Paper 2, Section I, J

Statistical Modelling
Part II, 2019

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 RR code below and its abbreviated output.

>lm.1=lm>\operatorname{lm} .1=\operatorname{lm} (episodes miles ++ history, data=cycling)

>summary(1 lm.1)>\operatorname{summary}(1 \mathrm{~lm} .1)

Coefficients:

Estimate Std. Error tt value Pr(>t)\operatorname{Pr}(>|t|)

(Intercept) 0.669370.079658.404<2e160.66937 \quad 0.07965 \quad 8.404<2 \mathrm{e}-16 * * *

miles 0.049170.018392.6740.00761\quad-0.04917 \quad 0.01839-2.6740 .00761 * *

history 1.489540.0481830.918<2e161.489540 .0481830 .918<2 \mathrm{e}-16 * * *

>lm.2=lm(>\operatorname{lm} .2=\operatorname{lm}( episodes  ~incentive ++ history, data=cycling)

>> summary (lm.2)

Coefficients:

Estimate Std. Error tt value Pr(>t)\operatorname{Pr}(>|t|)

(Intercept) 0.095390.069601.3710.1710.09539 \quad 0.06960 \quad 1.371 \quad 0.171

incentiveYes 0.913870.0650414.051<2e160.91387 \quad 0.06504 \quad 14.051<2 \mathrm{e}-16 * * *

history 1.468060.0434633.782<2e161.46806 \quad 0.04346 \quad 33.782<2 \mathrm{e}-16 * * *

>lm.3=lm(>\operatorname{lm} .3=\operatorname{lm}( miles incentive ++ history, data=cycling)

>summary(lm.3)>\operatorname{summary}(\operatorname{lm} .3)

Coefficients :

Estimate Std. Error t value Pr(>t)\operatorname{Pr}(>|t|)

(Intercept) 1.470500.1168212.588<2e161.47050 \quad 0.11682 \quad 12.588<2 \mathrm{e}-16 * * *

incentiveYes 1.732820.1091715.872<2e161.73282 \quad 0.10917 \quad 15.872<2 \mathrm{e}-16 * * *

history 0.473220.072946.4871.37e10\quad 0.47322 \quad 0.07294 \quad 6.487 \quad 1.37 \mathrm{e}-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.