Statistics toolbox

Manufactured by MathWorks

Sourced in United States

The Statistics Toolbox is a MATLAB® toolbox that provides a comprehensive set of functions for statistical analysis, data visualization, and modeling. It includes tools for descriptive statistics, hypothesis testing, regression analysis, time series analysis, and more. The toolbox is designed to help users analyze and interpret data efficiently within the MATLAB environment.

Automatically generated - may contain errors

Lab products found in correlation

Matlab, by MathWorks (30 mentions) Matlab 7, by MathWorks (4 mentions) Prism 7, by GraphPad (2 mentions) Prism 4, by GraphPad (2 mentions) Matlab r2009a, by MathWorks (2 mentions) Matlab 2013b, by MathWorks (2 mentions) Simca p 13, by Sartorius (1 mentions) Version 14, by MedCalc (1 mentions) Prism 5, by GraphPad (1 mentions) Matlab r2015a, by MathWorks (1 mentions) Matlab 9.0 2016a, by MathWorks (1 mentions) Bioinformatics toolbox, by MathWorks (1 mentions) Masslynx v4, by Waters Corporation (1 mentions) Pls toolbox, by Eigenvector Research (1 mentions) F14201, by Thermo Fisher Scientific (1 mentions) Myopacer field stimulator, by IonOptix (1 mentions) Matlab 2014, by MathWorks (1 mentions) Sigmaplot, by Grafiti LLC (1 mentions) Anovan, by MathWorks (1 mentions) Spss v25, by IBM (1 mentions)

64 protocols using statistics toolbox

Continuous Cardiac Monitoring Protocol

Check if the same lab product or an alternative is used in the 5 most similar protocols

HR was continuously measured at 1 s intervals using a V800 Polar system with a heart rate sensor worn around the chest (Polar, Kempele, Finland). HR measurements were started upon the participant’s arrival at the hospital and ended after the participants finished their workday. Minute averages were calculated using a customized MATLAB and Statistics Toolbox software package (2012b, The MathWorks, Inc., Natick, USA).

de Korte J.Q., Bongers C.C., Catoire M., Kingma B.R, & Eijsvogels T.M. (2021). Cooling vests alleviate perceptual heat strain perceived by COVID-19 nurses. Temperature: Multidisciplinary Biomedical Journal, 9(1), 103-113.

+ Open protocol

+ Expand

Multivariate Analysis of Glioma Imaging

Check if the same lab product or an alternative is used in the 5 most similar protocols

Statistical analyses were conducted in Matlab using the statistics toolbox (The MathWorks Inc., MA). Demographic variables were compared between groups (Chi-square or two-tailed independent samples t-test) for descriptive purposes. The numbers of selected ICs from different groups (i.e., high grade or low grade gliomas) were compared using independent sample t-test with a threshold of p < 0.05 which was considered as statistically significant. In the meantime, the correlation coefficient value was calculated between the coefficient of variation of pixel intensity and IC numbers with a threshold of p < 0.05. In addition, one-factor ANOVA analysis was applied to test the relationship between coefficient of variation of pixel intensity and the tumor grade in the cases of p < 0.05. Finally, independent sample t-test was conducted again for the group comparison of ΔrCBV in the cases of p < 0.05.

Ji B., Wang S., Liu Z., Weinberg B.D., Yang X., Liu T., Wang L, & Mao H. (2019). Revealing hemodynamic heterogeneity of gliomas based on signal profile features of dynamic susceptibility contrast-enhanced MRI. NeuroImage : Clinical, 23, 101864.

+ Open protocol

+ Expand

Comprehensive Statistical Analysis Protocol

Check if the same lab product or an alternative is used in the 5 most similar protocols

Statistical analysis was carried out using the Statistics toolbox in MATLAB (Mathworks, MA). Data sets were analyzed using one- or two-way analysis of variance as described in figure legends. p-values less than 0.05 were considered statistically significant. All values were reported as mean ± standard error unless otherwise noted.

Chen Z., Wholey W.Y., Najafabadi A.H., Moon J.J., Grigorova I., Chackerian B, & Cheng W. (2019). Self Antigens Displayed on Liposomal Nanoparticles above a Threshold of Epitope Density Elicit Class-switched Autoreactive Antibodies Independent of T-cell Help. Journal of immunology (Baltimore, Md. : 1950), 204(2), 335-347.

