$FL2@(#) IBM SPSS STATISTICS DATA FILE 64-bit MS Windows 20.0.0 #### ####################Y@15 Nov 1314:08:12Bank Loan Default ###########################CUSTOMER########################AGE ###Age in years########################EDUCATIO####Level of Education ########################EMPLOY ####Years with current employer ########################ADDRESS ####Years at current address########################INCOME ####Household income in thousands ########################DEBTINC ####Debt to income ratio (x100) ########################CREDDEBT####Credit card debt in thousands ########################OTHDEBT ####Other debt in thousands ########################DEFAULT ####Has the customer defaulted ################ No College ######?#College or Higher Degree #############################No ######?#Yes ######## ###########DOCUMENT This is a hypothetical data file that concerns a bank's efforts to reduce the rate of loan defaults. The file contains financial and demographic information on 850 past and prospective customers. The first 700 cases are customers who were previously given loans. The last 150 cases are prospective customers that the bank needs to classify as good or bad credit risks. 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Mb###@#####|@fd#@~#?MMg?######}@emed433333#@jHcC?  #5 ?dd{ ######}@L6@xg#M#@ddhet#B ##@##### } @det7@#N]?*Ral! #@#####0}@~e#####k@1@c/@Tul#7@#####@}@ejed#@rSr ?^6S!?ejn#####P}@ffffff2@#? dydfdx3###@#####`}@edk#######@<,?V}b?#####p}@ixe| 3@d###?5Y##@#####}@edgegfffff#@/#EH?v#?T#1? epg#####}@gfffff#@#~#b?dzeedv#w~1#@#####}@deq#@#.#H? [Y?#####}@dd333333#@##?kA!?#####}@eptd#@##? Ym#@}dgfv#####}@L0@dz?e}diizH#G#@#####}@de %@o#/#?'#?#####}@yd#####d@######'@)D? [_$D2@######~@dytd#####0@##V#@7Y##@dmx######~@#@::Fv? d|efg~a#?##### ~@edigfffff#@# #fh?#%?#####0~@gd}333333#@<@?g$B##?#####@~@ejf~d#######@ -?@?eiu#####P~@######%@Tt< @eehn}*j0##@#####`~@ddf433333#@#4#6V BR#X@###^V BHz#X@####iV BfffffX@###sV BfffffX@###0~V Bp= X@#########V BR#X@ U Assignment 4 (Group) 60 points 1) Sunshine Bank is trying to decrease the losses of its personal loan department. Over the past 5 years the bank has collected several types of data on its customers. This includes their age, their level of education, years of employment with current employer, years at current residence, household income, debt to income ratio, credit card debt and other types of debt of the customer. The bank has given these customers personal loans in the past and has a record of whether they have defaulted on this loan or not. Use the data in Bankloan_Model_Test_Data.sav to build a model to predict if a customer is likely to default or not. Once you have built the model, use it to predict which of the customers listed in Bankloan_Prediction_Data.sav are likely to default. Use probability of 0.5 as a cutoff. Please list the account numbers for the customers who are likely to default. 2) Data on the corn consumption in Wisconsin from year 2000 to 2013 is given in the file Corn.sav. Fit a linear, exponential and quadratic trend model and select the best model. Based on the selected model, forecast the 2014 corn consumption levels. 3) Data on the daily exchange rate between the Japanese Yen and the US Dollar is given in the file JapaneseYen.sav. Compute the three period trailing moving average and the three period centered moving average. Also plot the Japanese Yen, TMA and CMA. Note that you must also submit the SPSS file with your computations. 4) The yearly car sales (in thousands) in the state of Illinois are provided in Car Sales.xlsx. Using Exponential Smoothing Method A and Method B provide a forecast for the year 1998. Please provide a clear screen shot of the excel computation. Also note that you must submit the excel file with your computations