Demographic information and Data of the Research Respondents
A conventional research process requires that the sample size would be reasonably large and sufficient to represent the whole research population. As such it would be imperative and critical to ensure that the sample size would be fair and comprehensive to include all the people covered in the research process (Valadabadi et al. 2010). To begin with, the study sites of KwaDube, Ulundi and KwaCeza were selected for the subsequent considerations. The research respondents were twenty three, thirteen and twenty two in KwaDube, Ulundi and KwaCeza respectively. This stems from a background of the representation of one hundred percent wholeness for a research process.
Theoretically, a factor or a variable with a p-value less than or equal to 0.05 is considered statistically relevant. This is to say that the value would hold some significance in the actual representation of reality that would be obtained from such postulations. In the study, gender, age group, type of farm and the outright consideration for the research respondent as being farmer fell below the threshold of 0.05. Hence, they were significant and practically essential in the final computation and analysis of the research. Precisely, gender had a p-value of 0.000 that means that it was significant. Ag group on the hand had 0.004, consideration of being a farmer 0.001 and the type of farm had 0.000. Thus, these variables had a bearing and contributed to the final inference of the research process.
However, all the other prognostic variables considered during the collection of data and information was insignificant and irrelevant. This is to say that they did not have a lot of realistic importance in the confirmation or subsequent rejection of the suggested hypotheses. This argument and logic stems from the fact that all the mathematical or statistical p-value that lies above the minimum threshold of 0.05 is insignificant. Implying that the null hypothesis suggested would hold as true or would not be rejected. Accurately, education as a variable has a greater p-value of 0.368 which is higher than the irreducible minimum of 0.05. Intuitively, this is to say that the educational level of a research respondent would not dictate the direction of the research process to any great extent.
Figure 1: QN 8 Responses of respondents from three sites, KwaDube, Ulundi and KwaCeza District when asked if they thought hail damage could be avoided.
This table sought to get the qualitative opinion of the research respondents on the question of whether or not they believed that the damage that hail could occasion would be avoided or limited. It was to inquire about the feel and opinion on the veracity and extent of the damage that that the hail would effect and lead to in their farms. Precisely, in KwaDube research site forty three percent of the respondents believed that the damage would be avoided and extent of harm limited or hampered. Comparatively, fifty seven percent of the participants were of the contrary opinion, meaning that they did not attach high value or weight on the likelihood of hail affecting or damaging their farms. The intuition and line of thought was the research participants believed that the corrective or remedial measures that would have been taken to avert the effects of the hail would have been irrelevant and insignificant. As such, the effects of the hail would have been negligible and could not have affected the outcome and performance of the farms. In Ulundi, twenty three percent of the research participants opined that the damage that could be occasioned by the hails would have to be averted. On the contrary, an overwhelming majority of the respondents differed in their view and opinion as they believed that the damage could not be avoided. In essence, the opposing majority asserted that the damage of the hail was somewhat of a natural occurrence and that no human action would have replicated or improved the situation. In KwaCeza, a minority five percent held that the negative effects of the hail could be avoided while the remaining majority of ninety percent did not share in that opinion, belief or line of thought.
Figure 2: QN 10 Responses of respondents from three sites KwaDube, Ulundi and KwaCeza District when asked if they thought hail damage could be minimized.
The research participants in KwaDube had little faith and belief that the veracity and extent of the damage caused by hail could be reduced. Nine percent of the representatives believed that it was possible to limit or minimize the extent of the damage while the remaining majority of ninety one percent did not partake in that line of thought. It is to say that ninety one percent believed that it was not viable or feasibly possible to minimize the extent of damage occasioned by hail. In Ulundi, twenty three percent of the respondents believed that it was feasible and practically possible to reduce the extent of the damage of the hail. The majority of seventy seven percent opposed that notion. Interestingly, the respondents in KwaCeza held by majority position of seventy seven percent that it was practical and possible to reduce the damage while twenty three percent did not.
Intuitively, it is critical and imperative to note and mention that hail is a natural climatic occurrence or situation. Natural calamities seem to catch most people around the world off-guard without any response mechanism due to the power of nature. As a result, it is not surprising when most of the respondents from the study sites did not believe that hail could be controlled or its adverse effects limited. Thus, the respondents in KwaCeza had a superior intuition due to their enlightenment levels and skill, exposure or technology that could have be used in that respect. The other respondents in other places did not particularly hold a similar view either due to their inferior skill level and technological exposure and options that would limit or reduce the extent of the damage caused by hail. Presumably, they did not have the wide and proper technological or skill level to balance and control the extent of the hail damage.
