The following graph models daily consumption of regular cust…
The following graph models daily consumption of regular customers in a temporal fashion. If a customer drinks coffee today, he/she will drink coffee again tomorrow with some probability. If he/she skips coffee today, he/she will skip it again tomorrow with some probability. Mona did not drink coffee today. What is the likelihood of her drinking coffee the day after tomorrow?
Read DetailsYou enjoy observing birds in the forest next to your home. O…
You enjoy observing birds in the forest next to your home. Over the years, you have gathered data on two types of birds: Migratory and Resident. For each bird, you recorded the following features: Wingspan (cm) Body weight (kg) Beak length (cm) Each feature appears to have a Gaussian distribution. However, you wonder if these features are correlated. To investigate this, you decide to estimate the parameters of the distribution (mean and covariance matrix) using your data, with the following code. Assume X is a numpy array containing your data. mu = np.mean(X, axis=0)cov = np.cov(X, rowvar=False)print(“Mean for [Wingspan, Body Weight, Beak Length]\n:”, mu)print(“Covariance Matrix:\n”, cov) The output of your code is as follows: Mean for [Wingspan, Body Weight, Beak Length]: [39.8651413 0.98364373 2.90184872]Covariance Matrix: [[27.24000213 -0.41958818 2.14532419] [-0.41958818 0.43377056 0.07994791] [ 2.14532419 0.07994791 0.38173139]] What can you conclude about your data?
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