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linear regression model  (OriginLab corp)


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    OriginLab corp linear regression model
    Linear Regression Model, supplied by OriginLab corp, 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+regression+model/linear+regression+model/pmc04602255-75-12-20
    Average 90 stars, based on 1 article reviews
    linear regression model - by Bioz Stars, 2026-11
    90/100 stars

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    Related Articles

    other:

    Article Title: Region-specific delamination strength of ascending thoracic aortic aneurysm of elderly hypertensive patients with bicuspid and tricuspid aortic valves.
    Article Snippet: Both ageing and hypertension are clinical factors that may lead to a higher propensity for dissection or rupture of ascending thoracic aortic aneurysms (ATAAs).. This study sought to investigate effect of valve morphology on regional delamination strength of ATAAs in the elderly hypertensive patients.. Whole fresh ATAA samples were harvested from 23 hypertensive patients (age, 71 ± 8 years) who underwent elective aortic surgery.

    Article Title: Bringing the Lab to the Field: Validating Water-Borne Corticosterone as a Conservation Tool in Captive and Wild Amphibian Larvae
    Article Snippet: A linear model including tissue CORT, Origin, and their interaction revealed significant main effects of tissue CORT (β = 3.18 ± 0.11, t = 28.09, p <0.001) and Origin (Lab) (β = 224.02 ± 64.50, t = 3.47, p = 0.001), as well as a significant tissue CORT × Origin interaction (β = −1.81 ± 0.13, t = −13.79, p <0.001).

    Derivative Assay:

    Article Title: Microfluidic system and method for real-time measurement of antibody-antigen binding and analyte detection
    Article Snippet: A calibration curve was derived using a range of insulin or insulin analogs concentrations ranging from 500 pg/ml-100 ng/ml (86 pM-17200 pM) and generated using a regression model with Origin Pro 8 (OriginLab Corporation, Northampton, MA). .. A calibration curve was derived using a range of insulin or insulin analogs concentrations ranging from 500 pg/ml-100 ng/ml (86 pM-17200 pM) and generated using a regression model with Origin Pro 8 (OriginLab Corporation, Northampton, MA). ..

    Generated:

    Article Title: Microfluidic system and method for real-time measurement of antibody-antigen binding and analyte detection
    Article Snippet: A calibration curve was derived using a range of insulin or insulin analogs concentrations ranging from 500 pg/ml-100 ng/ml (86 pM-17200 pM) and generated using a regression model with Origin Pro 8 (OriginLab Corporation, Northampton, MA). .. A calibration curve was derived using a range of insulin or insulin analogs concentrations ranging from 500 pg/ml-100 ng/ml (86 pM-17200 pM) and generated using a regression model with Origin Pro 8 (OriginLab Corporation, Northampton, MA). ..



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    Image Search Results


    Recorded experimental data (A) Experimental setup: human participant face-to-face with Pepper humanoid. Robot performs preprogrammed motor sequences. Human mimics robot motion, mirroring the robot’s movements. Both have reflective markers for motion tracking. (B) Motion tracking data: position in 1 and 2D, and velocity. Human in green, robot in black. Light-to-dark follows beginning to end in an example sequence of movements between the four spatial targets. Note Lag as the time offset from robot to human movement onset. used as a performance measure. (C) ERSP grand average illustrating task-related desynchronization between task epochs of rest, fixation and movement for Theta, mu and alpha bands. Note strong desynchronization in mu and beta during the 80 element movement sequence. Color bar corresponds to min-max ERSP values in decibels (dB). (D) EEG signals mapped onto 10–20 coordinates for the 9 included electrodes that will be used in the MSLR model.

    Journal: iScience

    Article Title: Following the robot’s lead: Predicting human and robot movement from EEG in a motor learning HRI task

    doi: 10.1016/j.isci.2025.112914

    Figure Lengend Snippet: Recorded experimental data (A) Experimental setup: human participant face-to-face with Pepper humanoid. Robot performs preprogrammed motor sequences. Human mimics robot motion, mirroring the robot’s movements. Both have reflective markers for motion tracking. (B) Motion tracking data: position in 1 and 2D, and velocity. Human in green, robot in black. Light-to-dark follows beginning to end in an example sequence of movements between the four spatial targets. Note Lag as the time offset from robot to human movement onset. used as a performance measure. (C) ERSP grand average illustrating task-related desynchronization between task epochs of rest, fixation and movement for Theta, mu and alpha bands. Note strong desynchronization in mu and beta during the 80 element movement sequence. Color bar corresponds to min-max ERSP values in decibels (dB). (D) EEG signals mapped onto 10–20 coordinates for the 9 included electrodes that will be used in the MSLR model.

    Article Snippet: Markov-Switching Linear Regression (MSLR) models, which we ran using Dynamax , are a powerful tool for modeling time series data that exhibit regime-switching behavior, where the underlying dynamics of the system change over time.

    Techniques: Sequencing

    Modeling pipeline and example results for time-resolved predictions (A) The time-resolved model (Markov-switching linear regression, MSLR) learns the linear mapping from EEG inputs to movement readouts. However, this linear relationship varies over time, through different hidden states. After training the model, it will output movement and hidden state predictions from novel EEG inputs. (B) The model is able to predict human velocity (HV), robot X (RX), and human X (HX) positions; ground truth traces are shown in gray, model predictions in light blue. (C) Mapping inferred states as color codes onto the predicted movement readouts, over time.

    Journal: iScience

    Article Title: Following the robot’s lead: Predicting human and robot movement from EEG in a motor learning HRI task

    doi: 10.1016/j.isci.2025.112914

    Figure Lengend Snippet: Modeling pipeline and example results for time-resolved predictions (A) The time-resolved model (Markov-switching linear regression, MSLR) learns the linear mapping from EEG inputs to movement readouts. However, this linear relationship varies over time, through different hidden states. After training the model, it will output movement and hidden state predictions from novel EEG inputs. (B) The model is able to predict human velocity (HV), robot X (RX), and human X (HX) positions; ground truth traces are shown in gray, model predictions in light blue. (C) Mapping inferred states as color codes onto the predicted movement readouts, over time.

    Article Snippet: Markov-Switching Linear Regression (MSLR) models, which we ran using Dynamax , are a powerful tool for modeling time series data that exhibit regime-switching behavior, where the underlying dynamics of the system change over time.

    Techniques: