diff --git a/part1script.py b/part1script.py
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+++ b/part1script.py
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+#Exercise9, 10/27/17
+
+import numpy
+import pandas
+from scipy.optimize import minimize 
+from scipy.stats import norm
+from plotnine import *
+
+data = pandas.read_csv('ponzr1.csv', header=0,sep=',')
+
+#subset data into three different data frames 
+sub1=data.loc[data.mutation.isin(['WT','M124K']),:]
+sub2=data.loc[data.mutation.isin(['WT','V456D']),:]
+sub3=data.loc[data.mutation.isin(['WT','I213N']),:]
+
+
+#Make new data frame with 'group' column (your x=0 or x=1)
+#var2=pandas.DataFrame({'y':var1.col2name, 'x':})
+sub1frame=pandas.DataFrame({'y':sub1.ponzr1Counts,'x':0})
+sub2frame=pandas.DataFrame({'y':sub2.ponzr1Counts,'x':0})
+sub3frame=pandas.DataFrame({'y':sub3.ponzr1Counts,'x':0})
+#Designate 'treatment' group as x=1
+#var2.loc[var1.col1name=='name of treatment group', 'x']=1
+sub1frame.loc[sub1.mutation=='M124K','x']=1
+sub2frame.loc[sub2.mutation=='V456D','x']=1
+sub3frame.loc[sub3.mutation=='I213N','x']=1
+#print(sub3frame)
+
+#### Define null function
+def nllikeNull(p,obs):
+    B0=p[0]
+    sigma=p[1]
+    
+    expected=B0
+    nll=-1*norm(expected,sigma).logpdf(obs.y).sum()
+    return nll
+
+# estimate parameters by minimizing the NLL for sub1frame
+initialGuess=numpy.array([1,1])
+fit=minimize(nllikeNull,initialGuess,method="Nelder-Mead",options={'disp': True},args=sub1frame)
+nullM124K= fit.fun #M124K
+#gives NLL value for affect of mutation 1 in null model
+#print(nullM124K)
+
+initialGuess=numpy.array([1,1])
+fit=minimize(nllikeNull,initialGuess,method="Nelder-Mead",options={'disp': True},args=sub2frame)
+nullV456D= fit.fun #V456D
+
+initialGuess=numpy.array([1,1])
+fit=minimize(nllikeNull,initialGuess,method="Nelder-Mead",options={'disp': True},args=sub3frame)
+nullI213N = fit.fun #I213N
+
+#### Define function y=B0+B1*treat+error
+def nllike(p,obs):
+    B0=p[0]
+    B1=p[1]
+    sigma=p[2]
+    
+    expectedAlt=B0+B1*obs.x
+    nll=-1*norm(expectedAlt,sigma).logpdf(obs.y).sum()
+    return nll
+   
+#estimate parameters by minimizing the NLL
+initialGuess=numpy.array([1,1,1])
+fit=minimize(nllike,initialGuess,method="Nelder-Mead",options={'disp': True},args=sub1frame)
+altM124K = fit.fun #M124K
+
+initialGuess=numpy.array([1,1,1])
+fit=minimize(nllike,initialGuess,method="Nelder-Mead",options={'disp': True},args=sub2frame)
+altV456D = fit.fun #V456D
+
+initialGuess=numpy.array([1,1,1])
+fit=minimize(nllike,initialGuess,method="Nelder-Mead",options={'disp': True},args=sub3frame)
+altI213N = fit.fun #I213N
+
+### Calculating D values
+DvalM124K = 2*(nullM124K-altM124K)
+print("D-value for M124K= ", DvalM124K)
+
+DvalV456D = 2*(nullV456D-altV456D)
+print("D-value for V456D = ", DvalV456D)
+
+DvalI213N = 2*(nullI213N-altI213N)
+print("D-value for I213N = ", DvalI213N)
+
+### Chi-squared dist test values
+pval1=1-scipy.stats.chi2.cdf(x=DvalM124K,df=1)
+pval2=1-scipy.stats.chi2.cdf(x=DvalV456D,df=1)
+pval3=1-scipy.stats.chi2.cdf(x=DvalI213N,df=1)
+print(pval1)
+print(pval2)
+print(pval3)
+
+
diff --git a/part2script.py b/part2script.py
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+++ b/part2script.py
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+###Exercise 9 Part 2
+import numpy
+import pandas
+from scipy.optimize import minimize
+from scipy.stats import norm
+from plotnine import *
+data=pandas.read_csv("MmarinumGrowth.csv")
+def Grate(p,obs):
+    um=p[0]
+    K=p[1]
+    sigma=p[2]
+    ue=um*(obs.S/(obs.S+K))
+    nll=-1*norm(ue,sigma).logpdf(obs.u).sum()
+    return nll
+probswrong=numpy.array([1,1,1])
+best=minimize(Grate,probswrong,method="Nelder-Mead",options={"disp":True},args=data)
+print (best.x)
diff --git a/part3script.py b/part3script.py
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--- /dev/null
+++ b/part3script.py
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+import numpy as np
+import pandas as pd
+import scipy as sp
+from scipy.optimize import minimize
+from scipy.stats import norm
+from plotnine import *
+
+leaf=pd.read_csv("leafDecomp.csv")
+
+
+ggplot(leaf, aes(x="Ms", y="decomp")) + geom_point()
+
+
+def nllike_null (p,obs):
+    a=p[0]
+    b=p[1]
+    c=p[2]
+    sigma=p[3]
+    
+    expected=a
+    nll=-1*norm(expected,sigma).logpdf(obs.decomp).sum()
+    return(nll)
+    
+def nllike_linear(p,obs):
+    a=p[0]
+    b=p[1]
+    c=p[2]
+    sigma=p[3]
+    
+    expected=a+b*obs.Ms
+    nll=-1*norm(expected,sigma).logpdf(obs.decomp).sum()
+    return(nll)    
+    
+def nllike_hump(p,obs):
+    a=p[0]
+    b=p[1]
+    c=p[2]
+    sigma=p[3]
+    
+    expected=a+b*obs.Ms+c*obs.Ms**2
+    nll=-1*norm(expected,sigma).logpdf(obs.decomp).sum()
+    return(nll)
+
+
+guess_hump=np.array([1,100,500,50])
+fit_null=minimize(nllike_null,guess_hump, method="Nelder-Mead", options={'disp':True},args=leaf)
+fit_linear=minimize(nllike_linear,guess_hump, method="Nelder-Mead", options={'disp':True},args=leaf)
+fit_hump=minimize(nllike_hump,guess_hump, method="Nelder-Mead", options={'disp':True},args=leaf)
+
+chi2_linear=2*(fit_null.fun-fit_linear.fun)
+1-sp.stats.chi2.cdf(x=chi2_linear,df=1)
+
+chi2_hump=2*(fit_null.fun-fit_hump.fun)
+1-sp.stats.chi2.cdf(x=chi2_hump,df=2)
+
+#based on the chi2 test, the hump-shaped model is the best fit for the leaf decomposition data.