Copyright by Radhika Anantharaman Nair 2005 - The University of ...

Ordinary least squares regression was used to determine the relationship betweenthe independent variables and monthly depression-related physician visits. The analysisyielded an adjusted R-square of 0.038, but none of the variables in the model weresignificant.To further evaluate the relationship with age, the variable percentage ofindividuals 45 years and older with depression-related visits was replaced with threevariables which measured the percentage of individuals with depression-related visits andwere 25-44 years, 45-64 years and 65 years and older. Again, none of the variables weresignificant.Time Series Analyses: Split by Time (Objective IV)To determine if any of the relationships were significant in time period one werealso significant in time period two, the dataset was split by time period (January 1994 toAugust 1997, September 1997 to April 2001). The time series analysis for both timeperiods with untransformed and transformed (Logarithmic) values for depression-relatedvisits and total DTCA expenditures for antidepressants yielded the same result and theresults from the analysis with untransformed variables are reported.The critical interval for positive autocorrelation was identified as 1.336 to 1.720and for negative autocorrelation above 2.664 (4.0-1.336=2.664). The Durbin-Watsonstatistic for time period one and two were calculated to be 2.093 and 2.017, respectively.The null hypothesis of no serial correlation present in error term for both time periodswas not rejected. On regressing the independent variables on depression-related, none ofthe variables were significant in either time period (Table 5.48).360