An Introduction to Bivariate Regression

“Jumping the Shark”: An Interdisciplinary Activity for Engaging Students With the Principles of Bivariate Regression

Teaching of Psychology ◽

10.1177/00986283211008857 ◽

2021 ◽

pp. 009862832110088

Author(s):

Todd D. Watson

Keyword(s):

Self Report ◽

Learning Goals ◽

Bivariate Correlation ◽

Process Learning ◽

Content Acquisition ◽

Bivariate Regression ◽

Feedback Data ◽

The Relationship ◽

Basic Content ◽

Teaching Implications

Background: Student anxiety about statistics may lead to poorer learning outcomes. Objective: The purpose of this study was to evaluate an exercise designed to teach students in an introductory statistics class the principles of bivariate regression and to emphasize how statistical tools used by psychologists are also implemented in other fields. Method: Students used a published model on the relationship between tooth size and the length of great white sharks to estimate the length of extinct sharks and to explore factors that could affect the accuracy or validity of regression analyses. Data from an anonymous self-report scale were used to assess the activity. Results: More than 95% of respondents agreed or strongly agreed that the activity was engaging, approximately 95% of students agreed or strongly agreed that the activity helped them learn about factors that can lead to problems with bivariate correlation/regression, and approximately 91% of respondents correctly answered a question designed to assess basic content acquisition. Conclusion: Feedback data suggest that the exercise was successful in achieving its content and process learning goals. Teaching Implications: Implementation of similar exercises may improve student engagement and outcomes in psychology statistics courses.

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Abstract P777: Statistical Modeling of Proteomic Signaling During Stroke in Patients Undergoing Thrombectomy

Stroke ◽

10.1161/str.52.suppl_1.p777 ◽

2021 ◽

Vol 52 (Suppl_1) ◽

Author(s):

Sydney Claypoole ◽

Jacqueline Frank ◽

Madison Sands ◽

Christopher J McLouth ◽

Jill Roberts ◽

...

Keyword(s):

Ischemic Stroke ◽

Statistical Modeling ◽

Internal Control ◽

Stepwise Regression ◽

Infarct Volume ◽

Tissue Samples ◽

Bivariate Regression ◽

Inflammatory Proteins ◽

The Relationship ◽

Proximity Extension Assay

Introduction: The previously published Blood and Clot Thrombectomy Registry and Collaboration (BACTRAC) protocol (clinicaltrials.gov NCT03153683) utilizes mechanical thrombectomy to obtain tissue samples for banking. Peripheral blood proximal to the clot and intracranial blood distal from the clot were isolated. Proteomic and statistical analyses revealed normalized (intracranial-systemic) CCL19 expression was a predictor of infarct volume. Statistical modeling analyses were used determine the CCL19-associated proteomic signaling network occurring during ischemic stroke relating to infarct volume. Methods: Arterial intracranial and systemic blood samples underwent analysis for inflammatory proteins using Proximity Extension Assay (PEA) via Olink (Olink Proteomics, Boston, MA). Systemic expression was used as an internal control to normalize expression in the intracranial blood. Bivariate regression was used to examine the relationship between the intracranial normalized CCL19 expression and infarct volume. A backwards stepwise regression was then used to determine a model of predictability of infarct volume by CCL19 and associated inflammatory proteins. Results: 25 subjects (>18 yrs) with a mean infarct volume of 8,172 ± 82,284 mm 3 and mean infarct time of 513 ± 246 minutes were included in this study. Their median age was 64 (24-91) and 10 (40%) were male. 16 subjects (64%) had hypertension, 15 (60%) had BMI > 25, and 6 (24%) had a previous stroke. The stepwise regression model shows normalized expression of 16 proteins correlated with an increase in infarct volume (p<0.005): CCL20, CXCL1, OSM, CD6, OSMR, TGF-alpha, TRANCE, CXCL10, LIF-R, CCL19, CDCP1, Flt3L, CCL23, CD244, TRAIL, NOTCH1. Conclusions: In our model, the expression of these proteins were consistently changed, though the directionality differed. LIF-R, NOTCH1, TRAIL, CD6, CCL23, TGF-alpha, and CCL20 were positively correlated, while the expressions of Flt3L, OSM, OSMR, TRANCE, CD244, CDCP1, CXCL1, CXCL10, and CCL19 were negatively correlated with infarct volume. This model depicts the proteomic signaling occurring during stroke in relationship to infarct volume, which reveals potential biomarkers and therapeutic targets for the early phase of ischemic stroke.

