Question

The following question is based on scenario 2: Researchers are interested in seeing how sitting for long periods of time can influence cardiovascular health. They ask people how many hours per day they sit, and then they measure their blood pressure to see if they are associated. They expect that as hours spent sitting increases, blood pressure will also increase. Question 6 1 pts What is the best statistical test to use to analyze the hypothesis in scenario 2? Z-score z-test Structural Equation Model Factor Analysis Correlation Coefficient Dependent sample t-Test Independent sample t-Test One-way ANOVA

Answer 1

Question 6
The answer is correlation coefficient and t-test to measure the correlation coefficient because it measure association and the hour sitting increases as blood pressure increases indicate a positive correlation which forms a direct proportionality. So the answer is Correlation coefficient and the test of the correlation is t-test.

Question 14
The study am coming up with is: "Age as a determinant of life expectancy". Age is the independent variable here while life expectancy is the dependent variable. Also both are continuous variable. However, the suitable statistical tools to apply is regression analysis because we want to use age to predict the life expectancy. Our hypothesis will be

Null hypothesis: there is no relationship between age and life expectancy

Alternative hypothesis: there is a relationship between age and life expectancy.

Decision: if the P-value of the overall regression is less than alpha (which is the level of significance and can be 0.01 or 0.05 e.t.c), we reject the null hypothesis and that implies that there is a significant relationship between age and life expectancy. Also we also check the R-square which is the coefficient of determination that measure the percentage variation in the dependent variable (life expectancy) explained by the independent variable (age).