You want to predict whether a Euro banknote is forged or genuine. For this purpose, you...

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You want to predict whether a Euro banknote is forged or genuine. For this purpose, you collected a random sample of 900 Euro banknotes and took their images. Then you applied Wavelet Transform tools to extract the following features from each image: VARIANCE, SKEWNESS, KURTOSIS and ENTROPY. The first six lines of the data are shown as follows: 1 234 5 6 GENUINE yes yes yes 3.821300 no 0.704900 VARIANCE SKEWNESS KURTOSIS 1.334900 6.11890 0.46497 5.042900 -0.52974 0.50439 1.10600 0.23175 2.01330 2.05640 0.17174 -1.78590 0.36119 9.74840 0.15394 -1.61340 yes -0.016103 yes -0.782890 11.36030 -0.37644 -7.04950 The variable GENUINE equals no for forged Euro banknotes, and yes otherwise. You used the data and built a Logistic Regression model where GENUINE is the dependent variable. The coefficient estimates of the model are given as follows: Coefficients: (Intercept) -5.26 4.96 VARIANCE SKEWNESS KURTOSIS ENTROPY Estimate 2.64 3.39 0.27 ENTROPY 0.49826 Consider the following Euro banknote: VARIANCE 4.39 SKEWNESS -2.12 CURTOSIS -3.79 ENTROPY -0.03 Based on the model, what percent of such banknotes are predicted to be forged? Enter the answer as a percentage with one decimal place (e.g. 21.4%). (If your answer is not correct to three decimal places but is still within Correct Answer10.80 Correct Answer, you will receive 50% partial credit. I You want to predict whether a Euro banknote is forged or genuine. For this purpose, you collected a random sample of 900 Euro banknotes and took their images. Then you applied Wavelet Transform tools to extract the following features from each image: VARIANCE, SKEWNESS, KURTOSIS and ENTROPY. The first six lines of the data are shown as follows: 1 234 5 6 GENUINE yes yes yes 3.821300 no 0.704900 VARIANCE SKEWNESS KURTOSIS 1.334900 6.11890 0.46497 5.042900 -0.52974 0.50439 1.10600 0.23175 2.01330 2.05640 0.17174 -1.78590 0.36119 9.74840 0.15394 -1.61340 yes -0.016103 yes -0.782890 11.36030 -0.37644 -7.04950 The variable GENUINE equals no for forged Euro banknotes, and yes otherwise. You used the data and built a Logistic Regression model where GENUINE is the dependent variable. The coefficient estimates of the model are given as follows: Coefficients: (Intercept) -5.26 4.96 VARIANCE SKEWNESS KURTOSIS ENTROPY Estimate 2.64 3.39 0.27 ENTROPY 0.49826 Consider the following Euro banknote: VARIANCE 4.39 SKEWNESS -2.12 CURTOSIS -3.79 ENTROPY -0.03 Based on the model, what percent of such banknotes are predicted to be forged? Enter the answer as a percentage with one decimal place (e.g. 21.4%). (If your answer is not correct to three decimal places but is still within Correct Answer10.80 Correct Answer, you will receive 50% partial credit. I

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SOLUTION To predict whether a banknote is forged or genuine using the logistic regression model we need to calculate the probability of the banknote being genuine based on its features The logistic re... View the full answer