Make an ungrouped frequency distribution for Price/Sales. Use the frequency distribution to find the mean, median, mode and standard deviation for Price/Sales ratio. (Note the boldface – must show and describe steps for how you USED the frequency distribution to compute the statistics)

. Find the z-score for Kohl’s for Profit Margin. Also, find the z-score for Kohl’s for Price/Sales. Describe, in a few sentences, the meaning of these z-scores.

Construct a histogram of your empirical data using eight bars and then five bars. For consistency's sake, for eight bars use the class width of 0.125. So, your first bar will be 0.000-0.124, your next bar will be 0.125 - 0.249, etc. For five bars, use the class width of 0.2. So, your first bar will be 0.00-0.19, your next bar will be 0.2-0.39, etc. Upload both pictures of your histograms. ***Below are my 50 random numbers. What do I need to show? I am confused on what exactly I need to show. Can you help me on these?

0.9          1.0          0.7          0.0          0.8         

0.0          0.8          0.2          0.0          1.0

0.5          0.2          0.5          0.4          0.0

0.8          0.1          0.4          0.2          1.0

0.1          0.1          0.9          0.7          0.1

0.9          1.0          0.9          0.2          0.4

0.0          1.0          0.5          0.9          0.4

0.9          0.0          0.0          1.0          0.3

0.4          0.8          0.9          0.6          0.5

0.4          0.5          0.3          0.7          1.0

Find the equilibrium vector for the transition matrix below.

0.4......0.6

0.3......0.7

The equilibrium vector is....

Find the equilibrium vector for the transition matrix below.

0.2...0.2...0.6

0.2...0.4...0.4

0.2...0.3...0.5

The equilibrium vector is...

Find the equilibrium vector for the transition matrix below.

0.2...0.2...0.6

0.8...0.1...0.1

0.2...0.4...0.4

The equilibrium vector is ...

USE Python 2.7(screen shot program with output)

the task is: takes in a list of protein sequences as input and finds all the transmembrane domains and returns them in a list for each sequence in the list with given nonpolar regions and returns the lists for those.

1. This code should call two other functions that you write: regionProteinFind takes in a protein sequence and should return a list of 10 amino acid windows, if the sequence is less than 10 amino acids, it should just return that sequence. (initially it should grab amino acids 1-10…the next time it is called it should grab amino acids 2-11…) for each sequence in the list.

testcode:

"protein='MKLVVRPWAGCWWSTLGPRGSLSPLGICPLLMLLWATLR''

the regionProteinFind returns:['MKLVVRPWAG','KLVVRPWAGC','LVVRPWAGCW','VVRPWAGCWW','VRPWAGCWWS','RPWAGCWWST','PWAGCWWSTL','WAGCWWSTLG','AGCWWSTLGP','GCWWSTLGPR',
'CWWSTLGPRG','WWSTLGPRGS','WSTLGPRGSL','STLGPRGSLS','TLGPRGSLSP','LGPRGSLSPL','GPRGSLSPLG','PRGSLSPLGI','RGSLSPLGIC','GSLSPLGICP','SLSPLGICPL','LSPLGICPLL','SPLGICPLLM','PLGICPLLML','LGICPLLMLL','GICPLLMLLW','ICPLLMLLWA','CPLLMLLWAT','PLLMLLWATL','LLMLLWATLR']

2nd testcode;

protein=MP

region protein sequence should return: ['ME']

2. A second function called testForTM , which should calculate and return the decimal fraction of ten amino acid window which are nonpolar for each sequence in the list. the nonpolar regions are (A,V,L,I,P,M,F,W). my code for this is:

def testForTM(AAWindow):
totalNP= 0
nonPolarList=['A', 'V', 'L', 'I', 'P', 'M', 'F', 'W']
for aa in AAWindow:   
if aa in nonPolarList:   
totalNP+=1
return totalNP/10.0 #THIS SHOULD DEVIDE BY len(AAWindow) so it works for sequences less than 10 length like 'MP'

3. The last function,tmSCANNER should call the get protein region and test for TM and Ultimately, as a result the code should be used to scan each protein sequence in the list as input generating list of numbers of non polar for each protein sequence which measures the fraction of nonpolar residues in each 10bp window(it slides 10 amino acids at a time until it is at the last aa window of a protein sequence with any length and give the lists for those. The code should output what is displayed below.

#Test code for TMFinder

input=> listOfProtein=['MKLVVRPWAGCWWSTLGPRGSLSPLGICPLLMLLWATLR', 'MARKCSVPLVMAWLTWTTSRAPLPH', 'MPWPTSITXXXXXXSWSPEWLSSGLRSILGWEQPRVSHKGHSHEWHRRP']

tmValuesList=TMFinder(listOfProtein)

print 'The list of TM values are:', tmValuesList

as a result it should print out this list:

["protein 1:'MKLVVRPWAGCWWSTLGPRGSLSPLGICPLLMLLWATLR'", 'TMValue:[0.7, 0.6, 0.7, 0.7, 0.6, 0.5, 0.6, 0.5, 0.5, 0.4, 0.4, 0.4, 0.4, 0.3, 0.4, 0.5, 0.4, 0.5, 0.4, 0.5, 0.6, 0.7, 0.7, 0.8, 0.8, 0.8, 0.9, 0.8, 0.9, 0.8]',"protein2:'MARKCSVPLVMAWLTWTTSRAPLPH'", 'TMValue:[0.6, 0.6, 0.6, 0.7, 0.8, 0.8, 0.9, 0.8, 0.7, 0.6, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5]',"protein3:'MPWPTSITXXXXXXSWSPEWLSSGLRSILGWEQPRVSHKGHSHEWHRRP'",'TMValue:[0.5, 0.4, 0.3, 0.2, 0.1, 0.1, 0.2, 0.1, 0.2, 0.2, 0.3, 0.4, 0.4, 0.4, 0.4, 0.5, 0.4, 0.4, 0.4, 0.5, 0.4, 0.4, 0.4, 0.4, 0.5, 0.4, 0.5, 0.5, 0.4, 0.3, 0.3, 0.2, 0.2, 0.2, 0.1, 0.2, 0.1, 0.1, 0.1]']

This is time sensitive.Thank you for the help!!!

Use the Lot Price Data to run a regression in Excel. Your response variable is Lot Price, while the other four variables are all X variables in this regression. For the Mature Trees variable, the 95% confidence interval for the slope coefficient includes the hypothesized value of zero.

TRUE OR FALSE

, we are given a set of n items. Each item weights between 0 and 1. We also have a set of bins (knapsacks). Each bin has a capacity of 1, i.e., total weight in any bin should be less than 1. The problem is to pack the items into as few bins as possible. For example Given items: 0.2, 0.5, 0.4, 0.7, 0.1, 0.3, 0.8 Opt Solution: Bin1[0.2, 0.8], Bin2[0.5, 0.4, 0.1], Bin3[0.7, 0.3] Each item must be placed in a bin before the next item can be pro- cessed. This problem is NP-hard. Give an approximation algorithm to solve the bin packing problem. Provide pseudo-code, time complexity analysis and proof of performance ratio.

1. Emily, Car, Stock Market, Sweepstakes, Vacation and Bayes.

Emily is taking Bayesian Analysis course. She believes she will get an A with probability 0.6, a B with probability 0.3, and a C or less with probability 0.1. At the end of semester, she will get a car as a present form her uncle depending on her class performance. For getting an A in the course Emily will get a car with probability 0.8, for B with probability 0.5, and for anything less than B, she will get a car with probability of 0.2. These are the probabilities if the market is bullish. If the market is bearish, the uncle is less likely to make expensive presents, and the above probabilities are 0.5, 0.3, and 0.1, respectively. The probabilities of bullish and bearish market are equal, 0.5 each. If Emily gets a car, she would travel to Redington Shores with probability 0.7, or stay on campus with probability 0.3. If she does not get a car, these two probabilities are 0.2 and 0.8, respectively. Independently, Emily may be a lucky winner of a sweepstake lottery for a free air ticket and vacation in hotel Sol at Redington Shores. The chance to win the sweepstake is 0.001, but if Emily wins, she will go to vacation with probability of 0.99, irrespective of what happened with the car.

After the semester was over you learned that Emily is at Redington Shores.

(a) What is the probability that she won the sweepstakes?

Ross Co., Westerfield, Inc., and Jordan Company announced a new agreement to market their respective products in China on July 18, February 12, and October 7, respectively. Given the information below, calculate the cumulative abnormal return (CAR) for these stocks as a group. Assume all companies have an expected return equal to the market return. (A negative value should be indicated by a minus sign. Leave no cells blank - be certain to enter "0" wherever required. Do not round intermediate calculations. Round your answers to 1 decimal place.)