Multiple Regression Analysis: Student's Mental Health and Number of Mental Health Workers (Essay Sample)

Multiple Regression Analysis: Mental Health Workers and Students’ Mental Health Wellness Student’s Name Institutional AffiliationCourse Instructor Date Multiple Regression Analysis: Mental Health Workers and Students’ Mental Health Wellness One of the key approaches to evaluate how mental health workers impact students’ mental health wellness is through a multiple regression analysis. The method comprises a statistical approach used to examine the relationship between a dependent variable, such as the outcome or response variable, and two or more independent variables (Mizumoto et al., 2023). The critical functionality of the multiple regression method is to extend the simple linear regression, which encompasses the assessment of one dependent variable. Thus, by employing the multiple regression approach, the key goal of the analysis is to underscore how different factors could collectively impact the dependent variable (Chen et al., 2020). In assessing the impacts of healthcare workers on the mental wellness of high school students, several independent variables could be analyzed to analyze such outcomes as rates of depression and anxiety cases, comprising the most reported mental healthcare issues in school settings. On the one hand, the independent variables, consisting of the predictor variables, could include the key attributes of the mental health workers, noting how, collectively, they impact the outcomes, which include the depression and anxiety rates reported among the students. Therefore, in the analysis, the elements that will be assessed as part of the independent variables will include the number of mental health workers, qualifications, and frequency of the services. Under this segment of independent variables, the number of workers will represent the presence and staffing levels of the mental health professionals. The second element, comprising qualifications of mental health workers, will include the level of qualification and expertise of each worker. Lastly, the frequency of mental health services will consider how often the students receive the services within the school premises. Student Number of M. Health Workers Qualifications Frequency of Services 1 3 Licensed Therapist Weekly 2 4 School Counselor Bi-Weekly 3 1 Counselor Trainee Monthly 4 3 Licensed Therapist Weekly 5 2 Psychologist Bi-weekly 6 4 Licensed Therapist Weekly 7 2 School Counselor Monthly 8 5 Psychologist Bi-Weekly 9 3 Licensed Therapist Weekly 10 2 Counselor Trainee Bi-Weekly 11 4 Psychologist Monthly 12 1 Licensed Therapist Weekly 13 2 School Counselor Bi-Weekly 14 4 Psychologist Weekly Based on the dataset provided, it is possible to interpret the statistical significance and size of the regression coefficients. The fundamental goal of this process is to develop relevant insights into interpreting provided independent variables. Thus, the statistical significance and size of the regression of the coefficients in the data could be evaluated as follows. Depression Scores =b0 +( b1* number of workers) + (b2 * Qualifications) = (b3* Frequency of services). Based on the provided string, the statistical significance and size of the regression coefficients could be assessed as follows. The intercept (b0) represents the estimated rate of depression and anxiety score when the independent variables are valued at zero. The second coefficient (b1) comprises the changes recorded in the rate of depression and anxiety while all the other variables are constant. In the third element, consisting of the coefficient of qualifications (b2), the coefficient represents the change in the rate of depression and anxiety when the other factors are held constant. Lastly, the coefficient of frequency of services (b3) encompasses the scores when the rest are constant. The input of the dataset is in R, and consequently, a variable coding was conducted. The categorical variables were consequently coded for the qualifications in the evaluation process. More specifically, 1 (Licensed Therapist), 2 (Counselor trainee), 3 (Psychologist), and 4 (School Counselor). Consequently, a multiple regression analysis was conducted with the Y values, including the scores of rates of depression and anxiety. At the same time, the X-axis dataset comprised the number of workers, qualifications, and frequency of services. Consequently, the interpretation was interpreted, noting the relationships. Herein, the p-values associated with the coefficients were assessed in their statistical significance p<0.05: was considered statistically significant. The results of the coefficient were as follows. B1 (Number of Workers) was 0.2 B2 (Qualifications) was 0.15 B3 (Frequency of Services) was 0.25 The fit of the Regression Model in Data Analysis It is essential to underscore ways the multiple regression approach is a fit model for interpreting the rate of depression and anxiety based on the provided dataset. One of the critical approaches in which the fit could be interpreted is using the R-squared (R2) measure. This approach is essential in analyzing the variations that exist between the dependent variable, which includes the rate of depression and anxiety in students as relative to the elements of independent variables. Thus, based on this consideration, if R2 is close to 1, it is possible to deduce that the approach is a good fit (Meda et al., 2021). Another critical approach that can be utilized in evaluating how fit the multiple regression method is in assessing the dataset is the examination of the p-values relative to the coefficients (b0, b1, b2, and b3). Based on this analysis aspect, if the p-value is less than 0.005, which is the significance level or threshold, the coefficient can be considered significant. Nonetheless, if the p-value is greater than the significance level, the coefficient can be considered as not statistically significant. Therefore, considering the evaluation models that could be used in the multiple regression assessment, it is evident that the method...