Multiple Regression :: Gender

IntroductionFor this study researchers were interested in assessing whether self-reported health behavioursand health literacy be able to predict self-rated physical health, aft(prenominal) controlling for the effects ofgender and age. They are further interested in knowing which of the variables provide astatistically significant contribution to the equation. in addition 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 clutch decisions about their health, and the impact of this interaction on health.Data was collected from 350 peck randomly selected from a dataset from a population-basedstudy of health and health determinants. wellness was measured on a scale of 1 to 10, where higher gain ground represent better health. Health behaviours include healthy diet, physical action at law andrelaxation and are measured on a scale from 1 to 15. Health literacy is measured on a scale from10 to 45. sexual urge and age in years were in like manner collected from the respondents.Data coating & Assumption TestingThe initial step in this data abridgment involved screening the data for possible missing set, out of telescope 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, one 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 compend 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 do by generating a scatterplot matrix for all variables(Figure 1), and plots of C ooks distances (Figure 2) and Mahalanobis distances (Figure 3). in that respectare no cases which indicate a particular cause for concern. On the Mahalanobis distance chartthere are no cases that is substantially large than the rest and on the Cooks distance there is nocase with a distance above 1 which would indicate an prestigious 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 straightforward for the data in this study. The secondassumption is that each observation in the sample is independent of the other observations, the