Short Paper Case Study Analysis Rubric Guidelines For 689840

Short Papercase Study Analysis Rubricguidelines For Submission Short

Short Paper/Case Study Analysis Rubric Guidelines for Submission: Short papers should use double spacing, 12-point Times New Roman font, and one-inch margins. Sources should be cited according to a discipline-appropriate citation method. Page-length requirements: 1–2 pages (undergraduate courses) or 2–4 pages (graduate courses). Critical Elements Exemplary (100%) Proficient (85%) Needs Improvement (55%) Not Evident (0%) Value Main Elements Includes all of the main elements and requirements and cites multiple examples to illustrate each element Includes most of the main elements and requirements and cites many examples to illustrate each element Includes some of the main elements and requirements Does not include any of the main elements and requirements 25 Inquiry and Analysis Provides in-depth analysis that demonstrates complete understanding of multiple concepts Provides in-depth analysis that demonstrates complete understanding of some concepts Provides in-depth analysis that demonstrates complete understanding of minimal concepts Does not provide in-depth analysis 20 Integration and Application All of the course concepts are correctly applied Most of the course concepts are correctly applied Some of the course concepts are correctly applied Does not correctly apply any of the course concepts 10 Critical Thinking Draws insightful conclusions that are thoroughly defended with evidence and examples Draws informed conclusions that are justified with evidence Draws logical conclusions, but does not defend with evidence Does not draw logical conclusions 20 Research Incorporates many scholarly resources effectively that reflect depth and breadth of research Incorporates some scholarly resources effectively that reflect depth and breadth of research Incorporates very few scholarly resources that reflect depth and breadth of research Does not incorporate scholarly resources that reflect depth and breadth of research 15 Writing (Mechanics/Citations) No errors related to organization, grammar and style, and citations Minor errors related to organization, grammar and style, and citations Some errors related to organization, grammar and style, and citations Major errors related to organization, grammar and style, and citations 10 Earned Total 100%

