A statistical program (JMP, v. 11.0, SAS Institute Inc, Cary, NC) was used for all statistical analyses and data are presented as total range, median, and interquartile range (IQR). Echocardiographic and Doppler variables in dogs with (Class C) and without (Class B) CHF according to the ACVIM classification were compared. The non‐parametric Kruskal‐Wallis test was used for testing equality of medians. The 3 methods (RT3DE, SMOD, and ALM) were compared by linear regression, comparing the fitted curves to the equality lines, and Bland‐Altman plots, in which the mean bias and 95% confidence intervals were calculated. Level of significance was set at P < 0.05.
Jmp v11
Manufactured by SAS Institute
Sourced in United States, Japan
JMP v11 is a data analysis software application developed by SAS Institute. It provides a range of statistical and visualization tools for data exploration, modeling, and presentation.
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Prism 6, by GraphPad (2 mentions)
Sas v9, by SAS Institute (2 mentions)
For windows version 12, by MedCalc (1 mentions)
Facs fortessa, by BD (1 mentions)
Prism v6.0c, by GraphPad (1 mentions)
Genemarker software, by SoftGenetics (1 mentions)
Stata 13, by StataCorp (1 mentions)
Prism v5, by GraphPad (1 mentions)
Prism 8, by GraphPad (1 mentions)
Prism v6, by GraphPad (1 mentions)
Prism 5, by GraphPad (1 mentions)
Prism version 6, by GraphPad (1 mentions)
97 protocols using jmp v11
1
Comparing Echocardiographic Methods in Canine CHF
2
Preadipocyte Differentiation Gene Expression
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All data are shown as means ± SD. Student’s t-tests were used to compare results between two groups. When more than two groups were compared, a generalized linear model (GLM) procedure followed by the Turkey’s HSD test was used according to the model: Y = μ + T +e, where Y represents the gene expression level, μ represents the population mean, T represents the fixed effect of time points during preadipocyte differentiation, and e represents the random residual effect. JMP v11.0 (SAS Institute, Inc., Cary, NC, United States) was used for all analyses, and the threshold of significance was P < 0.05 or P < 0.01.
3
Statistical Analysis of Cell Responses
4
Quantifying Temporal Genetic Differentiation
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We determined the overall proportion of the variance attributable to differences in sampling dates for all 18 sampled points using the analysis of molecular variance (AMOVA) implemented in Arlequin v 3.5; first, we ran AMOVA with two groups containing all sites at generation zero pooled together to form one group and sites with multiple generations forming another. Then, we performed AMOVA with multiple groups by having each temporal sample (generation 0, 22, 27, 29, 35, 38, 48 and 52) forming a separate group.
Genetic differentiation between temporal samples from the same population was quantified by computing pairwise FST values in Arlequin v 3.5, and Jost’s DEST [40 (link)] using DEMEtics [41 (link)] in R [42 ]. It is well documented that high-allelic diversity markers like microsatellites can lead to underestimates of FST [40 (link), 43 , 44 (link)]. To account for this potential bias, we standardized FST values using the formulae developed by Hedricks [43 ], and used Jost's DEST. P-values and confidence intervals for Jost’s DEST were also calculated based on 1000 bootstrap resamplings. To test for correlation of differentiation indices and time since first sampling, we ran a linear regression of standardized FST and DEST against number of generations separating time points sampled in JMP® v11.0 (SAS Institute Inc., Cary, NC, USA, 1989–2007).
Genetic differentiation between temporal samples from the same population was quantified by computing pairwise FST values in Arlequin v 3.5, and Jost’s DEST [40 (link)] using DEMEtics [41 (link)] in R [42 ]. It is well documented that high-allelic diversity markers like microsatellites can lead to underestimates of FST [40 (link), 43 , 44 (link)]. To account for this potential bias, we standardized FST values using the formulae developed by Hedricks [43 ], and used Jost's DEST. P-values and confidence intervals for Jost’s DEST were also calculated based on 1000 bootstrap resamplings. To test for correlation of differentiation indices and time since first sampling, we ran a linear regression of standardized FST and DEST against number of generations separating time points sampled in JMP® v11.0 (SAS Institute Inc., Cary, NC, USA, 1989–2007).
