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Levenes Test for Equality of Variances T-Test for Equality of Means

Date:  2021-05-24 17:55:23
3 pages  (791 words)
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F Sig. t dfSig. (2-tailed) Mean Difference Std. Error Difference 95% Confidence Interval of the Difference

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Count Equal variances assumed .338 .570 -.029 14 .978 -16.5425000 576.7726200 -1253.5967370 1220.5117370

Equal variances not assumed -.029 13.769 .978 -16.5425000 576.7726200 -1255.5500570 1222.4650570


Testing for null hypothesis in the number of seabirds precisely at the edges of post-construction and reference situations involves assessing whether the logic in bpt = nt + dt + spp + (nt x spp) + (dt x spp) holds water. In this case, additional species in the winter were not present in the summer (e.g. sea ducks) but many of the seabirds present in summer were absent in winter (Georges Chaplain, 2015, p. 947). It is clear that the critical region under study is defined and more importantly the relation above falls in the critical region. With nt showing the number of turbines and dt representing the density of turbines square kilometers, the definition of a critical region and rejection of the the Nt uses percentiles of the null distribution instead of the theoretical distribution (Marko Tomic, 2014, p. 600). A simple correlation between seabirds and the turbine then realizes that for the critical region, there exists a set of differences that has No rejected and Nt accepted so as to allow for room for investigation of effect or non-effect of seaducks on the dam (Georges Chapalain, 2015, p. 943). In the study, a demonstration of the method through the use of the artificially improvised dataset aids in explain the various ecological and behavioral reasons for the patterns observed, and what these findings mean for the mode through which wind farms should be rendered designable (Hameed, 2010, p. 888) . On a proper scale, before the construction of the wind farm, the survey would cater for the design parameters such as temperature conditions, area of the farm which are two critical limitations affecting the performance of the survey.

On behavioral reasons, the study shows that the overall mean bird count on the study area shows that the feeding of the birds affect the number of the observations targeted. Most birds also migrate in most cases in search of food, mates and other requirements making it difficult in making real-time observations (MILLOY, S. J. ,2009, p. 1). Ecological conditions also have a hand in the availability of the seabirds as increased dam water temperatures show a likelihood of affecting the number and the frequency of observable birds at the dam. These effects can generally get explanation through the General Linear Model (GLM) and as a technique for modeling the data to simple mathematical equations, the explanation would consider and for testing (MILLOY, S. J., 2009, p. 1). In this case, the premise is whether there are statistically significant effects of one or more explanatory (x) variables on your response or dependent (y) variable. These variables point to collision fatalities that exist at the wind farm which the study presents as comprising: the high densities of birds and the increased frequencies of migration that happen around the wind farms (Georges Chaplain, 2015, p. 948). In addition to the above, the ecological factors such as wind farm operation and development arise with regard to increased human activity beyond optimal levels (Fernandez-Infantes, 2006). The noise and irregular motions from the turbines create disturbance that ultimately propel the displacement of birds from the dam site where observation is executed (MILLOY, S. J., 2009, p. 1). Another variable such as the choice of lighting, bright lighting attracts the seabirds to a large extent and consequently, the birds would become blinded leading to their collision with tall building giving rise to the fatality (Marko Tomic, 2014, p. 601). The bright light occurrence behavior therefore needs avoidance around the wind farms, or needs to be kept at minimal hence reduction of fatality cases.


Fernandez-Infantes, Alberto, Javier Contreras, and Jose L. Bernal-Agustin. "Design Of Grid Connected PV Systems Considering Electrical, Economical And Environmental Aspects: A Practical Case". Renewable Energy 31.13 (2006): 2042-2062. Web.

Guillou, Nicolas and Georges Chapalain. "Numerical Modelling Of Nearshore Wave Energy Resource In The Sea Of Iroise". Renewable Energy 83 (2015): 942-953. Web.

Hadzic, Neven, Hrvoje Kozmar, and Marko Tomic. "Offshore Renewable Energy In The Adriatic Sea With Respect To The Croatian 2020 Energy Strategy". Renewable and Sustainable Energy Reviews 40 (2014): 597-607. Web.

Hameed, Z., S.H. Ahn, and Y.M. Cho. "Practical Aspects Of A Condition Monitoring System For A Wind Turbine With Emphasis On Its Design, System Architecture, Testing And Installation". Renewable Energy 35.5 (2010): 879-894. Web.

KOELLER, J., KOEPPEL, J., & PETERS, W. (2006). Offshore wind energy: research on environmental impacts. New York, Springer.

MILLOY, S. J. (2009). Green hell: how environmentalists plan to control your life and what you can do to stop them. Washington, D.C., Regnery Pub.

Niknam, Taher and Bahman Bahmani Firouzi. "A Practical Algorithm For Distribution State Estimation Including Renewable Energy Sources". Renewable Energy 34.11 (2009): 2309-2316. Web.

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