INFO-B Mid-Term Exam October 23rd, 2020 OPEN ON October 23rd, 3:30 pm and Due on October 24th, 3:30 pm Total Points: 50 Images are from Different Resources ALL QUESTIONS ARE COMPULSORY Question 1: True or False [5 points] (i) A Wilcoxon matched -pairs test is used when there are two matched pairs. (ii) The paired t-test is best used when the measurement scale of the characteristic of interest is interval or ratio. (iii) Median is not used for non-parametric test. (iv) If the variances of the two groups being compared are significantly different, then we will always use the independent samples t-test for pooled variances. (v) A researcher is interested in understanding the following question: Do children make more visit to a doctor’s office then adults. For this study the researcher should always use two Independent variables. Question 2: Identify the TEST for the below scenario and explain why you will choose the test for the analysis: [10 points] (a) In an exercise program there are a total 90 women enrolled. The women are in the two age groups (i) 18 to 45 years and (ii) 45 years above. Which test will you use to analyze the weight loss of the women in the two age groups in this exercise program? (b) A researcher is interested to study the behavior of twins in a day care center. For all the twins in the program: One twin was given toys to play and the other twin was given books to read. Which test would you use to understand the behavior of the twins with respect to calmness after one week in the program? There were 15 twins enrolled in the program. (c) You are interested to understand the yearly patients visit to a dentist in a given dental practice. There are total 500 patients visiting the dentist yearly and they are enrolled in either Insurance A or Insurance B. Which test will you use to understand the patients visit to the dentist? Assume the data was not normally distributed. (d) In a hospital for one year the comorbidities of alcoholic and non-alcoholic patients were studied. Which test will be used to understand that alcoholic patients are more susceptible to comorbidities? (e) A researcher is interested to know which parameters in COVID-19 are important to classify patients with respect to disease severity. For this the researcher downloads the patient’s – lab values-demography data. Which test should the researcher implement to find the parameters that associate strongly with COVID disease severity? Question 3: Define any FIVE terms from the given terms: [5 points] (a) Big Data (b) Retrospective Study (c) Systematic Variation Study (d) Stratified Sample (e) Sampling with Replacement (f) Skewness (g) Addition Rule of Probability (h) Sample Space (i) Sampling Distribution (j) Significance Level Question 4: Solve any FIVE from the given questions (a to g): [10 points] (a) For each of the following variables (1 to 5) select the correct measurement scale: Nominal, Ratio, Interval, Ordinal (i) Age in years (ii) Birth Order (iii) Marital Status (iv) Number of years spent in College (v) The number of miles joggers run per week (b) Give one word for the following: (i) A pair of variables related to each other are known as: (ii) A study design that randomly assigns participants into an experimental group or a control group: (iii) Two outcomes that cannot both happen together are known as: (iv) To understand a sample with less than 30 observations which table will you use: (v) When you reject Null Hypothesis even though it is true, this is known as: (c) A student scores 25 in a test. the class of 10 students with mean for the test was 21 and the standard deviation 5. What was the student’s Z score? (d) The average age of patients in a clinical trial was 40 years. The Standard Error was 0.90 years. What is the approximate 95% Confidence Interval for the average age of the patient’s in the clinical trial? (e) Given below is the summary statistics for the cost of a drug purchased by patients from two different stores. Set up and implement a hypothesis test to determine whether on average there is a difference between the drug prices from the two stores. Assume p-value 5. (assume paired test & actual mean under NULL condition is 0). ..26 (f) Given is the outcome of an exercise program. Outcome Total Weight Loss No-Weight Loss Exercise Female Male Use Chi-square analysis to understand Male Weight Loss by joining the Exercise Program. Assume the Critical Value of 3.84. (g) For each of the following figures. ((i) to (vi)) select the figure types: Pearson Correlation Coefficient, bar plot, stem and leaf, Regression, histogram, Normal Distribution, Box plot) (i) (ii) (iii) (iv) (vi) Question 5: Solve both the questions: [10 points] A. A researcher wants to understand the depression scores reported by patients enrolled in two drug trials. The levels of depression were measured next day and midweek after the drug consumption. Which test the researcher should use to understand the effect of the drug on the days? (use Data1.csv) B. (i) Given is the study table tabulated by researcher for patients on a new drug. Compute the odds ratio for survival. Treatments Odds Ratio Drug1 Drug2 Survived Died Question 6: [10 points] Indiana State is interested in understanding the COVID-19 patients’ with respect to its Severity, Mortality, Comorbidities and Parameters from March to September 2020. They have access to Patient data from different Hospitals and testing centers. They have arranged the data in four data tables (Table1, Table2, Table3, Table4). Assume you are a Lead Data Scientist in the State. Using the help of these students, design the methodology that can help the STATE to understand the COVID-19 patients’ in regards to its Severity, Mortality, Comorbidities and Parameters. Table 1: Patient ID, Gender, Birth Date, Race, Parents Alive, Siblings, Education, Income, Alcoholic/Non-alcoholic, Smoker/Non-Smoker, County, Children going to school, Home Zipcode, Date Tested for COVID, Survival Table 2: Patient ID, Oxygen Level, Blood Pressure, Glucose, HbA1C, Basophil Count, Neutrophil Count, Monocyte Count, Albumin, CRP, Protein, Creatinine, eGFR, Pulse, Cholesterol, Weight, Height, Hgb, Lymphocyte Count, Co2 level, Albumin Table 3: Patient ID, Type 2 Diabetes Diagnosed date, Cancer Diagnosed Date- Cancer Name, Autoimmune Disease Diagnosed Date- Autoimmune Disease Name, Neurodegenerative Disease Diagnosed Date- Disease Name, COPD diagnosed Date, Other Disease Table 4: Patient ID, Restaurant Last visited- Name/Zipcode of Restaurant, Living Near Highway, Work from home, Going to Work-Zipcode, Stay at Home Order date start, Stay at Home Order date end, Stage of Lock down, Park visited day- Name/Zipcode of park, Grocery Store visited- Name/Zipcode, Gas Pump Visit Date- Zipcode.

Paper For Above instruction

The submitted case study encompasses a comprehensive framework for analyzing diverse data types and scenarios relevant to healthcare research, statistical testing, and public health data analysis, as well as methodological design tailored for a state-level COVID-19 study. This paper systematically addresses the critical elements of research design, appropriate statistical tests, key terminologies, and data analysis strategies aligned with the given prompts, emphasizing rigorous methodology, valid inference, and evidence-based conclusions.

Main concepts about inferential and descriptive analysis

The foundation of any effective case study analysis involves understanding the distinction between descriptive statistics and inferential statistics. Descriptive statistics summarize and organize data characteristics through measures such as mean, median, mode, and visual displays like histograms and box plots. In contrast, inferential statistics utilize sample data to make generalizations or predictions about a larger population, often employing hypothesis testing, confidence intervals, and regression models. Recognizing when to apply each approach ensures the validity of conclusions when examining health or social science data, such as evaluating patient outcomes or behavioral patterns.

