Scholars trained in the use of factorial ANOVAs have increasingly begun using linear modelling techniques. When models contain interactions between continuous variables (or powers of them), it has long been argued that it is necessary to mean center prior to conducting the analysis. A review of the recommendations offered in statistical textbooks shows considerable disagreement, with some authors maintaining that centering is necessary, and others arguing that it is more trouble than it is worth. We also find errors in people’s beliefs about how to interpret first-order regression coefficients in moderated regression. These coefficients do not index main effects, whether data have been centered or not, but mischaracterizing them is probably more likely after centering. In this study we review the recommendations, and then provide two demonstrations using ordinary least squares (OLS) regression models with continuous predictors. We show that mean centering has no effect on the numeric estimate, the confidence intervals, or the t- or p-values for main effects, interactions, or quadratic terms, provided one knows how to properly assess them. We also highlight some shortcomings of the standardized regression coefficient (β), and note some advantages of the semipartial correlation coefficient (sr). We demonstrate that some aspects of conventional wisdom were probably never correct; other concerns have been removed by advances in computer precision. In OLS models with continuous predictors, mean centering might or might not aid interpretation, but it is not necessary. We close with practical recommendations.

1000 simulated data sets stored in a list of R dataframes used in support of Reisetter et al. (submitted) 'Mixture model normalization for non-targeted gas chromatography / mass spectrometry metabolomics data'. These are results after normalization using mean centering as described in Reisetter et al.

Animal ecologists often collect hierarchically-structured data and analyze these with linear mixed-effects models. Specific complications arise when the effect sizes of covariates vary on multiple levels (e.g., within vs among subjects). Mean-centering of covariates within subjects offers a useful approach in such situations, but is not without problems. A statistical model represents a hypothesis about the underlying biological process. Mean-centering within clusters assumes that the lower level responses (e.g. within subjects) depend on the deviation from the subject mean (relative) rather than on absolute values of the covariate. This may or may not be biologically realistic. We show that mismatch between the nature of the generating (i.e., biological) process and the form of the statistical analysis produce major conceptual and operational challenges for empiricists. We explored the consequences of mismatches by simulating data with three response-generating processes differing in the source of correlation between a covariate and the response. These data were then analyzed by three different analysis equations. We asked how robustly different analysis equations estimate key parameters of interest and under which circumstances biases arise. Mismatches between generating and analytical equations created several intractable problems for estimating key parameters. The most widely misestimated parameter was the among-subject variance in response. We found that no single analysis equation was robust in estimating all parameters generated by all equations. Importantly, even when response-generating and analysis equations matched mathematically, bias in some parameters arose when sampling across the range of the covariate was limited. Our results have general implications for how we collect and analyze data. They also remind us more generally that conclusions from statistical analysis of data are conditional on a hypothesis, sometimes implicit, for the process(es) that generated the attributes we measure. We discuss strategies for real data analysis in face of uncertainty about the underlying biological process.

Mean_Center_Hisp_2000

Dataset Description:

• This dataset has population data of each Indian district from 2001 and 2011 censuses.
• The special thing about this data is that it has centroids for each district and state.
• Centroids for a district are calculated by mapping border of each district as a polygon of latitude/longitude points in a 2D plane and then calculating their mean center.
• Centroids for a state are calculated by calculating the weighted mean center of all districts that constitutes a state. The population count is the weight assigned to each district.

Example Analysis:

• The complete code for calculating the centroids and web scraping for the data is shared on GitHub.

• The purpose of this project was to map population density center for each state.

• You can also read about the complete project here: https://medium.com/@sumit.arora/plotting-weighted-mean-population-centroids-on-a-country-map-22da408c1397

Output Screenshots: Indian districts mapped as polygons https://i.imgur.com/UK1DCGW.png" alt="Indian districts mapped as polygons">

Mapping centroids for each district https://i.imgur.com/KCAh7Jj.png" alt="Mapping centroids for each district">

Mean centers of population by state, 2001 vs. 2011 https://i.imgur.com/TLHPHjB.png" alt="Mean centers of population by state, 2001 vs. 2011">

National center of population https://i.imgur.com/yYxE4Hc.png" alt="National center of population">

Responding to a 2024 survey, data center owners and operators reported an average annual power usage effectiveness (PUE) ratio of 1.56 at their largest data center. PUE is calculated by dividing the total power supplied to a facility by the power used to run IT equipment within the facility. A lower figure therefore indicates greater efficiency, as a smaller share of total power is being used to run secondary functions such as cooling.

Mean and standard deviation (SD) of health center and woreda characteristics among Ethiopian health centers (N = 221).

