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chi-square test function  (CH Instruments)

 
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    CH Instruments chi-square test function
    Chi Square Test Function, supplied by CH Instruments, 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/chi-square+function/chi+square+function/pmc12204608-82-6-6
    Average 90 stars, based on 1 article reviews
    chi-square test function - by Bioz Stars, 2026-10
    90/100 stars

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    other:

    Article Title: Effect of enzymatic hydrolysis on digestibility and morpho-structural properties of hydrothermally pre-treated red rice starch.
    Article Snippet: The model’s fit was evaluated using the coefficient of determination (R2) and the chi-square function (χ2).

    Article Title: Thermodynamic, Thermal, Structural Properties and In Vitro Digestibility of Basmati Rice Starch Subjected to α‐Amylase
    Article Snippet: The objective of this work is to evaluate the effect of drying temperature combined with enzymatic modification with α-amylase on the in vitro digestibility, thermodynamic, thermal and structural properties of Basmati rice starch.. The basmati rice starch is dried at drying temperatures (40, 45, 50, 55, and 60 °C), during the process thermodynamic properties such as enthalpy, entropy, and Gibbs free energy are analyzed.. After drying, the raw starches are treated with α-amylase (3 U mg−1) and analyzed by X-ray diffraction and differential scanning calorimeter.

    Article Title: Constraints on the emergent universe from recent temperature and Hubble data
    Article Snippet: In this paper, the emergent universe model (EU) is explored (as per Mukherjee et al.) in a flat scenario with equation of state (EoS) p = Bρ − Aρ 2 (where A and B are constants).. Here, the temperature function is evaluated in terms of the EoS parameters and redshift under the conditions of balanced particle creation and annihilation.. To examine the thermodynamic evolution and find a viable EU model, constraints on its EoS parameters are determined.

    Article Title: Significance of a quadratically varying deceleration parameter in scalar field-dominated model and cosmic acceleration
    Article Snippet: In this study, we explore the enigma of late-time cosmic acceleration in the framework of a general scalar ̄eld playing the role of dark energy.. Our investigation yields an exact solution to Einstein's ̄eld equations, achieved through a parametrization of the deceleration parameter qðaÞ in quadratic form in the homogeneous and isotropic background geometry represented by Friedmann{Lemaître{Robertson{Walker (FLRW) spacetime.. This parametrization unveils a seamless transition from a decelerated to an accelerated phase in the cosmic evolution.

    Article Title: Exploring the deceleration parameter in f(T) gravity: A comprehensive analysis using parametrization techniques and observational data
    Article Snippet: International Journal of Geometric Methods in Modern Physics Vol.. 21, No. 9 (2024) 2450166 (34 pages) c© World Scientific Publishing Company DOI: 10.1142/S0219887824501664 Exploring the deceleration parameter in f(T ) gravity:

    Article Title: Viscoelastic properties of plasma-agarose hydrogels dictate favorable fibroblast responses for skin tissue engineering applications.
    Article Snippet: The optimal number of Maxwell elements required was determined using the Chi-square function (Fig. 1g) following Eq. (4): x2 = ∑100 j=0 [ Ej − E ( tj ) σj ] (4) where j varies from 0 to 100 s, Ej is the measured value at time j, E(tj) is calculated from Eq. (3) and σj is the standard error of the device equal to 1.41 Pa.

    Article Title: Anisotropic f(Q) gravity model with bulk viscosity
    Article Snippet: The mean values of the model parameters H0, , n, 0 and 1 are calculated using the chi-square function as follows: 2HðH0; ;n; 0; 1Þ 1⁄4 X31 k1⁄41 1⁄2HthðH0; ;n; 0; 1; zkÞ HobsðzkÞ 2 2HðzkÞ ; ð31Þ where Hth denotes the predicted Hubble parameter value from the model, Hobs represents its observed value, and the standard error in the observed value of H is denoted by HðzkÞ.



