- What are the overall findings and conclusions from #6, #7, #8 and #9? (given answering the findings)
7. How and how well age of patients predict discharge from the hospital? (Is there is a statistically significant relationship between age and days to discharge? If so, what is the nature and degree of relationship?) (4 points) Variable 1: Age of patients Variable 2: Number of days to be discharged from the hospital Ho:ul=u2 ( Null hypothesis)- There is no statically significance difference relationship between age and days of discharge. Hi:ul #u2 ( Alternative Hypotheses) There is statically significance difference relationship between the age and days of discharge. Statistical analysis used: Pearson Key statistics: A. Correlation Coefficient: r= 0.14 positive correlation B. Statistical significance- p=.01 C. Coefficient of determination/Effect size/Shared Variance- .14x.14=.14 (14%) The coefficient of determination is the square of correlation coefficient. It results as the proportion of the variance in the dependent variable that is predictable from the independent variable. Assumptions: A. Significant Outliers- No significant outliers B. Linearity- r=0.14 the relation between those two variables are positive linear because it shows on the graph a positive straight line. C. Normality- Shaprio-Wilk test of normality of age is .060. Shaprio-wilk test of covid 19 patients days to discharge from the hospital is .000. Accept or reject the Ho: Reject because the p value is less than .05 Summary and conclusion: Since p- value is (0.01) is less than (0.05), we reject the null hypothesis, there will be statistically significance difference Relationship between the age and days of discharge. Given your decision to accept or reject the Ho, which of the two errors are you likely to be committing, and why? This could be a type 1 error because we are rejecting the null hypothesis when there could be no significance relationship between the age and days of discharge.18. Do male and female patients differ in how long they take to discharge from the hospital? If so, how do they differ and by how much do they differ? (Is there a statistically significant difference in days to discharge between male and female patients?) (4 points) Independent variable: Male and female Patient (sex) Dependent variable: Recovery time, number of days to be discharged from the hospital. Ho:ul=u2 ( Null hypothesis) There is no statically significance difference between the days to discharge between male and female patients. Hi:ul # 2 ( Alternative Hypotheses) There is statically significance difference between days of discharge between male and female patients (sex). Statistical analysis used: Pearson Key statistics: A. Correlation Coefficient- For sex .99 and for discharge covid 19 patients is .99 which in indicates strong positive relationship. B. Statistical significance- p=.01 C. Coefficient of determination/Effect size/Shared Variance- .99x.99=.99 (99%) Assumptions: A. Significant Outliers- No significant outliers B. Linearity- r=0.99 C. Normality- Patient days from discharge from the hospital is .000 and the sex is .000 Accept or reject the Ho: Reject because the p value is less than .05 Summary and conclusion: Since p-value (0.01) is less than (.05) we reject the null hypothesis there is statistically significance difference between days of discharge between male and female patients. Given your decision to accept or reject the Ho, which of the two errors are you likely to be committing, and why? This could be type 1 error because we are rejecting the null hypothesis when there could be no statistically significance difference between day of discharge between make and female patients.Do patients differ in days to discharge from the hospital based on their race and ethnic background? If so, how do they differ and by how much do they differ? (Is there a statistically significant difference in days to discharge between patients from various race and ethnic group?) (4 points) Independent variable: Race and ethnic background Dependent variable: recovery time, patients differ in days to discharge from the hospital. Ho:ul=u2 ( Null hypothesis)- Theres is no statiscally significance difference between days to discharge between patients from hospital and race and ethnic group. Hi:ul # u2 ( Alternative Hypotheses)- There is statically significance difference between days to discharge between patients from hospital and race and ethnic group. Statistical analysis used: Pearson Key statistics: A. Correlation Coefficient- The correlation Coefficient for Race Ethnicity is .140 and for covid 19 patients days to discharge from the hospital is .140 B. Statistical significance- p=.01 C. Coefficient of determination/Effect size/Shared Variance- .140x.140=.140 (140%) Assumptions: A. Significant Outliers- No significant outliers B. Linearity- r= 0.140 C. Normality- The normality of race and ethnic background is .000 and the normality for patients days from discharge from the hospital is .000. Accept or reject the Ho: Reject the null hypothesis because the p value is less than .05 Summary and conclusion: Since p value .01 is less than .05 we reject the null hypothesis there is statistically significance difference between days to discharge between patients from hospital and race and ethnic group. Given your decision to accept or reject the Ho, which of the two errors are you likely to be committing, and why? Type 1 error because we are rejecting the null hypothesis when there could be no statistically significance difference between days to discharge between patients from hospital and race and ethnic group.6. How effective are three drugs/treatments in treating inflammation, therefore helping them recover from Covid-19? In other words, which drug/treatment group take longest and shortest to discharge from the hospital? (Is there a statistically significant difference in number of days Covid-19 patients take to discharge from the hospital based on drugs/treatments? If so, how do they differ and by how much do they differ?) (4 points) Independent variable: Treatment with 3 randomly assigned group of COVID 19 patients (drug/treatment) Dependent variable: Is recovery time, the number of days COVID 19 patients take to be discharged from the hospital. Ho:ul=u2 ( Null hypothesis) Theres no statistically significance difference relationship between number of days COVID 19 patients take to discharge from the hospital and drug/treatment. Hi:ul # u2 ( Alternative Hypotheses) There will be statistically significance difference between the number of days Covid 19 patients take to discharge from the hospital and drug/treatment. Statistical analysis used: Pearson Key statistics: A. Correlation Coefficient- r= -.71 strong negative correlation B. Statistical significance- p=.01 C. Coefficient of determination/Effect size/Shared Variance- -.71x-.71=.71 (71%). The coefficient of determination is the square of correlation coefficient. It results as the proportion of the variance in the dependent variable that is predictable from the independent variable. Assumptions: A. Significant Outliers- No significant Outliers B. Linearity-r=-0.715, the relationship between the two variables are linear as shown in the graph. C. Normality- Shaprio- Wilk test of normality of drug treatment with covid patient to discharge from the hospital is .000. Patients with Tocilizumab treatment is .013. Patients with Sarilumb treatment drug is .033 days to discharge from the hospital. Accept or reject the Ho: Reject the null hypothesis because the p value is .01 is less than .05. Summary and conclusion: Since p- value is (0.01) is less than (0.05), we reject the null hypothesis, there will be statistically significance difference between the number of days COVID 19 patients take to discharge from the hospital and drug/ treatment. Given your decision to accept or reject the Ho, which of the two errors are you likely to be committing, and why? This could be type 1 error because we are rejecting the null hypothesis when it could be true
