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pearson’s linear correlation coefficient (ρ)  (MathWorks Inc)


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    MathWorks Inc pearson’s linear correlation coefficient (ρ)
    Pearson’s Linear Correlation Coefficient (ρ), supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/linear+pearson+correlation+coefficient/pmc08338993-355-0-11
    Average 90 stars, based on 1 article reviews
    pearson’s linear correlation coefficient (ρ) - by Bioz Stars, 2026-10
    90/100 stars

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    Article Title: Oxidative potential of urban PM 2.5 in relation to chemical composition: Importance of fossil driven sources.
    Article Snippet: Pearson’s correlation between DTTv and chemical components was calculated using MATLAB, and significance was considered at p < 0.05.

    Article Title: Distribution of spine classes shows intra-neuronal dendritic heterogeneity in mouse cortex
    Article Snippet: We used Pearson’s chi-square test (Matlab’s function crosstab) to test for significant differences in class distribution on dendrites ( ) with a 5% significance level and corrected for multiple comparisons with the Bonferroni correction.

    Article Title: Decoupling of motor cortex to movement in Parkinson’s dyskinesia rescued by sub-anaesthetic ketamine
    Article Snippet: Second, Pearson’s correlations between all pairwise neuron combinations were computed (corr, MATLAB).

    Article Title: Enclosing 95% confidence area and volume to center of pressure and center of mass in posturography using optimization algorithm and coordinate ascent methods.
    Article Snippet: Balance control has been evaluated using center of pressure (CoP) and center of mass (CoM).. One of the most common approaches in stabilometry is enclosing ellipse to 95% of data using principal component analysis (PCA) or covariance methods.. However, these methods have limitations, including normality assumption, lack of accuracy, and sample size influence.

    Article Title: Distribution of spine classes shows intra-neuronal dendritic heterogeneity in mouse cortex
    Article Snippet: We used Pearson’s chi-square test (Matlab’s function crosstab) to test for significant differences in class distribution on dendrites (Fig. 2) with a 5% significance level and corrected for multiple comparisons with the Bonferroni correction.

    Article Title: Differences in cerebral structure among patients with amnestic mild cognitive impairment and patients with Alzheimer’s disease
    Article Snippet: By using MATLAB, the Pearson correlation analysis between the values of the significantly different brain areas obtained from SBM/VBM and MMSE/MoCA scores was performed, with the age and gender of individuals used as concomitant variables.

    Article Title: Subtle alteration in transcriptional memory governs the lineage-level cell cycle duration heterogeneities of mammalian cells
    Article Snippet: Using the Pearson correlation function in MATLAB, we calculated the correlation in these lineage pairs (M-D, D-D, and C-C).