+ Open protocol

+ Expand

Quantitative Analysis of Cell Behavior

Check if the same lab product or an alternative is used in the 5 most similar protocols

All data analyses including statistical tests were performed using self-written scripts and built-in functions in MATLAB (release 2017b) including the Curve Fitting and the Statistics Toolbox (MathWorks, Inc.). The group comparisons were done using two-unpaired t-tests (ttest2): Figures 2C, 2D, 3C, and 3D as well as paired t-tests (ttest): Figures 4B and 4D. The significance level of statistical tests was denoted as n.s. for p-value > 0.05, ∗ for p < 0.05, ∗∗ for p < 0.01, ∗∗∗ for p < 0.001 and ∗∗∗∗ for p < 0.0001. More statistical details of experiments can be found in the figure legends.

Wang H., Li B., Lu Y., Han K., Sheng H., Zhou J., Qi Y., Wang X., Huang Z., Song L, & Hua Y. (2021). Real-time threshold determination of auditory brainstem responses by cross-correlation analysis. iScience, 24(11), 103285.

+ Open protocol

+ Expand

Lognormal Distribution Fitting and Cell Length Variability

Check if the same lab product or an alternative is used in the 5 most similar protocols

Cell frequency distributions were normalized by the sum of the area under the curve, fit to a lognormal distribution to obtain lognormal mean (µ) and variance (v) using fitdist, lognpdf and lognstat functions using Matlab with the Statistics Toolbox (MathWorks Inc.). The Kolmogorov–Smirnov (KS) test statistic was calculated for the number of bins (n = 44) and significance level (α) of 0.01 to arrive at a test statistic (D_{(α, n)}) given by [45 ,46 ]

D_{(α, n)} = \frac{\sqrt{- \ln (α / 2)}}{2 n} .

The cumulative distribution function (CDF) of observed and fit data was calculated for each bin (i) from the length–frequency distribution. The difference

| d_{i} | = | F_{i} - {\hat{F}}_{i} |

between the observed (F_i) and expected (

{\hat{F}}_{i}

) values of the CDF was evaluated, and the maximum (d_max) was found. The hypothesis that the fit to the data was good was accepted if d_max < D(α,n). Variability in cell lengths was quantified by the coefficient of variation (CV_L) using the expression CV_L = σ_L/μ_L, where σ_L and μ_L are the standard deviation and mean of cell lengths, respectively.

Gangan M.S, & Athale C.A. (2017). Threshold effect of growth rate on population variability of Escherichia coli cell lengths. Royal Society Open Science, 4(2), 160417.

+ Open protocol

+ Expand

Left Atrial Wall Segmentation and Intensity Analysis

Cited 2 times

Check if the same lab product or an alternative is used in the 5 most similar protocols

Left atrial segmentations were performed manually by trained raters who processed the LGE-MRIs using Seg3D software (Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT) as described previously ^{2 (link), 3 (link)}. The segmentation was edited manually to cover the whole wall in each slice in different areas accounting for atrial wall with varying thickness. The two different scans for each patient were segmented by different raters, each with more than 2 years of experience. All the segmentations were checked at the end by a more experienced rater with more than 7 years of experience to make sure that segmentations were consistent and accurate. The left atrial wall was later masked out on the MRIs for further analysis (Figure 1A-B in supplementary material show the process).
In theory, the intensity histogram of homogenous tissue in an MRI with some noise can be modeled with a Rician distribution ^{13 (link)}. Using MATLAB and Statistics Toolbox (Release 2016a, The MathWorks, Inc., Natick, Massachusetts, United States), we removed the outliers from the wall intensity histograms (lowest and highest 1 percentile) and normalized the intensity values to be in the range of 0 to 1, fitted Rician distributions on the intensity histograms and calculated mean intensity value, skewness and standard deviation (SD) of the distributions.

Kamali R., Schroeder J., DiBella E., Steinberg B., Han F., Dosdall D.J., Macleod R.S, & Ranjan R. (2020). Reproducibility of Clinical Late Gadolinium Enhancement Magnetic Resonance Imaging in Detecting Left Atrial Scar after Atrial Fibrillation Ablation. Journal of cardiovascular electrophysiology, 31(11), 2824-2832.

+ Open protocol

+ Expand

Investigating Neuronal Mechanisms of Behavior

Check if the same lab product or an alternative is used in the 5 most similar protocols

Statistics for the wild-type and KO mice learning data were performed on the basis of values for each mouse per day. One-way and two-way repeated measures ANOVA were used to investigate general main effects; and paired or unpaired t-test were used in all planned and post-hoc comparisons. Z-test was used for the comparison of neuron proportions (Sheskin, 2003 ). Statistics for the optogenetic data were performed on the basis of control and stimulated values for each mouse per stimulation condition. Statistical analyses were conducted in Matlab using the statistics toolbox (The MathWorks Inc., MA) and GraphPad Prism 7 (GraphPad Software Inc., CA). Results are presented as mean ± SEM for behavior readouts and the neuronal recording data.

p < 0.05

was considered significant. All statistical details are located within the figure legends. The number of animals used in each experiment and the number of neurons are specified in the text and figure legend.

Li H, & Jin X. (2023). Multiple dynamic interactions from basal ganglia direct and indirect pathways mediate action selection. bioRxiv.

+ Open protocol

+ Expand

Metabolic Profiling of Milk Samples

Check if the same lab product or an alternative is used in the 5 most similar protocols

NMR spectra of milk samples were aligned using Icoshift by co-shifting of the whole spectra according to the anomeric lactose proton at 5.23 ppm [31 (link)]. The proton NMR spectra were subdivided into 0.01 ppm bins, reducing each spectrum into 957 separate variables in the regions 10.00–5.00 and 4.72–0.5 ppm. Principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were performed in order to identify differences in the metabolite profiles. The data was mean-centred and Pareto-scaled prior to analysis. The OPLS-DA model was cross-validated using segmentation with seven splits. Covariance was investigated by analysis of OPLS-DA regression coefficients back-transformed to original data and colour coded by the loading weights [32 (link)]. The multivariate data analysis was performed using SIMCA-P + 13 (Umetrics AB, Umeå, Sweden). Alignment by Icoshift, binning, and analysis of OPLS-DA plots were performed in MATLAB 7.13 using in-house developed scripts (MathWorks Inc., Natick, MA, USA). Univariate statistical significance was evaluated by Student’s t-test using the Statistics Toolbox in MATLAB 7.13 (MathWorks Inc., Natick, MA, USA).

Sundekilde U.K., Downey E., O’Mahony J.A., O’Shea C.A., Ryan C.A., Kelly A.L, & Bertram H.C. (2016). The Effect of Gestational and Lactational Age on the Human Milk Metabolome. Nutrients, 8(5), 304.

+ Open protocol

+ Expand

Stiffness Perception Psychometric Analysis

Check if the same lab product or an alternative is used in the 5 most similar protocols

Data analysis was performed using the Statistics Toolbox in Matlab (R2016b, MathWorks, Natick, MA, USA). For each stiffness variation, the success rate was evaluated across the population of participants, together with the 95% confidence interval (binofit test) of the rates of identification of stimuli with increasing stiffness (normalized Δstiffness > 0). A logistic fit of the resulting psychometric curves was computed for the presented stiffness variations using the Matlab nlinfit function, using a custom fitting cumulative distribution function. The significance of participants’ responses for each normalized stiffness variation (normalized Δstiffness) was computed using the Matlab binofit test.

Sorgini F., Massari L., D’Abbraccio J., Palermo E., Menciassi A., Petrovic P.B., Mazzoni A., Carrozza M.C., Newell F.N, & Oddo C.M. (2018). Neuromorphic Vibrotactile Stimulation of Fingertips for Encoding Object Stiffness in Telepresence Sensory Substitution and Augmentation Applications. Sensors (Basel, Switzerland), 18(1), 261.

+ Open protocol

+ Expand

Mesenchymal Gene Signature in Gastric Cancer

Check if the same lab product or an alternative is used in the 5 most similar protocols

The primary endpoint of the present study was the identification and validation of a mesenchymal gene signature in GC. The secondary endpoint was survival, defined as the time between the date of surgery and the date of death. Survival data were updated at the time of analyses (May 2016), and analyzed using a Cox regression model. Baseline characteristics were compared using chi-square or Fisher’s exact test. We used Spearman correlation for pairwise correlations between continuous variables. The significance levels were set at alpha=0.05. All analyses were performed using either the Matlab package including the Statistics toolbox (Mathworks, Natick, MA, USA) or R for Windows, v2.15 (R Core Team, Vienna, Austria; http://www.Rproject.org).

Lee J., Cristescu R., Kim K.M., Kim K., Kim S.T., Park S.H, & Kang W.K. (2017). Development of mesenchymal subtype gene signature for clinical application in gastric cancer. Oncotarget, 8(39), 66305-66315.

+ Open protocol

+ Expand

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?

Revolutionizing how scientists
search and build protocols!