5
Analyzing Manikin Depth Relationship
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All data were analyzed by JMP, v. 11.0 (the SAS Institute Inc.). Median and interquartile range were calculated for continuous or ordinal variables and compared by Wilcoxon signed-rank test or Kruskal-Wallis test. The total number and percentage were calculated for categorical variables and compared by Pearson’s chi-squared test. The relationship between the manikin and depth average was evaluated by scatterplot and calculating linear regression. Statistical analysis used a two-tailed hypothesis test, and the level of significance for decision-making was set at α = .05.
6
Chemoresponse Behavior Analysis Protocol
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Data for all chemoresponse behaviors were summed into a single dependent measure representing the amount of time the subject spent with its behavior directed toward the scent stimulus, which included responses to both urine and body odors combined into a single measure; we labeled this composite measure “time spent investigating.” Due to the small sample size and large effects of statistical outliers, we conducted a visual assessment of the data and removed a single outlier. By eliminating this variance-increasing outlier and log-transforming the data, we were able to ensure that the assumptions of normality (Shapiro–Wilk’s test; Sokal and Rohlf 2012 ) and homoskedasticity of variance (Hartley’s Fmax test; Sokal and Rohlf 2012 ) were met. We fitted a linear mixed-effect model using treatment (kin vs. nonkin) and scent type (urine vs. body odor) as fixed effects, and subject identity and subject age class (adult or subadult) as random effects. We used the Satterthwaite’s correction to adjust denominator degrees of freedom alpha was set to 0.05 for all tests, and analyses were conducted using JMP v. 11.0 (SAS Institute Inc., Cary, NC, USA).
7
Statistical Analysis of OCR Data
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All statistics were performed using JMP v11.0 software (SAS Institute). All OCR data was initially assessed with a one or two way ANOVA. When appropriate, post-hoc analysis was carried out using a Student’s t-test.
8
Regulation of ALKBH5 Expression in Adipogenesis
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All experiments were repeated thrice. All data are shown as the mean±SD. Student’s
t-test was used to compare results between two groups. When more than two groups were compared, a generalized linear model (GLM) procedure followed by Turkey’s HSD test was used according to the model:
Y=
μ+
F+
e, in which
Y is the dependent variable (
ALKBH5 mRNA expression level),
μ is the population mean,
F is various factors (time point of proliferation or differentiation of preadipocyte or broiler age) as the fixed effect, and
e is the random residual effect. JMP v11.0 (SAS Institute, Inc., Cary, USA) was used for all analyses, and the threshold of significance was set at
P<0.05.
t-test was used to compare results between two groups. When more than two groups were compared, a generalized linear model (GLM) procedure followed by Turkey’s HSD test was used according to the model:
Y=
μ+
F+
e, in which
Y is the dependent variable (
ALKBH5 mRNA expression level),
μ is the population mean,
F is various factors (time point of proliferation or differentiation of preadipocyte or broiler age) as the fixed effect, and
e is the random residual effect. JMP v11.0 (SAS Institute, Inc., Cary, USA) was used for all analyses, and the threshold of significance was set at
P<0.05.
9
Survival Analysis of NTMPD Pneumothorax
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Statistical analyses were conducted by using JMP v11.0 (SAS Institute Japan Ltd, Tokyo, Japan). Data are presented as median (interquartile range [IQR] or range), or number (%). Univariate analysis was performed by using Fisher's exact test to compare categorical variables and the Mann−Whitney test (between two groups) or the Kruskal−Wallis test (among three groups) to compare continuous variables. All P values were two-tailed; P <0.05 was considered significant.
We estimated survival rates and the rate of pneumothorax recurrence by using the Kaplan−Meier method. To identify the factors related to survival in pneumothorax associated with NTMPD, we first performed univariate Cox regression analysis. Then, multivariate Cox regression analysis was performed using variables strongly associated with (P <0.2) mortality in univariate analysis, in addition to age, sex, BMI, and CCI. CT findings were not included, as only 47 patients in the study had undergone CT.
We estimated survival rates and the rate of pneumothorax recurrence by using the Kaplan−Meier method. To identify the factors related to survival in pneumothorax associated with NTMPD, we first performed univariate Cox regression analysis. Then, multivariate Cox regression analysis was performed using variables strongly associated with (P <0.2) mortality in univariate analysis, in addition to age, sex, BMI, and CCI. CT findings were not included, as only 47 patients in the study had undergone CT.
10
Prostate Cancer Risk Assessment Protocol
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