The cycling data frame contains the results of a study on the effects of cycling to work among 1,000 participants with asthma, a respiratory illness. Half of the participants, chosen uniformly at random, received a monetary incentive to cycle to work, and the other half did not. The variables in the data frame are:
miles: the average number of miles cycled per week
episodes: the number of asthma episodes experienced during the study
incentive: whether or not a monetary incentive to cycle was given
history: the number of asthma episodes in the year preceding the study
Consider the R code below and its abbreviated output.
>lm.1=lm (episodes miles + history, data=cycling)
>summary(1 lm.1)
Coefficients:
Estimate Std. Error t value Pr(>∣t∣)
(Intercept) 0.669370.079658.404<2e−16∗∗∗
miles −0.049170.01839−2.6740.00761∗∗
history 1.489540.0481830.918<2e−16∗∗∗
>lm.2=lm( episodes incentive + history, data=cycling)
> summary (lm.2)
Coefficients:
Estimate Std. Error t value Pr(>∣t∣)
(Intercept) 0.095390.069601.3710.171
incentiveYes 0.913870.0650414.051<2e−16∗∗∗
history 1.468060.0434633.782<2e−16∗∗∗
>lm.3=lm( miles incentive + history, data=cycling)
>summary(lm.3)
Coefficients :
Estimate Std. Error t value Pr(>∣t∣)
(Intercept) 1.470500.1168212.588<2e−16∗∗∗
incentiveYes 1.732820.1091715.872<2e−16∗∗∗
history 0.473220.072946.4871.37e−10∗∗∗
(a) For each of the fitted models, briefly explain what can be inferred about participants with similar histories.
(b) Based on this analysis and the experimental design, is it advisable for a participant with asthma to cycle to work more often? Explain.