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REGRES: A FORTRAN-77 program to calculate nonparametric and “structural” parametric solutions to bivariate regression equations

Computers & Geosciences ◽

10.1016/0098-3004(86)90031-2 ◽

1986 ◽

Vol 12 (6) ◽

pp. 807-818 ◽

Cited By ~ 9

Author(s):

N.M.S Rock ◽

T.R Duffy

Keyword(s):

Regression Equations ◽

Parametric Solutions ◽

Bivariate Regression ◽

Fortran 77

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Cross-tissue eQTL mapping in the presence of missing data via surrogate outcome analysis

10.1101/2020.11.29.403063 ◽

2020 ◽

Author(s):

Zachary R. McCaw ◽

Sheila M. Gaynor ◽

Ryan Sun ◽

Xihong Lin

Keyword(s):

Missing Data ◽

Asymptotic Relative Efficiency ◽

Tissue Expression ◽

Outcome Analysis ◽

Eqtl Mapping ◽

Specific Expression ◽

Tissue Specific ◽

Surrogate Outcome ◽

Surrogate Outcomes ◽

Bivariate Regression

AbstractMissing data are prevalent in the Genotype-Tissue Expression (GTEx) project, where measurements from certain inaccessible tissues, such as the substantia nigra (SSN), are available at much smaller sample sizes than those from accessible tissues, such as blood. This severely limits power for identifying tissue-specific expression quantitative trait loci (eQTL). Here we propose Surrogate Phenotype Regression Analysis (Spray) for leveraging information from a correlated surrogate outcome (e.g. expression in blood) to improve inference on a partially missing target outcome (e.g. expression in SSN). Rather than regarding the surrogate outcome as a proxy for the target outcome, Spray jointly models the target and surrogate outcomes within a bivariate regression framework. Unobserved values of either outcome are regarded as missing data. We describe and implement an expectation conditional maximization algorithm for performing estimation in the presence of bilateral outcome missingness. We then demonstrate analytically, via the asymptotic relative efficiency, and empirically, through simulations and tissue-specific eQTL mapping, that in comparison with marginally modeling the target outcome, jointly modeling the target and surrogate outcomes increases estimation precision and improves power.

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PERBANDINGAN MODEL REGRESI BINOMIAL NEGATIF BIVARIAT DENGAN MODEL GEOGRAPHICALLY WEIGHTED NEGATIVE BINOMIAL BIVARIAT REGRESSION (GWNBBR) PADA KASUS ANGKA KEMATIAN BAYI DAN KEMATIAN IBU DI JAWA TENGAH

Jurnal Gaussian ◽

10.14710/j.gauss.v10i4.33096 ◽

2022 ◽

Vol 10 (4) ◽

pp. 488-498

Author(s):

Yashmine Noor Islami ◽

Dwi Ispriyanti ◽

Puspita Kartikasari

Keyword(s):

Infant Mortality ◽

Maternal Mortality ◽

Count Data ◽

Poisson Regression ◽

Negative Binomial ◽

Poor People ◽

Significant Variable ◽

Central Java ◽

Observation Area ◽

Bivariate Regression

Infant mortality (0-11 months) and maternal mortality (during pregnancy, childbirth, and postpartum) are significant indicators in determining the level of public health. Central Java Province which has 35 regencies/cities is included in the top five regions with the highest number of infant and maternal mortality in Indonesia. The data characteristics of the number of infants and maternal mortality are count data. Therefore, the Poisson Regression method can be used to analyze the factors that influence the number of infants and maternal mortality. In Poisson regression analysis, there must be a fulfilled assumption, called equidispersion. Frequently, the variance of count data is greater than the mean, which is known as the overdispersion. The research, binomial negative bivariate regression is used as a solutions to overcome the problem of overdispersion in poisson regression. This method produce a global model. In reality, the geographical, socio-cultural, and economic conditions of each region will be different. This illustrates the effect of spatial heterogeneity, so it needs to be developed into Geographically Weighted Negative Binomial Bivariate Regression (GWNBBR). The model of GWNBBR provides weighting based on the position or distance from one observation area to another. Significant variables for modeling infant mortality cases included the percentage of obstetric complications treated (X1), the percentage of infants who were exclusively breastfed (X3), and the percentage of poor people (X5). Significant variable for modeling maternal mortality cases is the percentage of poor people (X5). Based on the AIC value, GWNBBR model is better than binomial negatif bivariat regression model because it has a smaller AIC value.

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