The relationship between two categorical independent variables was evaluated using Chi-square test. Descriptive statistics for numeric variables was represented as mean ± standard deviation, and for categorical variables, as numbers and % values. SPSS Windows version 24.0 package software (SPSS Inc., Chicago, IL, USA) was used for statistical analysis and P < 0.05 was considered as statistically significant.
Spss windows version 24
Manufactured by IBM
Sourced in United States
SPSS Windows version 24.0 is a statistical software package designed for data analysis, visualization, and management. It provides a comprehensive set of tools for descriptive statistics, data exploration, modeling, and reporting. The software is widely used in various fields, including academic research, market research, and business analytics.
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Lab products found in correlation
22 protocols using spss windows version 24
1
Categorical Variables Relationship Analysis
2
Categorical Variable Relationship Analysis
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The relationship between two categorical independent variables was evaluated using Chi square test. Descriptive statistics for numeric variables was represented as mean, and for categorical variables, as numbers and % values. SPSS Windows version 24.0 package software (SPSS Inc.; Chicago, IL, U.S.A.) was used for statistical analysis.
3
Categorical Variable Analysis Protocol
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Descriptive statistics for categorical variables were represented as numbers and % values. The relationship between two categorical independent variables was evaluated using the chi-square test. SPSS Windows version 24.0 package software (SPSS Inc., Chicago, IL, USA) was used for statistical analysis and P < 0.05 was considered statistically significant.
4
Statistical Analysis of Experimental Data
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The SPSS Windows Version 24.0 package (SPSS, Chicago, IL, USA) was used for statistical analysis. The normality of the distribution of data was tested using the Shapiro–Wilk test. The Mann–Whitney U-test was used to compare non-normally distributed data between the two independent groups. In addition, the Kruskal–Wallis test and the all-pairwise multiple comparison test were used to compare numerical data among more than two independent groups. Relationships between numerical variables were assessed using Spearman’s correlation coefficient, while relationships between categorical variables were assessed using the Chi-square test. Descriptive statistics for numerical variables are expressed as means±standard deviations, while those for categorical variables are expressed as numbers and percentages p<0.05 was considered as statistically significant.
5
Survival Analysis of Prognostic Factors
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All statistical tests were performed using Statistical Package for Social Scientists (SPSS/Windows, Version 24.0, SPSS, Inc., Chicago, IL, USA). Included patients’ characteristics were described by categorical variables (frequency and percentage). Survival analysis was conducted, and Kaplan-Meier plot was used for calculating the survival curves. Univariable and multivariable Cox proportional hazard model were used to assess prognostic factors and calculate the survival hazard ratio (HR) with 95% confidence interval (95% CI) of PFS and OS. All prognosis-related factors were included in a multivariable Cox model, regardless of their significance level of the univariate analysis. A two-sided P value of <0.05 was considered statistically significant in all aforementioned statistical tests.
6
Nutrition Adequacy and Mortality in Critical Care
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SPSS Windows version 24.0 was used for statistical analysis. Data was presented as either mean ± standard deviation (SD), median; inter quartile range (IQR), or proportions and compared using t-test, Chi-square test, logistic regression, and log rank tests, respectively. Normality was checked using mean, median, mode.
For the analysis, all statistical tests were evaluated with a 2-tailed p-value, and p-values less than 0.05, a 95% confidence interval (CI) were deemed significant. All potential risk factors were compared between survivors and non-survivors by univariate analysis using Chi-square/Fisher's exact tests, and Student's t test/Mann Whitney U tests for continuous variables. Univariate and multivariate logistic regression analysis was performed with mortality outcome for all the patients. In the second part of the analysis, 14-day energy and protein adequacy were categorized as ≥80% and <80%, and their association with the mortality outcome for ward and ICU patients separately was determined. We also investigated nutrition delivery in a subclass of patients who received MV ≥ 24 h and during prone ventilation.
For the analysis, all statistical tests were evaluated with a 2-tailed p-value, and p-values less than 0.05, a 95% confidence interval (CI) were deemed significant. All potential risk factors were compared between survivors and non-survivors by univariate analysis using Chi-square/Fisher's exact tests, and Student's t test/Mann Whitney U tests for continuous variables. Univariate and multivariate logistic regression analysis was performed with mortality outcome for all the patients. In the second part of the analysis, 14-day energy and protein adequacy were categorized as ≥80% and <80%, and their association with the mortality outcome for ward and ICU patients separately was determined. We also investigated nutrition delivery in a subclass of patients who received MV ≥ 24 h and during prone ventilation.
7
Categorical Variable Relationship Analysis
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The relationships between two independent variables at the categorical measurement level
were tested with the Exact Chi-square test. Numbers and percentage values were given for
categorical variables as descriptive statistics. The SPSS Windows version 24.0 package
program was used for the statistical analysis and a P value of <0.05
was considered statistically significant.
were tested with the Exact Chi-square test. Numbers and percentage values were given for
categorical variables as descriptive statistics. The SPSS Windows version 24.0 package
program was used for the statistical analysis and a P value of <0.05
was considered statistically significant.
8
Data analysis and statistical methods
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The collected data were coded on a prearranged coding sheet and entered into EpiData Windows version 4.1 statistical programs. The data was cleaned accordingly and then exported to Statistical Package for the Social Sciences (SPSS) Windows version 24.0 for analysis. Descriptive analyses such as percentages, frequency distribution, and measures of central tendencies were used. Bivariable analysis was performed between a dependent variable and each of the independent variables. Then, all variables found to be significant at the bivariable level (p value < 0.25) were entered into a multivariable logistic regression model. p values of <0.05 and 95% confidence level were used to determine statistical significance.
9
Evaluating Diagnostic Test Performance
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The suitability of the data to normal distribution was tested using the Shaphiro Wilk test. Student t Test was used to compare the parameters with normal distribution, otherwise Mann-Whitney U test was used to compare the parameters without normal distribution. The categorical variables were analyzed by Pearson or Exact Chi-square test. In the study, age, gender, smoking, some clinical characteristics as well as laboratory, treatment and diagnostic results were analyzed firstly with the univariate logistic regression (LR) method, and then the variables found significant were analyzed with the stepwise multivariate LR method (Forward Wald test). By analyzing the significant variables obtained as a result of the last analysis, their performances were examined. Considering the regression co-efficients (β) of the significant variables obtained as a result of the final analysis, their optimal cut-off values were determined by the receiver operation characteristic (ROC) curve analysis. Descriptive statistics are given as mean ± standard deviation for numerical variables and number and % values for categorical variables. SPSS Windows version 24.0 package program was used for statistical analysis and P < 0.05 was considered statistically significant.
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The suitability of the data to normal distribution was tested using the Shaphiro Wilk test. Student t Test was used to compare the parameters with normal distribution, otherwise Mann-Whitney U test was used to compare the parameters without normal distribution. The categorical variables were analyzed by Pearson or Exact Chi-square test. In the study, age, gender, smoking, some clinical characteristics as well as laboratory, treatment and diagnostic results were analyzed firstly with the univariate logistic regression (LR) method, and then the variables found significant were analyzed with the stepwise multivariate LR method (Forward Wald test). By analyzing the significant variables obtained as a result of the last analysis, their performances were examined. Considering the regression co-efficients (β) of the significant variables obtained as a result of the final analysis, their optimal cut-off values were determined by the receiver operation characteristic (ROC) curve analysis. Descriptive statistics are given as mean ± standard deviation for numerical variables and number and % values for categorical variables. SPSS Windows version 24.0 package program was used for statistical analysis and P < 0.05 was considered statistically significant.
10
Statistical Analysis of Encoded Data
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The data collected was encoded and entered into a computer using Microsoft Excel 2016 version and exported to SPSS. All statistical analyses were performed using the SPSS Windows version 24 (IBM SPSS Statistics, New York, United States). Descriptive statistics such as frequency, percentages, mean, and standard deviation were calculated and presented in the tables.
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