diff --git a/Exercise9Answers.py b/Exercise9Answers.py
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+#Answer to number 1
+import pandas as pd
+import numpy as np
+from plotnine import *
+cd ../
+cd Desktop
+pwd
+wages=pd.read_csv("wages.csv")
+a=ggplot(wages,aes(x="yearsSchool",y="wage"))
+a+geom_point()+coord_cartesian()
+a=ggplot(wages,aes(x="yearsSchool",y="wage"))+theme_classic()+geom_point()
+a+xlab("Years of School")+ylab("Wage")+stat_smooth(method="lm")
+
+
+#Anser to number 2 after converting data.txt into a csv manually
+cd ../ICB_EX9
+data=pd.read_table("data.txt", header=0, sep ",")
+data.shape
+! pip install scikit-misc
+#Barplot of means of data
+d=ggplot(data)+theme_classic()+xlab("Region")+ylab("Observations")
+d+geom_bar(aes(x="region",y="observations"),stat="summary",fun_y=np.mean)
+#Scatterplot of data
+e=ggplot(data)+theme_classic()+coord_cartesian()+xlab("Region")+ylab("Observations")
+e+geom_jitter(aes(x="region",y="observations"))
+#Barplot and scatter tell similar stories since they show the general distribution and relationship of regions and observations.