All results are described as the mean ± s.d. The assay data were analyzed using Student’s t-tests. All differences were considered significant at the level of P<0.05. All statistical analyses were performed using SPSS for Windows release 22.0 (SPSS Inc., Chicago, IL, USA).
Spss for windows release 22
Manufactured by IBM
Sourced in United States
SPSS® for Windows® release 22.0 is a statistical software package developed by IBM. It provides tools for data analysis, data management, and data documentation. The software supports a wide range of analytical techniques, including descriptive statistics, bivariate statistics, predictions for numerical outcomes, and predictions for identifying groups.
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Lab products found in correlation
12 protocols using spss for windows release 22
1
Statistical Analysis of Assay Data
2
Statistical Analysis of Experimental Data
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Descriptive and inferential statistics were used to analyze the data. The mean ± SD indices were used in descriptive statistics. Afterwards, the data were checked for normality using the Kolmogorov–Smirnov test. Analysis of covariance was used to compare the variables’ improvements in both groups. The significant difference level was set at P < 0.05. All statistical analyses were performed using SPSS for Windows (Release 22.0; Chicago, IL, USA).
3
Factors Influencing E-Cigarette Usage
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Spearman rank correlation coefficients were computed to describe the correlation between current and ever e-cigarette use and each potential correlate of interest. We then undertook two multivariable analyses for each outcome The second model included all the policy variables except presence of an active monitoring system of tobacco consumption, which was viewed as a method to monitor changes due to the other six policies. The analyses of the first two models were performed using a stepwise approach with backwards elimination. A third model included variables retained in the last step of the backward elimination in the two previous analyses plus one indicator of use of conventional cigarettes and GDP per person if these were not retained in the last step of the backwards elimination. Finally, we produced a model for each outcome with five covariates.
Statistical significance was set at p \ 0.05. The analysis was conducted using SPSS for Windows, release 22.0
Statistical significance was set at p \ 0.05. The analysis was conducted using SPSS for Windows, release 22.0
4
Material Roughness Evaluation After Chewing
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Statistical analysis was performed using SPSS for Windows release 22 (SPSS Inc., Chicago, IL, USA). The data were assessed for normality using the Shapiro-Wilk test, and all parameters were shown to be normally distributed (p ≥ 0.094). The mean and standard deviation for each parameter were calculated. Paired sample t-test was performed to compare the mean value for each parameter before and after chewing simulation. Independent t-test was performed to assess differences in roughness parameters between the two materials. The significance level was set at p < 0.05.
5
SPSS Statistical Analysis of Health Questionnaire
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The statistical analysis was performed using SPSS for windows Release 22 (Chicago, IL). A p value of less than 0.05 was considered statistically significant. The sample size was in accordance with the principle of health status questionnaire study [27 (link)].
6
Multivariate Analysis of Co-Culture Compounds
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All assays were repeated at least three times and data were expressed as
mean ± standard deviation (SD). Data were analysed using the Statistical
Package for Social Sciences (SPSS®) for Windows® release 22.0 (IBM Corp.,
Armonk, NY, USA). Student’s t-test was used to evaluate
significance compared with controls and a P-value <0.05
was considered to indicate statistical significance.
Using ClustVis software, Principal Component Analysis (PCA) was used to model
the multivariate data sets. For PCA, data from all metal salts were used as
control and the difference between co-culture natural compounds and metal
salts were plotted.
mean ± standard deviation (SD). Data were analysed using the Statistical
Package for Social Sciences (SPSS®) for Windows® release 22.0 (IBM Corp.,
Armonk, NY, USA). Student’s t-test was used to evaluate
significance compared with controls and a P-value <0.05
was considered to indicate statistical significance.
Using ClustVis software, Principal Component Analysis (PCA) was used to model
the multivariate data sets. For PCA, data from all metal salts were used as
control and the difference between co-culture natural compounds and metal
salts were plotted.
7
Bone Turnover Markers in Osteoporosis
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All data in each group are presented as mean±standard deviation, and statistical analysis was performed using Statistical Package for the Social Sciences (SPSS) for Windows Release 22.0 (IBM, Armonk, NY, USA). Comparison of baseline characteristics between groups was assessed by Student t-tests. Comparisons of BTMs between osteoporosis and non-osteoporosis in each group were analyzed in the same manner. Analysis of variables between groups based on postmenopausal period was performed using ANOVA with p<0.05 considered statistically significant.
8
Statistical Analysis of Prognostic Factors
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Data were analysed using the Statistical Package for Social Sciences (SPSS®) for Windows® release 22.0 (IBM Corp., Armonk, NY, USA). All tests were two-sided and a P-value <0.05 was considered to indicate statistical significance. Quantitative data were expressed as median and interquartile range and were analysed using the Mann-Whitney test. Numerical data were expressed as a percentage and were analysed using the χ2 test. Odd ratios (ORs) and 95% confidence intervals (CIs) were calculated for all potential risk factors for poor prognosis. All variables were included in the univariate analysis and those with a P value of 0.05 were included in a logistic regression model for multivariate analysis.
9
Survival Analysis of AECOPD Patients
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Continuous data are presented as mean ± SD, and categorical data are presented as number (n) or percentage. Continuous variables were analyzed using the Mann–Whitney U-test, and categorical variables were analyzed by chi-square test for nonparametric variables. A Kaplan–Meier survival curve was used to analyze the effect of CAP on survival of critically ill AECOPD patients hospitalized in the RICU. A log-rank test was used to evaluate statistical differences in the survival curves. Cox’s proportional hazards regression model analysis was performed to assess risk factors for inhospital mortality for multivariate analysis. P<0.05 was considered statistically significant. The statistical analyses were performed using a software package (SPSS for Windows, release 22.0; IBM Corporation, Armonk, NY, USA).
10
PTEN RNA Expression Analysis
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Descriptive statistics were expressed as the mean ± sem. Statistical analyses were by one or two-way ANOVA or Student's t test using SPSS for Windows release 22.0 (IBM Corp, NY, USA) or Graphpad Prism software (v4.02; Graphpad CA 92037 USA). Scatterplots and bar charts were used for display of quantitative numerical or categorical data. PTEN RNA values were log transformed to provide a normal distribution.
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