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    Spine classes are inhomogeneously distributed on the dendrites. (a) Spines were categorized into four classes by hierarchical clustering of shape and length: upper panels—individual diameter profiles (gray) and average profile of each class (green). The total lengths of the profiles were scaled to the average spine length of the respective class; lower panels—representative spine examples. Scale bars, 1 μ m . (b) Relative abundance of each spine class on the dendrites, the distribution differs among dendrites. Numbers 1 to 4 refer to the four neurons, letters to the dendrites (see <xref ref-type=Fig. 1 ). Color code as in panel (a). (c) Pie charts showing which fraction of a specific spine class is found on each dendrite. (d) Pairwise Pearson’s chi-square tests confirm significant differences among the repartition of the classes on the dendrites within three of the four neurons. p values color coded: p ≤ 0.05 red, p ≤ 0.01 yellow, p ≤ 0.001 green, and p > 0.05 blue. All tests have been corrected for multiple comparisons. (e) Pairwise Pearson’s chi-square tests for all dendrites of all cells. " width="250" height="auto" />
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    Spine classes are inhomogeneously distributed on the dendrites. (a) Spines were categorized into four classes by hierarchical clustering of shape and length: upper panels—individual diameter profiles (gray) and average profile of each class (green). The total lengths of the profiles were scaled to the average spine length of the respective class; lower panels—representative spine examples. Scale bars, 1 μ m . (b) Relative abundance of each spine class on the dendrites, the distribution differs among dendrites. Numbers 1 to 4 refer to the four neurons, letters to the dendrites (see <xref ref-type=Fig. 1 ). Color code as in panel (a). (c) Pie charts showing which fraction of a specific spine class is found on each dendrite. (d) Pairwise Pearson’s chi-square tests confirm significant differences among the repartition of the classes on the dendrites within three of the four neurons. p values color coded: p ≤ 0.05 red, p ≤ 0.01 yellow, p ≤ 0.001 green, and p > 0.05 blue. All tests have been corrected for multiple comparisons. (e) Pairwise Pearson’s chi-square tests for all dendrites of all cells. " width="250" height="auto" />
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    Spine classes are inhomogeneously distributed on the dendrites. (a) Spines were categorized into four classes by hierarchical clustering of shape and length: upper panels—individual diameter profiles (gray) and average profile of each class (green). The total lengths of the profiles were scaled to the average spine length of the respective class; lower panels—representative spine examples. Scale bars, 1 μ m . (b) Relative abundance of each spine class on the dendrites, the distribution differs among dendrites. Numbers 1 to 4 refer to the four neurons, letters to the dendrites (see <xref ref-type=Fig. 1 ). Color code as in panel (a). (c) Pie charts showing which fraction of a specific spine class is found on each dendrite. (d) Pairwise Pearson’s chi-square tests confirm significant differences among the repartition of the classes on the dendrites within three of the four neurons. p values color coded: p ≤ 0.05 red, p ≤ 0.01 yellow, p ≤ 0.001 green, and p > 0.05 blue. All tests have been corrected for multiple comparisons. (e) Pairwise Pearson’s chi-square tests for all dendrites of all cells. " width="100%" height="100%">

    Journal: Neurophotonics

    Article Title: Distribution of spine classes shows intra-neuronal dendritic heterogeneity in mouse cortex

    doi: 10.1117/1.NPh.12.1.015001

    Figure Lengend Snippet: Spine classes are inhomogeneously distributed on the dendrites. (a) Spines were categorized into four classes by hierarchical clustering of shape and length: upper panels—individual diameter profiles (gray) and average profile of each class (green). The total lengths of the profiles were scaled to the average spine length of the respective class; lower panels—representative spine examples. Scale bars, 1 μ m . (b) Relative abundance of each spine class on the dendrites, the distribution differs among dendrites. Numbers 1 to 4 refer to the four neurons, letters to the dendrites (see Fig. 1 ). Color code as in panel (a). (c) Pie charts showing which fraction of a specific spine class is found on each dendrite. (d) Pairwise Pearson’s chi-square tests confirm significant differences among the repartition of the classes on the dendrites within three of the four neurons. p values color coded: p ≤ 0.05 red, p ≤ 0.01 yellow, p ≤ 0.001 green, and p > 0.05 blue. All tests have been corrected for multiple comparisons. (e) Pairwise Pearson’s chi-square tests for all dendrites of all cells.

    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.

    Techniques: