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QSO 570 SNHU Patient Children and Patient Mental Health Status Essay

QSO 570 SNHU Patient Children and Patient Mental Health Status Essay



You are a data and business intelligence analyst working with a network of hospitals in rural districts. The hospital’s chief of behavioral health services (CBHS) would like to use existing research data to create a model to predict or classify newer patients as at-risk or not at-risk for clinical depression. Such classification predictions will enable the hospital to provide early mental health interventions to at-risk individuals. The CBHS has tasked your team with creating the predictive classification model and then testing the model on five new patients at the hospital.

So far, you have applied different predictive algorithms on the data set and presented your findings to the CBHS. Now, you want to visualize and analyze the results further and determine the fit and accuracy of the model.

In this assignment, you will produce visualizations of your regression model in Excel. Then, you will summarize your findings about the model to your team of analysts, explaining any problems you see in the model and how to reduce errors and make a better model. You will also write an executive summary to stakeholders describing the fit of your model and how it will benefit the organization.

For this assignment, you will use the output values from the logistic regression you performed in Module Two using Excel. Ensure that you have the final regression results, including any updates you may have made based on your instructor’s feedback.


Write two executive summaries to describe the fit and usefulness of your regression model to two different audiences, your analyst team and executive business stakeholders. Your summaries will use the same visualizations, but you should customize your interpretation for the audiences based on their role in the organization. Include relevant screenshots from Excel.

Specifically, you must address the following criteria:

  1. Summary for Analyst Team
    • Scatterplot: Create a scatterplot to show the relationship between the most important variable and the prediction score in the regression model.
      • Create a scatterplot of the most important variable and the corresponding risk of depression in patients.
        • Identify one variable that is most influential, in addition to the prediction score.
        • Create a scatterplot using the output from the regression model.
      • Explain why you chose this variable to show the scatterplot.
    • Regression Trend Line: Create and interpret the regression trend line for the scatterplot. Include relevant screenshots from Excel.
      • Create a linear trend line, and display the equation of the regression on the chart
      • Explain what the trend line and its equation tell you about your model and its performance.
      • Compare the r2 value for the trend line with the r2 in the results from your regression model.
        • Why might they be different? Explain.
    • Summary: Assess the overall fit of the model.
      • Explain whether this model is acceptable. Why or why not?
      • Identify problems with the model.
        • Recommend ways to reduce error and make a better model to predict risk of depression in future patients. Explain.
  2. Summary for Stakeholders
    • Regression Trend Line: Interpret the graph of the regression trend line with the equation of regression. Include the relevant screenshot.
      • Explain how you chose the variable for your graph and trend line.
        • What does this variable indicate about the risk factors of depression?
      • Explain what the trend line and its equation tell you about your model and its performance.
    • Summary: Explain the overall fit of the model for solving the business problem.
      • Explain why you have reasonable confidence in the model.
      • Recommend ways to make the model more accurate.
        • What data analytics process or techniques can help you improve the model?
        • Are there any business processes or additional data that might help improve accuracy?
      • How can this model be used to benefit the hospital and its proposed programs?

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