analysis of covariance (ancova) with the matlab aoctool Search Results


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Function Aoctool, 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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(a) Linear regressions for the ratio of percentage of subjects in high to low vigilance stages between all resting states. (b) Temporal evolution of the percentage of subjects in high (red) versus low (blue) vigilance stages for each TR following the three narratives: insecure‐dismissing (left), insecure‐preoccupied (middle), and secure (right). Linear regressions for both high and low vigilance stages are plotted as black lines. The coefficients for the intercept and slope of the <t>ANCOVA</t> model equations differed between narrative conditions: insecure‐dismissing: y = 76.1−0.151x+ε; insecure‐preoccupied: y = 68.8−0.077x+ε; secure: y = 69.6−0.074x+ε and revealed significant differences between the slopes of the regression in insecure‐dismissing versus insecure‐preoccupied as well as insecure‐dismissing versus secure
Aoctool, 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/result/aoctool/product/MathWorks Inc
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(a) Linear regressions for the ratio of percentage of subjects in high to low vigilance stages between all resting states. (b) Temporal evolution of the percentage of subjects in high (red) versus low (blue) vigilance stages for each TR following the three narratives: insecure‐dismissing (left), insecure‐preoccupied (middle), and secure (right). Linear regressions for both high and low vigilance stages are plotted as black lines. The coefficients for the intercept and slope of the <t>ANCOVA</t> model equations differed between narrative conditions: insecure‐dismissing: y = 76.1−0.151x+ε; insecure‐preoccupied: y = 68.8−0.077x+ε; secure: y = 69.6−0.074x+ε and revealed significant differences between the slopes of the regression in insecure‐dismissing versus insecure‐preoccupied as well as insecure‐dismissing versus secure
Matlab Matlab R2020a, 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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analysis of covariance (ancova) with the matlab aoctool  (MathWorks Inc)
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(a) Linear regressions for the ratio of percentage of subjects in high to low vigilance stages between all resting states. (b) Temporal evolution of the percentage of subjects in high (red) versus low (blue) vigilance stages for each TR following the three narratives: insecure‐dismissing (left), insecure‐preoccupied (middle), and secure (right). Linear regressions for both high and low vigilance stages are plotted as black lines. The coefficients for the intercept and slope of the <t>ANCOVA</t> model equations differed between narrative conditions: insecure‐dismissing: y = 76.1−0.151x+ε; insecure‐preoccupied: y = 68.8−0.077x+ε; secure: y = 69.6−0.074x+ε and revealed significant differences between the slopes of the regression in insecure‐dismissing versus insecure‐preoccupied as well as insecure‐dismissing versus secure
Analysis Of Covariance (Ancova) With The Matlab Aoctool, 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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analysis of covariance (ancova) with the matlab aoctool - by Bioz Stars, 2026-04
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analysis of covariance tool aoctool  (MathWorks Inc)
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(a) Linear regressions for the ratio of percentage of subjects in high to low vigilance stages between all resting states. (b) Temporal evolution of the percentage of subjects in high (red) versus low (blue) vigilance stages for each TR following the three narratives: insecure‐dismissing (left), insecure‐preoccupied (middle), and secure (right). Linear regressions for both high and low vigilance stages are plotted as black lines. The coefficients for the intercept and slope of the <t>ANCOVA</t> model equations differed between narrative conditions: insecure‐dismissing: y = 76.1−0.151x+ε; insecure‐preoccupied: y = 68.8−0.077x+ε; secure: y = 69.6−0.074x+ε and revealed significant differences between the slopes of the regression in insecure‐dismissing versus insecure‐preoccupied as well as insecure‐dismissing versus secure
Analysis Of Covariance Tool Aoctool, 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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analysis of covariance tool (aoctool)  (MathWorks Inc)
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MathWorks Inc analysis of covariance tool (aoctool)
(a) Linear regressions for the ratio of percentage of subjects in high to low vigilance stages between all resting states. (b) Temporal evolution of the percentage of subjects in high (red) versus low (blue) vigilance stages for each TR following the three narratives: insecure‐dismissing (left), insecure‐preoccupied (middle), and secure (right). Linear regressions for both high and low vigilance stages are plotted as black lines. The coefficients for the intercept and slope of the <t>ANCOVA</t> model equations differed between narrative conditions: insecure‐dismissing: y = 76.1−0.151x+ε; insecure‐preoccupied: y = 68.8−0.077x+ε; secure: y = 69.6−0.074x+ε and revealed significant differences between the slopes of the regression in insecure‐dismissing versus insecure‐preoccupied as well as insecure‐dismissing versus secure
Analysis Of Covariance Tool (Aoctool), 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/result/analysis of covariance tool (aoctool)/product/MathWorks Inc
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analysis of covariance tool (aoctool) - by Bioz Stars, 2026-04
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aoctool function  (MathWorks Inc)
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(a) Linear regressions for the ratio of percentage of subjects in high to low vigilance stages between all resting states. (b) Temporal evolution of the percentage of subjects in high (red) versus low (blue) vigilance stages for each TR following the three narratives: insecure‐dismissing (left), insecure‐preoccupied (middle), and secure (right). Linear regressions for both high and low vigilance stages are plotted as black lines. The coefficients for the intercept and slope of the <t>ANCOVA</t> model equations differed between narrative conditions: insecure‐dismissing: y = 76.1−0.151x+ε; insecure‐preoccupied: y = 68.8−0.077x+ε; secure: y = 69.6−0.074x+ε and revealed significant differences between the slopes of the regression in insecure‐dismissing versus insecure‐preoccupied as well as insecure‐dismissing versus secure
Aoctool Function, 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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statistics toolbox aoctool  (MathWorks Inc)
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(a) Linear regressions for the ratio of percentage of subjects in high to low vigilance stages between all resting states. (b) Temporal evolution of the percentage of subjects in high (red) versus low (blue) vigilance stages for each TR following the three narratives: insecure‐dismissing (left), insecure‐preoccupied (middle), and secure (right). Linear regressions for both high and low vigilance stages are plotted as black lines. The coefficients for the intercept and slope of the <t>ANCOVA</t> model equations differed between narrative conditions: insecure‐dismissing: y = 76.1−0.151x+ε; insecure‐preoccupied: y = 68.8−0.077x+ε; secure: y = 69.6−0.074x+ε and revealed significant differences between the slopes of the regression in insecure‐dismissing versus insecure‐preoccupied as well as insecure‐dismissing versus secure
Statistics Toolbox Aoctool, 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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(a) Linear regressions for the ratio of percentage of subjects in high to low vigilance stages between all resting states. (b) Temporal evolution of the percentage of subjects in high (red) versus low (blue) vigilance stages for each TR following the three narratives: insecure‐dismissing (left), insecure‐preoccupied (middle), and secure (right). Linear regressions for both high and low vigilance stages are plotted as black lines. The coefficients for the intercept and slope of the <t>ANCOVA</t> model equations differed between narrative conditions: insecure‐dismissing: y = 76.1−0.151x+ε; insecure‐preoccupied: y = 68.8−0.077x+ε; secure: y = 69.6−0.074x+ε and revealed significant differences between the slopes of the regression in insecure‐dismissing versus insecure‐preoccupied as well as insecure‐dismissing versus secure
Matlab Statistics Toolbox, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 96/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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Image Search Results


