For Exercises 6-10, work with the adult ch3 training data set. The response is whether income exceeds $50,000. 6. Add a record index field to the data set. 7. Determine whether any outliers exist for the education field. 8. Do the following for the age field. a) Standardize the variable. b) Identify how many outliers there are and identify the most extreme outlier. 9. Derive a flag for capital-gain, called capital-gain-flag, which equals 0 for capital gain equals zero, and 1 otherwise (hint: use code for replace based on a condition- dataset/'column] = np.where(dataset['column'] value, value if true, value_if_false). 10. Age anomaly? Select only records with age at least 80. Construct a histogram with the data of people whose age is at least 80 (hint: a way to solve the question is to use np.where). Explain what you see in one sentence and why it is like that in another sentence.