Copy of End of Module B1 Quiz

The correlation coefficient (r):

In the context of simple linear regression, consider the following scenario: The relationship between height (cm) and weight (kg) was studied in 100 women aged 35-40 years. The following regression equation was obtained:

Weight (kg) = -72.05 + 0.82 x height (cm)

What does this equation imply?

[In reference to the previous Simple Linear Regression question]

If a woman is 160 cm tall, what weight would the model predict?

A fictitious study was conducted among 500 male weekly drinkers to investigate the relationship between systolic blood pressure (SBP) and alcohol consumption level. SBP was measured in mmHg and recorded by variable called sbp_mean. Based on their self-reported alcohol consumption, participants were categorised into 4 alcohol categories (wkcat) (wkcat1: 1-140 g/week; wkcat2: 140 – 279 g/week; wkcat3: 280 – 419 g/week; wkcat4: 420 + g/week).

The association between SBP and alcohol consumption was investigated using ANOVA. Based on the following ANOVA output, what can you conclude?

[In reference to the Linear Regression & ANOVA question]

A simple linear regression model was used to investigate the relationship between SBP and alcohol consumption using the same dataset. What conclusion(s) can be drawn from the output shown?

Based on data from 212 study volunteers aged 13-27 years, it has been estimated that peak nasal inspiratory flow can be estimated by the following regression equation:

Peak nasal inspiratory flow (l/min) = 1.4256 x height (cm) + 33.0215 x gender (where 0=female and 1=male) + 1.4117 x age (years) - 136.6778

The intercept of the multiple regression model provides an estimate of:

Referring to the same regression model:

Peak nasal inspiratory flow (l/min) = 1.4256 x height (cm) + 33.0215 x gender (where 0=female and 1=male) + 1.4117 x age (years) - 136.6778

If gender were to be recategorized as 1=female and 0=male:

Referring once more to the same regression model:

Peak nasal inspiratory flow (l/min) = 1.4256 x height (cm) + 33.0215 x gender (where 0=female and 1=male) + 1.4117 x age (years) - 136.6778

Which of the following is the correct interpretation of the model regression coefficients?

Which of the following is not a required model assumption for linear regression?

Regarding linear regression, if the assumption of homogeneity in variance (i.e. homoscedasticity) of the residuals is satisfied, in a plot of the residuals against the fitted (predicted) values we should see: