings of SANRice matched the readings of Leaf Color Chart.
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CONCLUSION
The researchers were able to meet their objectives, which is to develop an Android-based application that will determine the level of nitrogen deficiency of rice. The project also aims to improve the capability of the LCC by automation reading and by using android based features which allows the user to save his/her current readings in the database. During the actual testing, it was found that the discrepancy of reading was very minimal comparing the leaf color chart to the developed android application, which only shows the success in the projects accuracy in reading. The application was able to overcome the bias of color perception and color blindness since anyone who is not oriented to use LCC and with the disability of color perception cannot do readings without the bias of color perception. Since the light is controlled through the light module, the sunlight does not affect the consistency of the greenness of leaves and of the captured image in general. Therefore, it could be used in any time of the day. It is concluded that this project has provided particular upgrades necessary for the improvement of the LCC in future application.
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RECOMMENDATION
The researchers have the following recommendations to improve the application and its functions:
a) Applicability to other crops since the leaf color chart adopted as standard in this application is only applied to rice.
b) Multi-user functionality since the application does not have the ability to distinguish one user from another which might pertain to a different rice population.
c) A more reliable source of power for the controlled-light module: Even if the module’s source of power seems reliable as can be manifested by the consistency of the results based on the trials conducted, it is preferable if the source will come from the tablet itself.
d) For a more accurate setting of standard values, it is recommended to use a SPAD meter since it outputs a reading which could be easily compared to the reading of the application.
e) It is desirable that the results will be accessible through the internet or may have a printable version.