Multiple Regression :: Gender

IntroductionFor this study researchers were interested in assessing whether self-reported health behavioursand health literacy atomic number 18 able to predict self-rated physical health, after imperative for the effects ofgender and age. They are further interested in knowing which of the variables provide astatistically significant contribution to the equation.Also of interest to the researches was the interaction between gender and health literacy, that is,the degree to which individuals are able to obtain, process and understand the information neededto make appropriate decisions about their health, and the impact of this interaction on health. entropy was collected from 350 people randomly selected from a dataset from a population-basedstudy of health and health determinants. Health was measured on a outmatch of 1 to 10, where higherscores represent better health. Health behaviours include healthy diet, physical activity andrelaxation and are measured on a scale from 1 to 1 5. Health literacy is measured on a scale from10 to 45. Gender and age in years were also collected from the respondents.Data Screening & Assumption TestingThe initial step in this data abridgment involved screening the data for possible missing values, out of snip values, univariate and multivariate outliers and multicollinearity. Three variables used forthis study contained missing values both system and identified missing. These variables werehealth literacy, physical activity and age in years, sensation case 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 trim 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 hyaloplasm for all variables(Figure 1), and plots of Cooks distan ces (Figure 2) and Mahalanobis distances (Figure 3). Thereare no cases which indicate a particular cause for concern. On the Mahalanobis distance chartthere 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 check the regression assumptions 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 genuine for the data in this study. The secondassumption is that each observation in the sample is independent of the other observations, the