Multiple Regression :: Gender

IntroductionFor this study researchers were interested in assessing whether self-reported wellness behavioursand health literacy be able to predict self-rated physical health, after controlling for the set up ofgender and age. They are further interested in knowing which of the variables set up astatistically significant contribution to the equation.Also of interest to the researches was the fundamental interaction between gender and health literacy, that is,the degree to which individuals are able to obtain, suffice and understand the information neededto make appropriate decisions about their health, and the bear upon of this interaction on health.Data was collected from 350 people randomly selected from a dataset from a population-basedstudy of health and health determinants. Health was mensurable on a exfoliation of 1 to 10, where higherscores represent go bad health. Health behaviours include healthy diet, physical activity andrelaxation and are measured on a scale from 1 to 15. Health literacy is measured on a scale from10 to 45. Gender and age in years were also collected from the respondents.Data Screening & Assumption examinationThe initial step in this data analysis involved wake the data for possible lacking determines, out of carry values, univariate and multivariate outliers and multicollinearity. triplet variables used forthis study contained missing values both system and determine missing. These variables werehealth literacy, physical activity and age in years, one nerve for each of these variables. Each ofthese missing values were recoded with a missing value code of 999. Descriptive statisticsproduced for each of the variables used for the analysis revealed out of range values for thevariables healthy diet, physical activity and relaxation. These values were also recoded to themissing value code 999.Testing for the presence of outliers was done by generating a scatterplot matrix for all variables(Figure 1), and plots of Cooks dist ances (Figure 2) and Mahalanobis distances (Figure 3). Thereare no cases which indicate a particular cause for concern. On the Mahalanobis distance graph at that place are no cases that is substantially larger than the rest and on the Cooks distance there is nocase with a distance above 1 which would indicate an influential point. Multicollinearity was testedand there were no variables with a tolerance of less than 0.3.It is also necessary to notice the regression surmisals to ensure that any results from analysisare valid. The first assumption is that all variables are measured on a metric scale or thatcategorical variables are dichotomously coded. This is true for the data in this study. The secondassumption is that each observation in the sample is separatist of the other observations, the