Monday Wednesday Friday Saturday Sunday 1. Tuesday Thursday Home 12 am Home Home Home Home Home Home Home Home Home Home Home 1 am Home Home Home Home Home Home Home 2 am Home Home Home Home Home Home Home Home Home 3 am Home Home Home Home 4 am Home Home Home Home 5 am Home Home Park Home Home Home Home 6 am Home Park Park Grocery Home Home Park Home Home 7 am Home Park Home Home Office Office Park Home Home 8 am Office Home Office Home Restaurant Park 9 am Office Office Office Park 10 am Office Office Office Office Home Restaurant Park 11 am Office Office Office Office Grocery Office Office Office Grocery Park Noon Office Office Office 1 pm Office Office Office Park Office Office Home Office Office Home Park 2 pm Office Office Office Office Office Home Home 3 pm Office Office Office Home 4 pm Office Office Office Office Office Home Office Office Home Office Office Office Park 5 pm Movie Park Home 6 pm Home Office Grocery Restaurant Home 7 pm Home Home Grocery Restaurant Movie Home Restaurant Home 8 pm Home Home Home Restaurant Movie Home Home Home Home Restaurant Home 9 pm Home Home Restaurant Home 10 pm Home Home Home Home Home Home Home 11 pm Home Home Home Home Table: User A's Hourly Location Log Consider a smart thermostat (e.g., Nest by Google). The thermostat shuts down the air conditioning system when users are not at home, which should help to save energy. Based on a predictive model, and given the location of the users, the thermostat determines when it should turn on the air conditioning so the temperature is comfortable as soon as the users arrive at their homes (and remains nice until they leave again). Suppose that a particular user (User A) lives alone in a house. User A's hourly location log is provided in the table. This log is available to a mobile application that interfaces with the Nest thermostat. Design a Markov chain with two states: Home and Not Home. Consider that the day of the week does not matter. Also consider that the data for all 7 days of the week have equal probability of occuring when you build the Markov chain. Compute the stationary probability matrix. Given that User A is currently at home, what is the probability that User A will not be at home in the next hour? (Write your answer as a decimal and round to the nearest hundredths.) Enter answer here