<jupyter_start><jupyter_text>10. For the above example, we then created indicator variables, age18_29, age30_44, age45_64, and age65up, for four age categories. We then fit a new regression:```rlm(formula = weight ~ age30_44 + age45_64 + age65up)             coef.est coef.se(Intercept)     157.2     5.4age30_44TRUE     19.1     7.0age45_64TRUE     27.2     7.6age65upTRUE       8.5     8.7  n = 2009, k = 4  residual sd = 119.4, R-Squared = 0.01```Make a graph of weight versus age (that is, weight in pounds on y-axis, age in years on x-axis) and draw the fitted regression model. Again, this graph should be consistent with the above computer output.  Responses<jupyter_code>x = range(18,91)
y = [random.normalvariate(161, 7.3) +  random.normalvariate(0.26*a, 16) for a in x]
ax = sns.scatterplot(x,y)
plt.plot([18, 29], [157.2, 157.2], linewidth=3, c='green', alpha=1)
plt.plot([30, 44], [157.2 + 19.1, 157.2 + 19.1], linewidth=3, c='green', alpha=1)
plt.plot([45, 64], [157.2 + 27.2, 157.2 + 27.2], linewidth=3, c='green', alpha=1)
plt.plot([65, 90], [157.2 + 8.5, 157.2 + 8.5], linewidth=3, c='green', alpha=1)
ax.set(xlabel='Age', ylabel='Weight (lbs)', ylim=[60,280])
plt.show()<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.161873459326212, 'fraction_numerical': 0.1306491372226787, 'mean_word_length': 3.931174089068826, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '59462', 'license': 'MIT', 'n_tokens_mistral': 573, 'n_tokens_neox': 475, 'n_words': 135, 'path': 'Gelman10.ipynb', 'repo_name': 'petulla/AppliedRegression', 'size': '1217'}