Correlation analysis is the most appropriate statistical test to use in this scenario as it assesses the strength and direction of the relationship between two continuous variables, such as age and blood pressure. Student’s t-test and ANOVA are used to compare means between two or more groups, respectively, and are not appropriate for assessing the relationship between two continuous variables. Chi-square test is used for analyzing categorical data, while regression analysis is used to estimate the relationship between a dependent variable and one or more independent variables.

The absolute risk reduction is 5%, which means that for every 100 individuals treated with the new drug, 5 fewer heart attacks will occur compared to placebo.

The relative risk reduction is 30%, which means that the risk of heart attack is reduced to 70% of the risk with placebo (100% – 30% = 70%).

The number needed to treat (NNT) can be calculated as the reciprocal of the absolute risk reduction:

NNT = 1 / absolute risk reduction

NNT = 1 / 0.05 = 20

Therefore, the answer is 20. It means that 20 individuals need to be treated with the new drug to prevent one heart attack.

The attributable risk (AR) is the difference between the incidence of diabetes in the exposed group and the incidence in the unexposed group.

AR = incidence in exposed group – incidence in unexposed group

AR = 10% – 5% = 5%

Therefore, the answer is 5%. The risk factor is responsible for a 5% increase in the incidence of diabetes.

Relative risk is the most appropriate measure of association to use in this study because it directly measures the risk of developing lung cancer among smokers compared to non-smokers. Odds ratio and hazard ratio can also be used in certain circumstances, but relative risk is the most commonly used and straightforward measure in studies of this nature. Prevalence ratio and incidence rate ratio are not appropriate for this study as they are measures of disease burden rather than measures of association between a risk factor and an outcome.

The number of individuals who do not have the disease in the population is 1,000 – 100 = 900.

Since the sensitivity of the test is 90%, 90 out of 100 individuals with the disease will test positive.

Therefore, the number of individuals who test positive among those who have the disease is 90.

Since the specificity of the test is 80%, 20% of the 900 individuals who do not have the disease will test positive.

Therefore, the number of individuals who test positive among those who do not have the disease is 900 x 0.20 = 180.

Thus, the answer is 180.