Utilization of Power Analysis in Horticulture

The aim of this study was to determine associations and the values of power analysis as their reliability degrees between Year or Cultivars and traits such as fruit weight (FW), total acid (TA) and, the soluble substance that can be dissolved in water (SSDW) from various ten raspberry cultivars in an adaptation study regarding horticulture field by using Chi-Square and Likelihood Ratio Chi-Square statistics after FW, TA and SSDW were categorized as binary (low and high). Association between FW and CULTIVAR, association between SSDW and YEAR, association between SSDW and CULTIVAR, association between TA and CULTIVAR were much more significant (P<0.001). Besides, corresponding power values for Chi-Square and Likelihood Ratio Chi-Square statistics were very close on each other and had a reliability of approximately 100% and enough sample size. Contrary to these four contingency tables, associations between both FW-YEAR and TA-YEAR were non-significant and non-reliable because corresponding power values for Chi-Square and Likelihood Ratio Chi-Square statistic were 50-51% (a power of moderate-level) and 22-23% (power of low level), respectively and sufficient sample sizes for both FW-YEAR and TA-YEAR should be 240 and 560, respectively in order to provide a power of 80%. As a result, in order to be obtained reliable results and determined enough sample size in Chi-Square and Likelihood Ratio Chi-Square Statistics, power analysis should be performed

Comparison of Some Raspberry Cultivars' Herbal Features by Repeated Random Complete Design Statistic Technique.

The aim of this study was comparatively to examine herbal traits of the cultivars such as Rubin, Summit, Holland Dwarf, Heritage, Tulameen, Aksu Red, Nuburg, Canby and Willamette red raspberries cultivated at Ankara Condition, in the capital of Turkey between 2002 and 2005. According to Repeated Random Complete Design (RRCD) (which was composed of four random plot design experiments) used in the experiment, the effects of cultivar, year and cultivar by year interaction on herbal traits such as the height of shoot, diameter of shoot, number of shoot, fruitfulness of shoot and weight of fruit were further more significant (p < 0.0001). Besides, determination coefficients of RRCD for traits ranged from 95.60 to 99.94% (very-high). As a result, we concluded in Ankara condition that as to herbal traits such as the height of shoot, diameter of shoot, number of shoot, fruitfulness of shoot and weight of fruit, Willamette cultivar were more superior to others. In addition, we can suggest that researchers should analyze using RRCD because Determination Coefficients of RRCD for all traits were much more found

Phytochemical profiles and antioxidant activity of some grape accessions (<i>Vitis</i> spp.) native to Eastern Anatolia of Turkey

Phytochemical profiles and antioxidant activity of four historical grape accessions (‘Kuzu Kuyrugu’, ‘Miskali’, ‘Erkek miskali’, and ‘Kirmizi Kismisi’) grown in Igdir province located in Eastern Anatolia Region of Turkey were examined. Levels of vitamin C, organic acids (citric acid, tartaric acid, malic acid, succinic acid, fumaric acid), sugars (fructose, glucose), phenolic acids (catechin, rutin, quercetin, chlorogenic  acid, ferulic acid, o-coumaric acid, p-coumaric acid, caffeic acid, syringic acid, vanillic acid, and gallic acid), and antioxidant capacity (Trolox Equivalent Antioxidant capacity, TEAC assay) were determined. Accession was found to be important source of variation for all the parameters identified above (P<0.01). Among the grape accessions analyzed, ‘Kuzu Kuyrugu’ had the predominant vitamin C (47.19 mg/100 g), chlorogenic acid (2.687 mg/L), ferulic acid (1.303 mg/L), o-coumaric acid (1.317 mg/L), and syringic acid content (1.687 mg/L). The highest citric acid (55.360 mg/L), fructose (10.36 g/100g), glucose (11.51 g/100g), and catechin (1.353 mg/L) were recorded in ‘Miskali’ genotype. ‘Kirmizi Kismisi’ was determined to be the accession with the highest tartaric acid (21.29 mg/L), succinic acid (0.94 mg/L), and caffeic acid (2.137 mg/L) levels. ‘Erkek Miskali’ accession produced the paramount contents for fumaric acid (0.42 mg/L), rutin (2.477 mg/L), quercetin (0.447 mg/L), and vanillic acid (0.313 mg/L). The investigated grape genotypes showed notable levels of sugars, organic acids and phenolic compounds. These accessions could be valuable in breeding programs for improving grape quality and nutrition, as well as enhancing commercial worth and production of the grapes in Igdir province of Turkey.

Using of Logistic Regression in Animal Science

This study was carried out to examine the effects of environmental factors on different growth with Chi-square, G-test and logistic regression analysis after body weights of these growth periods were categorized as binary. Besides, logistic regression was also based on concordant statistics except Chi-square and G-tests as model fit criteria. With respect to three fit criteria, the relationships among categorized birth weight with categorized body weights in 45th, 60th and 75th days were significant (p<0.01). Moreover, the relationship between periods of the lambs born in early March of 2001 year by using logistic regression. The relationships between categorized body weights and categorized birth weight and/or environmental factors were analyzed sex and only categorized body weight in 75th day was significant (p<0.05). It could be said that birth weight, one of environmental factors, improved model fit more than the other factors when considered all model fit criteria in logistic regression.As a result, it can be suggested that in addition to Chi-square and G-tests used for providing relationship between two traits, logistic regression in terms of obtaining from different information will be an alternative analysis in place of variance analysis

Examination of Transformationist Leadership in Turkish Army

The aim of this study is to examine “Transformational leadership” conception with respect to qualifications and opinions of military officers and military administrators working at military base and organization by means of Factor Analysis. The study contained the military officers working at military base and organization in cities of Van, Hakkari, Bitlis, Siirt, Ağrı, and Muşin East Anatolian. The sampling regarding study material was comprised of 70 military officers (5 femaleand 65 male) and administratorswere at random selected. Data were collected from those people by Questionnaire as to Scale of Podsakoff’s Transformational Leadership containing 37 items. For Transformationist Leadership, Statisticalanalysis for data was performed by Factor Analysis after designing 6 artificial variables from 37 items: i-Providing vision and inspiration, ii-Forming conduct models, iii-Commitment to group goals, iv-Providing individual support, v- Intellectual stimulation, vi-Intellectual stimulation. Artificial 6 variables were separated into Factor 1 and Factor 2. While Factor 1 consists of Providing vision and inspiration; Formingconduct models; Commitment to group goals and Intellectual stimulation, Factor 2 consists of Providing individual support; Intellectual stimulation. A variance (eigenvalue) of 2.523 for the first factor which explains %42.1 of total variation, while second factor’s eigenvalue,1.697. The second factor explains %28.2 of the total variation. These two factors together explain %70.3 of the total variation. Besides, RMSR value calculated for this study is small enough (0.094), it is possible to conclude that sufficient factor analyses have been made. As a result, it can be suggest that transformational leadership based on educatio

Determination of the Best Growth Curve and Measurement Interval in Norduz Male Lambs

The aim of this study, was to determine the best non-linear model and measure interval (biweekly or monthly) in explaining the body weight-age relationship in Norduz male lambs born in 2004. For this aim, Brody, Logistic, Gompertz and Richards non-linear models were fitted to the average body weight-age data with 15 (biweekly) days and 30 (monthly) days of measure intervals. Although, Logistic model become equal to Richards model (99.8%) for two intervals, Logistic model had lower RMSE than Richards model. Therefore, the best non-linear model for 2 intervals was Logistic model having the highest coefficient of determination (R2) but the lowest Root of Means Square Error (RMSE). Contrary to Brody non-linear model, the usage of 30 days of measure intervals performed positive effect on Logistic, Gompertz and Richards non-linear models instead of 15 days of measure interval. As a result, it was concluded that the best non-linear model for Norduz male lambs was Logistic model and the appropriate measure interval for Norduz male lambs was monthly interval

Usage of Penalized Maximum Likelihood Estimation Method in Medical Research: An Alternative to Maximum Likelihood Estimation Method

&lt;p style="text-align: justify"&gt;&lt;span&gt;The present paper was to reduce biased estimation using new approach (Penalized Maximum Likelihood Estimation Method) in Logistic Regression. It was assumed in the present paper that four various data sets on coronary heart disease (CHD) and smoking (including separation case) were obtained. Maximum Likelihood Estimation and Penalized Maximum Likelihood Estimation Methods were applied and compared for&lt;strong&gt; &lt;/strong&gt;separation case including biased estimation in Logistic Regression when one of the cells in 2 x 2 contingency tables becomes equal to zero (separation problem).The values of parameters&lt;span style="position: relative; top: 6pt"&gt;&lt;!--[if gte vml 1]&gt;&lt;v:shapetype id="_x0000_t75" coordsize="21600,21600" o:spt="75" o:preferrelative="t" path="m@4@5l@4@11@9@11@9@5xe" filled="f" stroked="f"&gt; &lt;v:stroke joinstyle="miter"/&gt; &lt;v:formulas&gt; &lt;v:f eqn="if lineDrawn pixelLineWidth 0"/&gt; &lt;v:f eqn="sum @0 1 0"/&gt; &lt;v:f eqn="sum 0 0 @1"/&gt; &lt;v:f eqn="prod @2 1 2"/&gt; &lt;v:f eqn="prod @3 21600 pixelWidth"/&gt; &lt;v:f eqn="prod @3 21600 pixelHeight"/&gt; &lt;v:f eqn="sum @0 0 1"/&gt; &lt;v:f eqn="prod @6 1 2"/&gt; &lt;v:f eqn="prod @7 21600 pixelWidth"/&gt; &lt;v:f eqn="sum @8 21600 0"/&gt; &lt;v:f eqn="prod @7 21600 pixelHeight"/&gt; &lt;v:f eqn="sum @10 21600 0"/&gt; &lt;/v:formulas&gt; &lt;v:path o:extrusionok="f" gradientshapeok="t" o:connecttype="rect"/&gt; &lt;o:lock v:ext="edit" aspectratio="t"/&gt; &lt;/v:shapetype&gt;&lt;v:shape id="_x0000_i1025" type="#_x0000_t75" style='width:12.75pt; height:18pt' o:ole=""&gt; &lt;v:imagedata src="file:///C:DOCUME~1ADMINI~1LOCALS~1Tempmsohtml11clip_image001.wmz" o:title=""/&gt; &lt;/v:shape&gt;&lt;![endif]--&gt;&lt;!--[if !vml]--&gt;&lt;img src="file:///C:/DOCUME%7E1/ADMINI%7E1/LOCALS%7E1/Temp/msohtml1/01/clip_image002.gif" alt="" width="17" height="24" /&gt;&lt;!--[endif]--&gt;&lt;/span&gt;&lt;!--[if gte mso 9]&gt;&lt;xml&gt; &lt;o:OLEObject Type="Embed" ProgID="Equation.DSMT4" ShapeID="_x0000_i1025" DrawAspect="Content" ObjectID="_1280690478"&gt; &lt;/o:OLEObject&gt; &lt;/xml&gt;&lt;![endif]--&gt;&lt;span&gt;&amp;nbsp;&lt;/span&gt;and their standard error obtained by using Maximum Likelihood estimation for four data sets were found approximately: 12.56&amp;plusmn;257.8, 13.46&amp;plusmn;264.3, 13.42&amp;plusmn;210.3, and 13.41&amp;plusmn;180.4, respectively, meaning that Maximum likelihood Estimations are biased estimates. However, corresponding values for Penalized Maximum Likelihood Estimation Method were found 2.28 &amp;plusmn; 1.81, 3.05 &amp;plusmn; 1.59, 3.45&amp;plusmn; 1.53, and 3.45 &amp;plusmn; 1.53, respectively, meaning that Penalized Maximum likelihood Estimations was unbiased estimates. For example, it is clear that standard error value for data set 1 reduced from 257.8 to 1.81 when using Penalized Maximum Likelihood Estimation Method for separation problem. According to the original approach, the odds of being coronary heart disease (CHD) risk for smoking were increased 21.08 times than that for no smoking in data set 2, which is statistically significant at 1% level. The odds of being coronary heart disease (CHD) risk for smoking were increased 31.63 times than that for no smoking in data set 3 (P &lt; 0.001). The odds of being coronary heart disease (CHD) risk for smoking were increased 41.93 times than that for no smoking in data set 4. &lt;/span&gt;&lt;/p&gt; &lt;p style="text-align: justify"&gt;&lt;span&gt;When one of the cells in 2 x 2 contingency tables becomes equal to zero (separation problem), Penalized Maximum Likelihood Estimation Method was more superior to Maximum Likelihood Estimation Method because Penalized Maximum Likelihood Estimation Method may be performed unbiased (reliable) estimation.&lt;/span&gt;&lt;/p&gt

