Prior to testing, the Shapiro-Wilk test was used to assess the normality of distribution. The test confirmed that the data analyzed conformed to a normal distribution. Raw data were analyzed for outliers (mean ± 2.5 SD). Significant outliers were not included in the mean results and statistical analysis. Based on the results obtained, for each of the properties mentioned above, the following were calculated: mean values and the pooled standard error of the mean (Pooled SEM), which were tabulated and presented in the subsection Results. The experimental units were tissues collected from birds (blood sample, lymphatic organ), egg, as well as a cage with 10 birds for the data on laying performance, feed intake and feed conversion. The results were statistically analyzed by one-way ANOVA with diets as fixed effects. Significant differences for the means between the experimental groups were determined with Duncan's Multiple Range Test. The P < 0.05 was considered as a significant difference, and a value between P > 0.05 and P < 0.10 was considered as a trend toward significance. Statistical analyses were carried out using the Statistica software version 12.0 PL (StatSoft Inc., 2011, Tulsa, OK).
Statistica software version 12.0 pl
Manufactured by StatSoft
Sourced in United States
Statistica software version 12.0 PL is a comprehensive data analysis and statistical software package. It provides a wide range of tools for data management, visualization, and advanced statistical modeling. The software is designed to handle large and complex datasets, enabling users to gain insights and make informed decisions.
Automatically generated - may contain errors
Lab products found in correlation
5 protocols using statistica software version 12.0 pl
1
Avian Dietary Effects on Physiological Parameters
2
Dietary Patterns and Metabolic Syndrome
Check if the same lab product or an alternative is used in the 5 most similar protocols
+ Expand
All statistical analyses were conducted using the Statistica software version 12.0 PL (Statsoft Inc., USA). For each DP, study participants were divided into four groups (quartiles, Q), based on the factor scores of their diets. The socioeconomic factors and the components of MS were assessed in the group of subjects with and without MS. The percentage of subjects with abnormal values of FG, HDL-C, TG, BP and WC, as well as the percentage of individuals diagnosed with MS, were calculated in the lowest and the highest quartile of each DP. Differences between Q1 and Q4 were calculated using chi-squared test. In order to assess the risk of the MS and its components occurrence in the quartiles of identified DPs, logistic regression was applied. The values of the lowest Q were considered a reference level for each DP. Two models were created: first model based on the crude data, while second model was adjusted for potential confounders: age, sex, place of residence, education level, physical activity level, smoking status and total energy intake. The level of statistical significance for all analyses was set at α = 0.05.
3
Dietary Patterns and Egg Intake Relationship
Check if the same lab product or an alternative is used in the 5 most similar protocols
+ Expand
All statistical analyses were conducted using the Statistica software version 12.0 PL (Statsoft Inc., USA). Mean nutritional value was estimated in the diets of studied men and women. Based on the obtained factor scores, for each of the identified DPs study individuals were divided into quartiles or included into one of two following groups: below the median (
4
Dietary Patterns and Cardiometabolic Factors
Check if the same lab product or an alternative is used in the 5 most similar protocols
+ Expand
All statistical analyses were conducted using Statistica software version 12.0 PL (Statsoft Inc., USA). For each dietary pattern, the study participants were divided into 4 groups (quartiles), based on the factor scores of their diet. Mean values of age, cholesterol, triglycerides and fasting blood glucose level, blood pressure, waist circumference, WHR and BMI, as well as selected nutrients content in the diet, were calculated for each quartile of derived DPs. Due to the differences in the normal values of HDL-C, waist circumference and WHR, those variables were calculated separately for both male and female individuals. Selected nutrients intake was calculated per 1,000 kcal because higher energy intake is usually associated with the increased macronutrients content in the diet. Differences between quartiles of DPs were calculated using the Kruskal-Wallis test. The level of statistical significance for all analyses was set at α=0.05.
5
Dietary Glycemic Load and Nutrient Intake
Check if the same lab product or an alternative is used in the 5 most similar protocols
+ Expand
Relationship between the overall dietary GL and GL/1,000 kcal and nutrient content in the diets of the study group was assessed with Spearman's rank correlation. To examine differences in the overall dietary GL and GL/1,000 kcal according to demographic, socio-economic and lifestyle factors, analysis of covariance (ANCOVA) adjusted for potential confounding variables was used. Potential confounders were: gender, age, place of residence, body mass index (BMI) and energy intake (only in the case of overall dietary GL). Relationships between the quartiles of the dietary GL and GL/1,000 kcal and demographic, socioeconomic and lifestyle factors in the study population were assessed with the Chi-square test for trend. Statistical analysis were performed using Statistica software version 12.0 PL (Statsoft Inc., USA). The level of statistical significance was set at α=0.05.
About PubCompare
Our mission is to provide scientists with the largest repository of trustworthy protocols and intelligent analytical tools, thereby offering them extensive information to design robust protocols aimed at minimizing the risk of failures.
We believe that the most crucial aspect is to grant scientists access to a wide range of reliable sources and new useful tools that surpass human capabilities.
However, we trust in allowing scientists to determine how to construct their own protocols based on this information, as they are the experts in their field.
Ready to get started?
Sign up for free.
Registration takes 20 seconds.
Available from any computer
No download required
Revolutionizing how scientists
search and build protocols!