Identification of appropriate statistical tests based on scenarios

Analyzing the specific scenario-based questions demonstrates an essential understanding of statistical test selection grounded in data distribution, measurement scale, and study design. For example, the comparison of weight loss between two age groups in an exercise program involves analyzing differences in means, which would typically employ a t-test. If the data are not normally distributed, a non-parametric alternative such as the Mann-Whitney U test would be appropriate. Similarly, twin behavior analysis utilizing paired data favors paired sample tests like the Wilcoxon signed-rank test, especially with small sample sizes. For categorical data, such as patients' visits or comorbidities, chi-square tests enable assessment of associations without assuming normality. Furthermore, understanding the use of odds ratio and logistic regression in analyzing binary outcomes like survival or disease severity reveals the importance of effect size measurement and risk estimation in epidemiological studies.

Key terminologies in data analysis and research design

Knowledge of fundamental statistical and research terms guides the proper framing of hypotheses and interpretation of results. Big Data refers to extremely large datasets that require advanced computational tools for processing, validation, and analysis. Retrospective studies analyze existing data collected in the past to identify correlations and potential causal relationships, often used in medical research. Systematic Variation Study involves examining consistent differences across groups or conditions to understand factors influencing variability. Stratified sampling divides a population into subgroups (strata) before sampling to improve representativeness. Sampling with Replacement indicates that sampled units are returned to the population for potential reselection, affecting probability calculations. Skewness quantifies asymmetry in data distribution, critical for choosing appropriate statistical tests. The Addition Rule of Probability calculates the likelihood that either of two events occurs, while Sample Space encompasses all possible outcomes in an experiment. Sampling Distribution refers to the distribution of a statistic (like mean) over multiple samples, essential for hypothesis testing. The Significance Level indicates the threshold for rejecting the null hypothesis, commonly set at 0.05.

Analysis of measurement scales and data presentation

Correct classification of measurement scales—nominal, ordinal, interval, and ratio—is fundamental for selecting suitable statistical methods. Age in years is ratio-level data because it has a true zero point and equal intervals; birth order is ordinal because it indicates ranking; marital status is nominal as it categorizes without order; years spent in college is ratio, whereas miles jogged per week is ratio as well. Recognizing these distinctions guides the choice of parametric or non-parametric tests. Visual data summaries, such as histograms for continuous variables, bar plots for categorical data, and box plots for distribution analysis, facilitate understanding data characteristics and detecting outliers or skewness.

Application of hypothesis testing and statistical calculations

The paper illustrates critical calculations, including Z-scores, confidence intervals, and hypothesis testing procedures. For example, calculating a student's Z score involves subtracting the mean from the observed value and dividing by the standard deviation, thereby standardizing the score to relate it to the normal distribution. Confidence intervals estimate the range within which the true population parameter resides with a specified level of confidence, here approximately 95%, using the standard error and critical values. Hypothesis testing for the difference in drug prices employs Z-tests for comparing means under assumptions about variance and distribution, considering p-values and critical values like 5.84 for significance. Chi-square tests assess associations in categorical data, such as gender and weight loss, with the critical value guiding decision thresholds. Moreover, the identification of statistical figure types enhances comprehensive data visualization, aiding in pattern recognition and hypothesis validation.

Designing a methodology for a complex COVID-19 data analysis

The multi-table dataset from Indiana State offers a challenging yet critical opportunity for comprehensive COVID-19 patient analysis. A logical methodology involves preprocessing and integrating tables based on Patient IDs, followed by exploratory data analysis to identify distributions, missing data, and correlations. The initial step includes data cleaning, imputation for missing values, and variable transformation where necessary (e.g., calculating BMI from weight and height). Subsequently, descriptive statistics help understand the baseline characteristics of patients, stratified by severity or mortality status.

Next, multivariate analyses such as logistic regression models are suited to assess the association between demographic factors, comorbidities, clinical parameters, and outcomes like severity and death. Stratified analysis by age groups, income, or comorbidities helps uncover vulnerable populations. Machine learning approaches, including decision trees or random forests, can identify parameters most predictive of disease severity. Network analysis might elucidate relationships between parameters such as blood markers, demographic factors, and disease outcomes. Cross-validation and ROC curve analysis evaluate model performance. Finally, the interpretation of statistical outputs from these models informs public health strategies and resource allocation, providing insights into risk factors that influence COVID-19 progression and patient prognosis.

Conclusion

This analysis synthesizes statistical theories, research methodologies, and practical applications essential for health data analysis and public health decision-making. It underscores the importance of choosing appropriate tests based on data scale and distribution, understanding key terminologies, and designing robust analysis pipelines. The comprehensive approach detailed in this paper illustrates how data-driven insights can inform clinical and policy interventions, especially in pandemic contexts like COVID-19. Through rigorous quantitative analysis and clear methodological frameworks, researchers and policymakers can improve health outcomes and optimize resource utilization, ultimately enhancing the effectiveness of health responses and scientific research.

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