COMPARISON OF PREDICTIVE PERFORMANCES OF MARS AND CART ALGORITHMS THROUGH R SOFTWARE

Within the framework of general linear model, there is lack of information on comparatively examining data mining algorithms viz. CART, CHAID, C5.0, Exhaustive CHAID, MLP, RBF and particularly MARS, which derives a convenient prediction equation. All of the algorithms can be more informative than a classical method like multiple linear regressions in the violation of some distributional assumptions in relation to variables to be studied. The aims of the current investigation were to comparatively examine MARS and CART algorithms and multiple linear regressions through R free software in terms of general linear model and to present how to step-by-step use R software in the application of these statistical approaches. MARS data mining algorithm also used as an alternative to response surface method in optimization process has been examined in detail in generalized cross validation for the first time. In the R software, “penalty = -1” and “a backward pruning method” were specified for MARS. Thus, GCV is converted into RSS/n where RSS is residual sum of squares and n is sample size. Model evaluation criteria estimated to compare these three approaches were R2, R2ADJUSTED, SDRATIO and Pearson correlation between predicted and actual dependent values. As a result, the current investigation will be a noble reference for researchers who will perform similar studies in next time

Usage of Penalized Maximum Likelihood Estimation Method in Medical Research: An Alternative to Maximum Likelihood Estimation Method

Abstract The paper was to reduce biased estimation using new approach (Penalized Maximum Likelihood Estimation (PMLE) Method) in Logistic Regression. For this aim, unreal four small data sets were randomly generated. Maximum Likelihood Estimation (MLE) and PMLE Methods were applied and compared for separation case including biased estimation in Logistic Regression when one of the cells in 2 x 2 tables becomes equal to zero (separation problem). Parameters 1 and their standard error obtained by using MLE for four data sets were 12.56 ± 257.8, 13.46 ± 264.3, 13.42±210.3, and 13.41 ± 180.4, respectively, meaning that MLE&apos;s are biased estimates. Corresponding values for PMLE method were found 2.28 ± 1.81, 3.05 ± 1.59, 3.45 ± 1.53, and 3.45 ± 1.53, respectively, meaning that PMLE&apos;s was unbiased estimates. It is clear that standard error value for data set 1 reduced from 257.8 to 1.81 when using PMLE method for separation problem. According to PMLE Method, the odds of being coronary heart disease risk for smokers were increased 21.08 times than that for non-smokers smoking in data set 2, which is significant at 1% level. The odds of being coronary heart disease risk for smokers were increased 31.63 times than that for non-smokers in data set 3 (P &lt; 0.001). The odds of being coronary heart disease risk for smokers were increased 41.93 times than that for non-smokers in data set 4. When one of the cells in 2 x 2 contingency tables becomes equal to zero, PMLE was more superior to MLE Method because PMLE Method may be performed unbiased (reliable) estimation

The Method of Leakage Policy Development

Abstract: Drinking, using and irrigating water are delivered to the subscription using a transmission system after going out of the source. Water is delivered to subscription using main pipes-storage, networks, house connections, housing installation complex and sort of pipes, values, suckers and professional meters. This way sometimes covers hundreds or even thousands km. Water known as flowing liquid, is not enough to reach its place physically. It is necessary for water to reach its place in a safety healthy and sufficient way. It is impossible to reduce leakage water into lowest level for system of supplying water. The only thing to be done for leakage water is to keep up it at the same level. There is no way to reduce the leakage water under the level of 5% even on the well organized and well managed transmission system. Today; to keep the leakage water at the level of 15 % is a big success. Many cities, towns and countries are delivered to water taken off sources, to it is subscription after loosing. It is 30 % and 65 % on the way. The water reached to it is subscription is sometimes inefficient or unhealthy for people. It is necessary to choose and practice rational method in order to find out the level of leakage water