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Multiple Linear Regression
Published in Marcello Pagano, Kimberlee Gauvreau, Heather Mattie, Principles of Biostatistics, 2022
Marcello Pagano, Kimberlee Gauvreau, Heather Mattie
In the regression model that contains gestational age, preeclampsia, and the interaction between the two, preeclampsia and the gestational age–preeclampsia interaction are highly correlated. In fact, the Pearson correlation coefficient quantifying the linear relationship between these two variables is . This model and the model that did not include the interaction term are contrasted in Table 18.1. When the interaction term is included in the equation, the estimated coefficient of preeclampsia doubles in magnitude. In addition, its standard error increases by a factor of 12. In the model without the interaction term, the coefficient of preeclampsia is significantly different from 0 at the 0.05 level; when the interaction term is present, it no longer achieves statistical significance. The coefficient of determination does not change when the interaction is included. It remains 65.3%. Furthermore, the adjusted R2 decreases slightly. These facts taken together indicate that the inclusion of the gestational age – preeclampsia interaction term in the regression model does not explain any additional variability in the observed values of head circumference, beyond that which is explained by gestational age and preeclampsia alone. The information supplied by this term is redundant.
Causality Analysis of Climate and Ecosystem Time Series
Published in Vyacheslav Lyubchich, Yulia R. Gel, K. Halimeda Kilbourne, Thomas J. Miller, Nathaniel K. Newlands, Adam B. Smith, Evaluating Climate Change Impacts, 2020
Mohammad Gorji Sefidmazgi, Ali Gorji Sefidmazgi
The Pearson correlation between and is −0.10, and the correlation between and is 0.1. Figure 7.7 shows the scatter plot and histograms of these three variables, where a linear trend for versus and does not exist.
Statistics, Research and Governance
Published in Manit Arya, Taimur T. Shah, Jas S. Kalsi, Herman S. Fernando, Iqbal S. Shergill, Asif Muneer, Hashim U. Ahmed, MCQs for the FRCS(Urol) and Postgraduate Urology Examinations, 2020
Hamid Abboudi, Erik Mayer, Justin Vale
Spearman correlation is used to establish an association between non-normally distributed numerical variables. Chi-square is the test that compares the proportion of people with a particular attribute in two or more independent groups of categorical data. T-test is used to compare the means of two groups of parametric numerical data. Pearson correlation is used to determine the strength of a relationship of a continuous normally distributed variable amongst two groups.
Hemodynamic monitoring and correlation between electrical cardiometry and esophageal Doppler in patients undergoing major abdominal surgery
Published in Egyptian Journal of Anaesthesia, 2023
Shady Rady Abdalla, Ahmed Salah Abdelazeem, Tarek Abdelhalium Kaddah, Abla Salah Elhadedy, Hanan Farouk Khafagy, Ahmed Abdalla Mohamed, Ahmed Mohamed Essam
Statistical analysis was done using SPSS version 18 for Windows. The mean and standard deviation (SD) of quantitative variables were reported, and they were compared for the same group using a paired Student’s t-test. Frequency and percentages (%) were used to present qualitative characteristics. Analyzing the sensitivity, specificity, positive predictive value (PPV), and negative predictive value of diagnostic performance (NPV). Agreement: The paired Student’s T test was used to compare ICON and ED measurement results. Between ICON and ED, bias and its standard deviation were computed. ICON and ED measurement graphs of modified Bland Altman were made. Pearson correlation was used to measure the strength of the linear relationship between variables. A two tailed P value <0.05 was considered significant.
Multifaceted impulsivity in obsessive-compulsive disorder with hoarding symptoms
Published in Nordic Journal of Psychiatry, 2021
Selim Tumkaya, Bengu Yucens, Mehmet Mart, Didem Tezcan, Himani Kashyap
Statistical analyses were performed using IBM Statistical Package for Social Sciences (SPSS) version 22 software. Continuous variables were expressed as mean ± standard deviation (SD) values and categorical variables as percentages. There were no missing data. The OCDwHH, OCDwLH, and control groups were compared using the Chi-square test for categorical variables. Post-hoc comparisons of variables which showed group differences in the Chi-square test were made again with the Chi-square test. Conformity of the data to normal distribution was assessed with the Kolmogorov–Smirnov test. The Analysis of Variance (ANOVA) test was used to compare quantitative variables as they showed normal distribution. Bonferroni corrections were used for post-hoc comparisons of groups. Accordingly, statistical significance was assumed when the uncorrected p-value was below 0.0166 (0.05/3). Correlations between clinical variables were assessed using Pearson correlation analysis. Correlation analyses were used as exploratory analyses to identify potential predictors for inclusion in the subsequent regression analysis. Multiple hierarchical linear regression analysis was used to determine the predictors of hoarding symptom severity. Statistical significance levels were set at p < 0.05.
Challenges and recommendations for magnetic hyperthermia characterization measurements
Published in International Journal of Hyperthermia, 2021
J. Wells, D. Ortega, U. Steinhoff, S. Dutz, E. Garaio, O. Sandre, E. Natividad, M. M. Cruz, F. Brero, P. Southern, Q. A. Pankhurst, S. Spassov
For correlation analysis of the data, both the Pearson and Spearman statistical methods were used. In both cases they were calculated using readily available spreadsheet functions. The Pearson correlation coefficient rxy is a measure of the linear correlation between two variables x and y, with rxy = +1 or −1 denoting a total positive or negative linear correlation, and rxy = 0 indicating no linear correlation at all. The Spearman rank correlation coefficient ρ is equal to the Pearson coefficient applied to the rank values of the two variables, rather than the variables themselves. It is the non-parametric version of the Pearson correlation and is used to assess the degree to which two variables are monotonically related.