Weighted_Mean_Center_Hispanic_2000

Weighted_Mean_Center_Blacks_2000

Weighted_Mean_Center_Hisp_2000

CoP excursion ranges in the anterior-posterior (AP) and medio-lateral (ML) directions for the early and late phases during the null, jump-0, jump-b, jump-f, and post blocks. Early consisted of the first 15 successful trials and late were the last 15 successful trials.□Subjects had different target distances.na = not applicable. Response times only calculated when subjects were forced to make a maneuver during the backwards target-jump.Bold text highlights planned comparisons.*Significantly different, p<0.05.

Graph and download economic data for Mean Commuting Time for Workers (5-year estimate) in Centre County, PA (B080ACS042027) from 2009 to 2023 about Centre County, PA; State College; commuting time; PA; workers; average; 5-year; and USA.

The average monthly asking rent per square foot of shopping center real estate in St. Louis, Missouri, increased overall between 2020 and 2024. Rents peaked in the third quarter of 2023 at ***** U.S. dollars, followed by a decrease to ***** U.S. dollars in the first quarter of 2024. Hawaii, San Francisco, and San Jose were the markets with the highest average shopping center rent in the U.S.

The average monthly asking rent for shopping centers in the leading U.S. markets in 2024 ranged between ** and ** U.S. dollars per square foot. In the first quarter of the year, Hawaii had the most expensive rent, at ***** U.S. dollars per square foot. Overall, rents were the highest in the West region.

No description is available. Visit https://dataone.org/datasets/b2031b8b6bd6e215f84b3cb330be7aca for complete metadata about this dataset.

Restricted mean survival time (RMST) has gained increased attention in biostatistical and clinical studies. Directly modeling RMST (as opposed to modeling then transforming the hazard function) is appealing computationally and in terms of interpreting covariate effects. We propose computationally convenient methods for evaluating center effects based on RMST. A multiplicative model for the RMST is assumed. Estimation proceeds through an algorithm analogous to stratification, which permits the evaluation of thousands of centers. We derive the asymptotic properties of the proposed estimators, and evaluate finite sample performance through simulation. We demonstrate that considerable decreases in computational burden are achievable through the proposed methods, in terms of both storage requirements and run time. The methods are applied to evaluate more than 5,000 U.S. dialysis facilities using data from a national end-stage renal disease registry.

No description is available. Visit https://dataone.org/datasets/4c89c8deb18d264755c7bc432ce8a38f for complete metadata about this dataset.

No description is available. Visit https://dataone.org/datasets/%7B8FBED25B-0F4A-4D68-A297-69288C8D597F%7D for complete metadata about this dataset.

Mean monthly temperature and the monthly sum of precipitation from long-term Colorado Climate Center meteorological observations near the study sites.

About the Dataset The dataset contains information on several measurements of breast cancer tumors for a group of patients. The dataset includes the following variables:

id: Identification number for each patient diagnosis: Whether the tumor is malignant (M) or benign (B) radius_mean: Mean of distances from center to points on the perimeter of the tumor texture_mean: Standard deviation of gray-scale values of pixels in an image of the tumor perimeter_mean: Perimeter of the tumor area_mean: Area of the tumor smoothness_mean: Local variation in radius lengths compactness_mean: Perimeter^2 / area - 1.0 concavity_mean: Severity of concave portions of the contour concave points_mean: Number of concave portions of the contour symmetry_mean: Symmetry of tumor fractal_dimension_mean: "Coastline approximation" - 1 radius_se: Standard error of the mean of distances from center to points on the perimeter texture_se: Standard error of gray-scale values perimeter_se: Standard error of the tumor perimeter area_se: Standard error of the tumor area smoothness_se: Standard error of local variation in radius lengths compactness_se: Standard error of perimeter^2 / area - 1.0 concavity_se: Standard error for severity of concave portions of the contour concave points_se: Standard error for number of concave portions of the contour symmetry_se: Standard error for symmetry of tumor fractal_dimension_se: Standard error for "coastline approximation" - 1 radius_worst: "Worst" or largest mean value for mean of distances from center to points on the perimeter texture_worst: "Worst" or largest mean value for standard deviation of gray-scale values perimeter_worst: "Worst" or largest mean value for tumor perimeter area_worst: "Worst" or largest mean value for tumor area smoothness_worst: "Worst" or largest mean value for local variation in radius lengths compactness_worst: "Worst" or largest mean value for perimeter^2 / area - 1.0 concavity_worst: "Worst" or largest mean value for severity of concave portions of the contour concave points_worst: "Worst" or largest mean value for number of concave portions of the contour symmetry_worst: "Worst" or largest mean value for symmetry of tumor fractal_dimension_worst: "Worst" or largest mean value for "coastline approximation" - 1