Discuss steps that should have been taken to self-correct the market failure and steps that are being taken to eliminate the need of another government bailout in the future. Is your chosen industry currently operating efficiently? Locate a recent article or event (published within the last year) that highlights your relevant microeconomics topic. Use the Hunt Library, newspapers, new stations, or other credible sources to discuss how your topic aligns with microeconomics. Include the following in your discussion: State the article or event you selected. Identify the microeconomic concept(s). Describe your findings. Analyze the relevance to real-life applications. Summarize your findings  

Overview and RationaleIn order to consolidate your theoretical knowledge into technique and skills with practical and applicational value, you will use the glmnet() package in R to implement Ridge and LASSO functions to build linear and logistic models through Ridge and LASSO regression over values of the regularization parameter lambda.Course OutcomesThis assignment is directly linked to the following key learning outcomes from the course syllabus:Conduct regularization method for models to describe relationships among variables and make useful predictions.Assignment SummaryUse the College dataset (https://rdrr.io/cran/ISLR/man/College.html) from the ISLR library to build regularization models by using Ridge and Lasso (least absolute shrinkage and selection operator). Predict Grad.Rate for all models.Split the data into a train and test set – refer to the Feature_Selection_R.pdf document for information on how to split a dataset.Ridge RegressionUse the cv.glmnet function to estimate the lambda.min and lambda.1se values. Compare and discuss the values.Plot the results from the glmnet function provide an interpretation. What does this plot tell us?Fit a Ridge regression model against the training set and report on the coefficients. Is there anything interesting?Determine the performance of the fit model against the training set by calculating the root mean square error (RMSE). sqrt(mean((actual - predicted)^2)).Determine the performance of the fit model against the test set by calculating the root mean square error (RMSE). Is your model overfit?LASSOUse the cv.glmnet function to estimate the lambda.min and lambda.1se values. Compare and discuss the values.Plot the results from the glmnet function provide an interpretation. What does this plot tell us?Fit a LASSO regression model against the training set and report on the coefficients. Do any coefficients reduce to zero? If so, which ones?Determine the performance of the fit model against the training set by calculating the root mean square error (RMSE). sqrt(mean((actual - predicted)^2)).Determine the performance of the fit model against the test set by calculating the root mean square error (RMSE). Is your model overfit?ComparisonWhich model performed better and why? Is that what you expected?Refer to the Intermediate_Analytics_Feature_Selection_R.pdf document for how to perform stepwise selection and then fit a model. Did this model perform better or as well as Ridge regression or LASSO? Which method do you prefer and why?ReportRefer to the attached rubric for more details on the report. The report should contain a well written cover/title page, introduction, body, conclusion, and references. It must follow APA format and have at least 1000 words (excluding title page and references page. All R code used for your report should be included in an appendix at the end of the report.Graphs, figures, charts, and tables are very useful visual effects to communicate your results and impress your readers. However, such items should not be included in the report unless they are well described and interpreted. Please use subtitles to make your assignment more reader friendly as well.Format & GuidelinesThe report should follow the following format:Title pageIntroductionAnalysisConclusion/InterpretationsReferences

Access the U.S. Department of Health and Human Services Office of Minority Health (2016) provided in this week’s reading :https://minorityhealth.hhs.gov/omh/browse.aspx?lvl=2&lvlid=26Select three of the five populations provided in the database, and compare profiles for these three populations.Reflect on how the three populations you selected might approach the management of heart disease.Reflect on heart disease risk factors for the three populations you selected.Consider how access to healthcare, Medicare, or Medicaid programs may impact heart disease and health outcomes for these populations.Part 1: Heart Disease Comparison Grid the Week 2 Matrix Template document attachedComplete the template by addressing the following:Describe the demographics for each of the three populations you selected. Be specific and provide examples.Explain at least three heart disease risk factors that may impact each of the three populations you selected. Be specific, and provide examples.Describe which social determinants of health most impact each of the three populations you selected for the management of heart disease, and explain why. Be specific, and provide examples.Part 2: Narrative SummaryIn a 2-page narrative summary, address the following:Explain what conclusions you might draw, regarding the management of heart disease and chronic illnesses for the populations you selected.Explain how healthcare access and health insurance programs (including Medicare and Medicaid) might help to address the heart disease risk factors and the social determinants of health for each of the populations you selected.