Credit Card Use: Consider the following hypothetical bank data on consumers' use of credit card credit facilities in Table 11.3. Create a JMP data table using Table 11.3 fFile>New>Dota Table), and create a neural network model (Analyze>Predictive Modelling>Neura using your new data table. Use the default validation method (Holdback Portion] and use the random seed 123. (see the screen capture below} TABLE 11.3 Data for Credit Card Example and Variable Descriptions Years Salary.r Used Credit 4 43 {l 18 65 l l 53 0 3 95 0 15 88 l 6 1 l2 1 N ate : Years = Number of years that a customer has been with the bank; Salary = customer's salary (in thousands of dollars); Used Credit 1 = customer has left an unpaid credit card balance at the end of at least one month in the prior year; and 0 = balance was paid off at the end of each month. \f0 What is the misclassiflcation rate on the validation set?Answer: [Select ] 0 How many parameters are estimated to build this model? Answer: [53'3\"] [H int: use the red triangle option "Show Estimates") Use the Proler (red triangle option} to explore how the predicted response changes as you change values of the predictors. Describe what you observe. Determine whether the statement below is either true or false. 0 As years increases, the probability of "Use Credit = 1 " decreases. As Salary increases, the probability of "Use Credit = 1" increases. The highest probability that "Use Credit = 1" is when Years is low and Salary is High. (TRUE/FALSE): [Select] Fit another neural network to this same data. To produce the same results rst set the random seed to 456 (in the rst launch window). There will be differences between this model and the rst model. Why do the results differ? Determine whether the statement below is either true or false. 0 The starting values for the weigh ts are different {random starting values are used unless the random seed is set], leading to different nal models. fTRUEfFALSEJ: [53'3\"]