BioStat Lab

• Data is assumed to be normal with more than 30 observations.
• Criteria to check if T-test is best to be used in data include thick-tailed/ skewed (invalid) and different categories.
• The mean difference between the Peak expiratory flow in pre test and post test is 24.951 L/min.
• The value of .896 shows a positive correlation between the 2 groups.
• There is a significant difference between the peak flow of the asthma patients before treatment and the peak flow of the asthma patients after treatment, t(39) = - 9.272, p = .000.
• Correlation analysis is used to test the relationship between qualitative or quantitative variables.
• Correlation analysis is a statistical method to discover the association between two continuous variables and to check how strong their relationship may be.
• Correlation analysis is used to spot patterns within data sets.
• Pearson - Product Moment Correlation Coefficient is a measure of the strength of a linear association between two variables and is denoted by r.
• The maternal mortality rate in the United States is usually expressed per 100,000 live births.
• The healthy worker effect states that people who work are more likely to be healthier than the population as a whole.
• The infant mortality rate in the United States is generally calculated on an annual basis.
• The neonatal period covers birth up to but not including 28 days.
• The maternal mortality rate is a ratio used to measure mortality associated with pregnancy.
• The neonatal mortality rate is usually expressed per 1,000 live births.
• Workers with a high injury-related proportionate mortality very likely have lower proportionate mortalities for chronic or disabling conditions that keep people out of the workforce.
• The infant mortality rate is most commonly used measure for comparing health status among nations.
• The postneonatal mortality rate is defined as the period from 28 days of age up to but not including 1 year of age.
• The death to case ratio is the number of deaths attributed to a particular disease during a specified time period divided by the number of new cases of that disease identified during the same time period.
• Advantages of Pearson correlation coefficient include a simple way to assess association between two variables and it cannot identify relationships that are not linear.
• Disadvantages of Pearson correlation coefficient include it may show a correlation of zero if not linear and it may not be able to identify whether the relationship is positive or negative.
• In Regression analysis, each variable has a well-defined role.
• Assumptions for Regression analysis include the dependent and independent variable should be measured at the continuous level, there should be a linear relationship between the two variables, there should be no significant outliers, there should be independence of observations, the data needs to show homoscedasticity, and the residuals (errors) of the regression line should be approximately normally distributed.
• Steps in SPSS for predicting response Y from value of predictor X include clicking Analyze > Regression > Linear, transferring the independent variable (income) into the Independent(s) box, and the dependent variable (price) into the dependent box, changing the options for the Statistics and Plot buttons, and clicking OK to generate the results.
• Outputs in SPSS include a table providing the R and R2 values, indicating how well the regression equation fits the data, and indicating that the regression model predicts the dependent variable significantly well.
• The length of hospital stay could be predicted from the severity of the operation.
• In Laboratory Correlation Analysis, the salesperson for a large car brand uses SPSS to determine whether there is a relationship between an individual's income and the price they pay for a car.
• To predict future economic conditions, trends, and values, variables such as X, Y, and Z play crucial roles.
• Examples of predicting response Y from value of predictor X include understanding whether exam performance can be predicted based on revision time, understanding whether cigarette consumption can be predicted based on smoking duration, and understanding whether blood pressure can be predicted based on drug dosage.
• Types of variables that can be evaluated using Pearson correlation coefficient include continuous variables.
• Pearson correlation coefficient, r, can take a range of values from +1 to - 1.
• Negative Predictive Value (NPV) is calculated as (TN(D))/(TN(D)+FN(C)
• Specificity is calculated as (TN(D))/(TN(D)+FB(B))
• Sensitivity in Example 2 is 0.96.
• Specificity in Example 2 is 0.97.
• Specificity in this case is 0.906.
• Sensitivity is calculated as (TP(A))/(TP(A)+FN(C))
• Positive Predictive Value (PPV) in Example 2 is 0.923076923…
• Sensitivity in this case is 0.961.
• The provider found that a total of 384 individuals actually had the disease, but how accurate was the blood test?