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    Box-and-whisker plots for each session-wise comparison, derived from 14 non-collision sports athletes (NCA, left) and 42 football athletes (FBA, right), demonstrate that self-similarity ( I self ) distributions for FBA computed at Pre-In2 were significantly different from those observed for Pre-In1 and Pre-Post , but not from the NCA I self distribution. The I self values for each scan session pair (as indicated for each column) was computed as the <t>Pearson’s</t> correlation between an individual’s functional connectomes for the given sessions, with each individual’s I self plotted as a gray circle. For FBA, colored lines connect datapoints representing the same participant in different session comparisons; solid red lines indicate decreasing I self , and dashed blue lines indicate increasing I self . Asterisks indicate a statistical significance at the p FDR < 0.05 level (Wilcoxon signed rank test).
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    Box-and-whisker plots for each session-wise comparison, derived from 14 non-collision sports athletes (NCA, left) and 42 football athletes (FBA, right), demonstrate that self-similarity ( I self ) distributions for FBA computed at Pre-In2 were significantly different from those observed for Pre-In1 and Pre-Post , but not from the NCA I self distribution. The I self values for each scan session pair (as indicated for each column) was computed as the <t>Pearson’s</t> correlation between an individual’s functional connectomes for the given sessions, with each individual’s I self plotted as a gray circle. For FBA, colored lines connect datapoints representing the same participant in different session comparisons; solid red lines indicate decreasing I self , and dashed blue lines indicate increasing I self . Asterisks indicate a statistical significance at the p FDR < 0.05 level (Wilcoxon signed rank test).
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    Box-and-whisker plots for each session-wise comparison, derived from 14 non-collision sports athletes (NCA, left) and 42 football athletes (FBA, right), demonstrate that self-similarity ( I self ) distributions for FBA computed at Pre-In2 were significantly different from those observed for Pre-In1 and Pre-Post , but not from the NCA I self distribution. The I self values for each scan session pair (as indicated for each column) was computed as the <t>Pearson’s</t> correlation between an individual’s functional connectomes for the given sessions, with each individual’s I self plotted as a gray circle. For FBA, colored lines connect datapoints representing the same participant in different session comparisons; solid red lines indicate decreasing I self , and dashed blue lines indicate increasing I self . Asterisks indicate a statistical significance at the p FDR < 0.05 level (Wilcoxon signed rank test).
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    Box-and-whisker plots for each session-wise comparison, derived from 14 non-collision sports athletes (NCA, left) and 42 football athletes (FBA, right), demonstrate that self-similarity ( I self ) distributions for FBA computed at Pre-In2 were significantly different from those observed for Pre-In1 and Pre-Post , but not from the NCA I self distribution. The I self values for each scan session pair (as indicated for each column) was computed as the <t>Pearson’s</t> correlation between an individual’s functional connectomes for the given sessions, with each individual’s I self plotted as a gray circle. For FBA, colored lines connect datapoints representing the same participant in different session comparisons; solid red lines indicate decreasing I self , and dashed blue lines indicate increasing I self . Asterisks indicate a statistical significance at the p FDR < 0.05 level (Wilcoxon signed rank test).
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    Box-and-whisker plots for each session-wise comparison, derived from 14 non-collision sports athletes (NCA, left) and 42 football athletes (FBA, right), demonstrate that self-similarity ( I self ) distributions for FBA computed at Pre-In2 were significantly different from those observed for Pre-In1 and Pre-Post , but not from the NCA I self distribution. The I self values for each scan session pair (as indicated for each column) was computed as the <t>Pearson’s</t> correlation between an individual’s functional connectomes for the given sessions, with each individual’s I self plotted as a gray circle. For FBA, colored lines connect datapoints representing the same participant in different session comparisons; solid red lines indicate decreasing I self , and dashed blue lines indicate increasing I self . Asterisks indicate a statistical significance at the p FDR < 0.05 level (Wilcoxon signed rank test).
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    Box-and-whisker plots for each session-wise comparison, derived from 14 non-collision sports athletes (NCA, left) and 42 football athletes (FBA, right), demonstrate that self-similarity ( I self ) distributions for FBA computed at Pre-In2 were significantly different from those observed for Pre-In1 and Pre-Post , but not from the NCA I self distribution. The I self values for each scan session pair (as indicated for each column) was computed as the <t>Pearson’s</t> correlation between an individual’s functional connectomes for the given sessions, with each individual’s I self plotted as a gray circle. For FBA, colored lines connect datapoints representing the same participant in different session comparisons; solid red lines indicate decreasing I self , and dashed blue lines indicate increasing I self . Asterisks indicate a statistical significance at the p FDR < 0.05 level (Wilcoxon signed rank test).
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    pearson’s linear correlation coefficient (ρ)  (MathWorks Inc)
    90
    MathWorks Inc pearson’s linear correlation coefficient (ρ)
    Box-and-whisker plots for each session-wise comparison, derived from 14 non-collision sports athletes (NCA, left) and 42 football athletes (FBA, right), demonstrate that self-similarity ( I self ) distributions for FBA computed at Pre-In2 were significantly different from those observed for Pre-In1 and Pre-Post , but not from the NCA I self distribution. The I self values for each scan session pair (as indicated for each column) was computed as the <t>Pearson’s</t> correlation between an individual’s functional connectomes for the given sessions, with each individual’s I self plotted as a gray circle. For FBA, colored lines connect datapoints representing the same participant in different session comparisons; solid red lines indicate decreasing I self , and dashed blue lines indicate increasing I self . Asterisks indicate a statistical significance at the p FDR < 0.05 level (Wilcoxon signed rank test).
    Pearson’s Linear Correlation Coefficient (ρ), supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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    Box-and-whisker plots for each session-wise comparison, derived from 14 non-collision sports athletes (NCA, left) and 42 football athletes (FBA, right), demonstrate that self-similarity ( I self ) distributions for FBA computed at Pre-In2 were significantly different from those observed for Pre-In1 and Pre-Post , but not from the NCA I self distribution. The I self values for each scan session pair (as indicated for each column) was computed as the Pearson’s correlation between an individual’s functional connectomes for the given sessions, with each individual’s I self plotted as a gray circle. For FBA, colored lines connect datapoints representing the same participant in different session comparisons; solid red lines indicate decreasing I self , and dashed blue lines indicate increasing I self . Asterisks indicate a statistical significance at the p FDR < 0.05 level (Wilcoxon signed rank test).

    Journal: Scientific Reports

    Article Title: Longitudinal changes in resting state fMRI brain self-similarity of asymptomatic high school American football athletes

    doi: 10.1038/s41598-024-51688-2

    Figure Lengend Snippet: Box-and-whisker plots for each session-wise comparison, derived from 14 non-collision sports athletes (NCA, left) and 42 football athletes (FBA, right), demonstrate that self-similarity ( I self ) distributions for FBA computed at Pre-In2 were significantly different from those observed for Pre-In1 and Pre-Post , but not from the NCA I self distribution. The I self values for each scan session pair (as indicated for each column) was computed as the Pearson’s correlation between an individual’s functional connectomes for the given sessions, with each individual’s I self plotted as a gray circle. For FBA, colored lines connect datapoints representing the same participant in different session comparisons; solid red lines indicate decreasing I self , and dashed blue lines indicate increasing I self . Asterisks indicate a statistical significance at the p FDR < 0.05 level (Wilcoxon signed rank test).

    Article Snippet: Symmetric 248 × 248 functional connectivity matrices were computed for each scan session, where the entry at location (i, j) is computed as the Pearson’s linear correlation coefficient (MATLAB corr ) between averaged fMRI time series of ROIs i and j .

    Techniques: Whisker Assay, Comparison, Derivative Assay, Functional Assay