OUTPUT 1 Leaf_1: Interpretation
The identifier cultivar prognostic or variable had an inclusive sample size of one hundred and sixty two. The research process accounted for all of them and none of them was recorded as having missed. It also has three distinct levels that are denoted as A, B, C. Another descriptive element is the identifier Hail sums as an ordinal categorical factors that is critical in a research process. It is imperative to mention that nominal elements were omitted in this respect due to the categorical nature of the ordinal factors such as hail. This is to say that the identifier could have been categorized to make sensible statistical statement. For instance, the veracity and extent of the Hail could have mild or extreme as would have been measured by the climatic tools or measurements.
Identifier leaf on its end was a continuous variable because it can be computed mathematically to arrive at a designated range or scope (Zamir et al. 2011). Exactly, the average intonation or minimum of 2.333, an approximate mean of 8.282 and maximum limit of 14.00. It is important to note and mention that the continuous variables are workable and statistically actionable. Thus, the values obtained demarcated the range and scope or sphere of reach for the computations.
The ANOVA technique refers to the Analysis of Variance approach where the Mean is repeated computed to remove any bias or statistical errors that could have incurred. The intention is to compare the means for more than two groups or data sets. Thus, the final representation of the figures was alive to the technical agility of the means since it encompasses or uses the variances the compare the means. The table of ANOVA represented some factors or variables that had some significance and essence to the research process. This showed that at the very least one of the means was different and varied to some large extent from the rest. Definitely, Cultivar, Date WAP and Density as variables had some mean value of 0.002, 0.001 correspondingly. These values are significant because they fall below the conventional standard of 0.05 at the level of significance. Introspectively, all the other variables were insignificant because their values were pegged above the level of significance representation.
Explicitly, Hail, Cultivar.Date, Cultivar.Density, Date_WAP.Density, Cultivar.Hail, Date_WAP.Hail, Density.Hail, Cultivar.Date_WAP, Density, Cultivar.Date_WAP.Hail Cultivar.Density.Hail, Date_WAP.Density.Hail, Cultivar.Date_WAP.Density.Hail are statistically insignificant because their value lay above 0.05. Hail and all the above mentioned variables with their interactions do not hold any mathematical or iterative weight to the direction of the inferences and the research process. Hence, they were non-essential.
The residuals or the error margins also represented the societally acceptable range of mistakes and other inadequacies. In a research process, it is common for deficiencies, mistakes and errors to occur. Meaning that the final conclusion would have to consider and factor in the degree or extent of the margin of error. Approximately, the residual or error margin was means sum of squares of roughly 1.166 that implied that was within a verifiable or believable range. In other words the error was minimal and negligible.
Table of Means
Implicitly, in Agricultural processes a yield with a greater mean value implies that it was the best in such a comparison as balanced with other factors in the same iteration (Luque et al. 2006). This is to say that the biggest mean was the best, and then the other respective or following value was moderate while the least was the worst performing. In the research process, Cultivar A had the highest mean of 9 implying that tit was the best performing or ranked. Variable B and C had lesser values of 8, meaning that they were lesser in terms of performance and rank in comparative terms and sense. VT 12 on the other hand was better as balanced or compared with VT 4due to that higher representation of the higher mean value. Hail on its end also had a mean value of 8 for all the components of A, B and C.
Least Significant Difference of Means:
In a statistical consideration and matter, the least significant differences of mean represents an enhanced computation of the classical t-test that is used to check whether the means obtained are different or similar. Thus, the tables or computations of the t-test helped to gauge that variation or difference. This is to say that the Cultivar has a significant mean of 0.4 that shows essential consideration of the mean. In principle, the other considerations and aspects that border on the Date_WAP, Density and Hail all had a similar value. The difference in the means of the factors from VT was 8 that depicted that it was a reasonably big mean. As such Tahir et al. (2009) states that, the difference in the mean values would be considered to be large in a statistical consideration, aspect and setting.
Coefficient of Variation:
The coefficient of variation is critical to compute the range of dispersion of the given data. Thus, the values were beneficial and important to relate the degree of spread or variation that the information and the data experienced. Implicitly, the measure of dispersion in the data or study had a 1.8% variability or degree of spread as it was represented and explained by the replication table. As such, the final results and inferences may experience a reasonable spread and dispersion of 1.8%. Also, the replication units had a comparative measure of 13% that was bigger than the replication due to interaction. It is to say...
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