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1. Use the following function to calculate the bootstrapped estimate for the correlation between age and weight on the boys data set. Use R=1000 bootstrap samples.

corfun <- function(data, i){
  data.sample <- data[i,]
  stat <- cor(data.sample$age, data.sample$wgt, use = "pairwise.complete.obs")
    return(stat)
}

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2. Explore the contents of the bootstr.cor object. For example, use function attributes() to see the listed dimensions that are within the bootstr.cor object and the class of the object.

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3. Plot the histogram of the individual bootstrapped estimates for every bootstrapped sample (i.e. $t).

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4. Add a column to boys that indicates whether boys are overweight by taking the boundary bmi > 25.

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5. Bootstrap a \(X^2\)-test that evaluates the distribution of overweight ovw boys over the regions reg.

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6. Plot the histogram of the individual bootstrapped estimates for every bootstrapped sample (i.e. $t).

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7. Do a bootstrap of the regression estimates of the following model:

• lm(wgt ~ age + hgt + I(hgt^2))

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8. ** Create a histogram of the individual bootstrapped estimates **

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9. Calculate the confidence intervals for

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End of Practical