(a) Linear regressions for the ratio of percentage of subjects in high to low vigilance stages between all resting states. (b) Temporal evolution of the percentage of subjects in high (red) versus low (blue) vigilance stages for each TR following the three narratives: insecure‐dismissing (left), insecure‐preoccupied (middle), and secure (right). Linear regressions for both high and low vigilance stages are plotted as black lines. The coefficients for the intercept and slope of the ANCOVA model equations differed between narrative conditions: insecure‐dismissing: y = 76.1−0.151x+ε; insecure‐preoccupied: y = 68.8−0.077x+ε; secure: y = 69.6−0.074x+ε and revealed significant differences between the slopes of the regression in insecure‐dismissing versus insecure‐preoccupied as well as insecure‐dismissing versus secure

Journal: Brain and Behavior

Article Title: Exposure to attachment narratives dynamically modulates cortical arousal during the resting state in the listener

doi: 10.1002/brb3.1007

Figure Lengend Snippet: (a) Linear regressions for the ratio of percentage of subjects in high to low vigilance stages between all resting states. (b) Temporal evolution of the percentage of subjects in high (red) versus low (blue) vigilance stages for each TR following the three narratives: insecure‐dismissing (left), insecure‐preoccupied (middle), and secure (right). Linear regressions for both high and low vigilance stages are plotted as black lines. The coefficients for the intercept and slope of the ANCOVA model equations differed between narrative conditions: insecure‐dismissing: y = 76.1−0.151x+ε; insecure‐preoccupied: y = 68.8−0.077x+ε; secure: y = 69.6−0.074x+ε and revealed significant differences between the slopes of the regression in insecure‐dismissing versus insecure‐preoccupied as well as insecure‐dismissing versus secure

Article Snippet: Linear regressions for high vigilance stages were added to compare the slope and intercept between narratives in an ANCOVA (aoctool in MATLAB) using a Bonferroni adjustment to correct for multiple comparisons (multcompare in MATLAB).

Techniques:

ANCOVA model coefficient estimates for all narrative conditions

Journal: Brain and Behavior

Article Title: Exposure to attachment narratives dynamically modulates cortical arousal during the resting state in the listener

doi: 10.1002/brb3.1007

Figure Lengend Snippet: ANCOVA model coefficient estimates for all narrative conditions

Article Snippet: Linear regressions for high vigilance stages were added to compare the slope and intercept between narratives in an ANCOVA (aoctool in MATLAB) using a Bonferroni adjustment to correct for multiple comparisons (multcompare in MATLAB).

Techniques:

Results of the post hoc tests on ANCOVA results

Journal: Brain and Behavior

Article Title: Exposure to attachment narratives dynamically modulates cortical arousal during the resting state in the listener

doi: 10.1002/brb3.1007

Figure Lengend Snippet: Results of the post hoc tests on ANCOVA results

Article Snippet: Linear regressions for high vigilance stages were added to compare the slope and intercept between narratives in an ANCOVA (aoctool in MATLAB) using a Bonferroni adjustment to correct for multiple comparisons (multcompare in MATLAB).

Techniques: