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diff --git a/answer.py b/answer.py
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+# -*- coding: utf-8 -*-
+"""
+Created on Thu Nov  8 09:50:51 2018
+
+@author: Annaliese
+"""
+
+# Exercise 9
+
+# import packages
+import pandas as pd
+import matplotlib.pyplot as plt
+import numpy as np
+from plotnine import *
+
+##############################################
+
+# Part One
+
+# define file name 
+file = "season.data.csv"
+
+# read the csv file in
+data = pd.read_csv("season.data.csv")
+
+# produce a scatterplot
+data.columns.values
+x = data.loc[:,'tech.score']
+y = data.loc[:, 'comp.score']
+
+# calculate trend line
+z = np.polyfit(x, y, 1)
+p = np.poly1d(z)
+
+# plot
+plt.scatter(x, y)
+plt.xlabel("Technical Score")
+plt.ylabel("Component Score")
+plt.title("Component Score vs. Technical Score for the 2017-2018 Figure Skating Grand Prix Season")
+plt.plot(x, p(x), "r--")
+
+# I included this data inthe assignment submission for reproducibility
+
+#########################################################
+
+# get new data
+data = pd.read_csv("data.txt")
+
+# calculate the means by each region
+means = data.groupby('region')['observations'].mean()
+
+# make a bar plot
+means.plot(kind = "bar")
+
+# make scatter plot
+base = ggplot(data, aes(x = "region", y = "observations"))
+base + geom_point() + geom_jitter() + theme_classic()
+
+# The two plots show very different information. The bar plot shows that all 
+# of the means are very close together, but the scatterplot reveals
+# that the spread of the data varies with the region. The West and East 
+# regions have large variability in their observations, whereas North has
+# very little variation. The south region has two modes for its observations,
+# which is very different from the others.
\ No newline at end of file
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diff --git a/season.data.csv b/season.data.csv
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+competition,segment,discipline,placement,skater,country,tech.score,comp.score,skating.skills,transitions,performance,composition,interpretation,is.judge,component.range
+russia,short,ladies,5,radionova.elena,russia,34.21,34.54,8.5,8.43,8.71,8.75,8.79,yes,0.36
+russia,short,ladies,6,tursynbaeva.elizabet,kazakhstan,32.71,31.21,7.75,7.68,7.86,7.86,7.86,yes,0.18
+russia,short,ladies,1,medvedeva.evgenia,russia,42.69,38.06,9.36,9.43,9.64,9.54,9.61,yes,0.28
+russia,free,ladies,3,higuchi.wakaba,japan,69.37,68.2,8.5,8.36,8.61,8.61,8.54,no,0.25
+russia,free,ladies,6,bell.mariah,america,64.37,60.34,7.61,7.25,7.71,7.57,7.57,yes,0.46
+russia,free,ladies,12,mete.mae.berenice,france,53.65,53.07,6.57,6.32,6.82,6.71,6.75,no,0.5
+canada,short,ladies,4,hicks.courtney,america,35.38,26.68,7.21,6.89,7.18,7.25,7.32,yes,0.43
+canada,short,ladies,7,wagner.ashley,america,28.1,33.47,8.18,8.18,8.39,8.43,8.68,yes,0.5
+canada,short,ladies,11,chartrand.alaine,canada,21.34,26.17,6.75,6.46,6.11,6.71,6.68,yes,0.64
+canada,free,ladies,1,osmond.kaetlyn,canada,65.95,71.9,8.96,8.82,8.86,9.11,9.18,yes,0.36
+canada,free,ladies,2,sotskova.maria,russia,61.34,65.08,7.96,7.89,8.29,8.25,8.29,yes,0.4
+canada,free,ladies,10,pogorilaya.anna,russia,35.86,54.98,7.29,7,6.21,7,6.86,yes,1.08
+china,short,ladies,4,zagitova.alina,russia,39.01,31.43,7.82,7.71,7.71,8.11,7.93,yes,0.4
+china,short,ladies,8,li.xiangning,china,32.95,26.25,6.68,6.18,6.75,6.64,6.57,yes,0.57
+china,short,ladies,9,choi.dabin,korea,26.1,27.8,7.04,6.71,6.89,7.07,7.04,yes,0.36
+china,free,ladies,3,mihara.mai,japan,74.45,64.72,8.14,7.79,8.32,8.07,8.14,yes,0.53
+china,free,ladies,10,glenn.amber,america,45.66,53.87,6.89,6.64,6.46,6.86,6.82,yes,0.43
+china,free,ladies,11,zhao.ziquan,china,48.75,46.57,6.04,5.39,5.93,5.96,5.79,yes,0.65
+japan,short,ladies,5,nagasu.mirai,america,34.45,30.72,7.82,7.29,7.82,7.68,7.79,yes,0.53
+japan,short,ladies,10,rajicova.nicole,slovakia,27.33,27.03,6.82,6.57,6.64,6.86,6.89,yes,0.32
+japan,short,ladies,6,miyahara.satoko,japan,31.02,34.03,8.5,8.29,8.54,8.5,8.71,yes,0.42
+japan,free,ladies,8,shiraiwa.yuna,japan,55.29,59.31,7.61,7.14,7.57,7.39,7.36,yes,0.47
+japan,free,ladies,12,park.soyoun,korea,35.19,50.06,6.5,6.21,6.11,6.25,6.21,yes,0.39
+japan,free,ladies,3,kostner.carolina,italy,62.96,75.71,9.39,9.39,9.36,9.5,9.68,yes,0.32
+france,short,ladies,4,mihara.mai,japan,31.93,32.64,8.29,7.82,8.11,8.21,8.36,yes,0.54
+france,short,ladies,5,zagitova.alina,russia,32.27,31.19,7.89,7.68,7.57,7.89,7.96,yes,0.39
+france,short,ladies,7,lecavelier.laurine,france,31.21,29.47,7.36,7.07,7.46,7.36,7.57,yes,0.5
+france,free,ladies,4,osmond.kaetlyn,canada,68.42,70.3,8.86,8.57,8.68,8.86,8.96,yes,0.39
+france,free,ladies,5,mihara.mai,japan,73.45,64.1,8.14,7.64,8.25,8,8.04,yes,0.61
+france,free,ladies,6,shiraiwa.yuna,japan,64.86,63.27,7.93,7.61,8.04,8,7.96,yes,0.43
+america,short,ladies,6,wagner.ashley,america,30.44,33.68,8.18,8.14,8.61,8.5,8.68,yes,0.54
+america,short,ladies,8,tsurskaya.polina,russia,33.31,30.89,7.82,7.5,7.64,7.86,7.79,yes,0.36
+america,short,ladies,9,chen.karen,america,29.6,30.93,7.71,7.5,7.64,7.89,7.93,yes,0.43
+america,free,ladies,1,miyahara.satoko,japan,72.23,71.08,8.79,8.57,9.04,8.96,9.07,yes,0.5
+america,free,ladies,2,sakamoto.kaori,japan,73.71,67.48,8.39,8.14,8.5,8.54,8.61,yes,0.47
+america,free,ladies,3,tennell.bradie,america,72.68,64.41,7.86,7.71,8.36,8.07,8.25,yes,0.65