.A 45 kW rated solar power system has its power output and the Solar irradiance on the PV system measured during daylight hours over a day. Based on the following data, create a scatter plot of the power output of the PV system vs the solar irradiance and comment on the relationship between the two. Based on the data, can you tell which season this day was in? Solar Irradiance on the PV system (kW/m²) 0 0.079 0.333 0.571 0.751 0.86 0.914 0.93 0.904 0.803 0.606 0.345 0.054 Power output from the PV system (kW) 0 3.098 12.438 20.124 25.703 28.498 29.587 29.69 28.798 26.264 20.434 12.223 2
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- At the beginning of each week, a machine is in one of four conditions: 1 = excellent; 2 = good; 3 = average; 4 = bad. The weekly revenue earned by a machine in state 1, 2, 3, or 4 is 100, 90, 50, or 10, respectively. After observing the condition of the machine at the beginning of the week, the company has the option, for a cost of 200, of instantaneously replacing the machine with an excellent machine. The quality of the machine deteriorates over time, as shown in the file P10 41.xlsx. Four maintenance policies are under consideration: Policy 1: Never replace a machine. Policy 2: Immediately replace a bad machine. Policy 3: Immediately replace a bad or average machine. Policy 4: Immediately replace a bad, average, or good machine. Simulate each of these policies for 50 weeks (using at least 250 iterations each) to determine the policy that maximizes expected weekly profit. Assume that the machine at the beginning of week 1 is excellent.Room registrations in the Toronto Towers Plaza Hotel have been recorded for the past 9 years. To project future occu-pancy, management would like to determine the mathemati-cal trend of guest registration. This estimate will help the hotel determine whether future expansion will be needed. Given the following time-series data, develop a regression equation relat-ing registrations to time (e.g., a trend equation). Then forecast year 11 registrations. Room registrations are in the thousands:Year 1: 17 Year 2: 16 Year 3: 16 Year 4: 21 Year 5: 20Year 6: 20 Year 7: 23 Year 8: 25 Year 9: 249. Mancini's Pizzeria sells four types of pizza crust. Last week, the owner tracked the number sold of each type, and this is what she found. Type of Crust Number Sold Thin crust 344 Thick crust 240 Stuffed crust 175 Pan style 316 Based on this information, of the next 5000 pizzas she sells, how many should she expect to be thin crust? Round your answer to the nearest whole number. Do not round any intermediate calculations.
- While analyzing their annual sales data (liters of wine sold per year) for the past 30 years, a vineyard found that after taking four successive differences, the resulting data had a mean of 2458 liters and looked like a stationary process (e.g. white noise). If the actual data for the past four years were 2018 - 2134 liters, 2017 - 2403 liters, 2016 - 2076 liters, and 2015 - 2290 liters. What would be a reasonable forecast for sales in 2019? Explain.Chart and Regression analysis : What does the intercept predict? X: C16 (number of cars on the sales lot) versus Y: C17 (cars sold per day) Equation: y=2.9x + 14.5 Slope:2.9 Intercept:14.5 Does the intercept mean the intercept is 14.5 means that the cars sold per day( Y) predicted number of cars on sale lot(X) to be 14.5, but this intercept has no meaning. So, I will not use to predict cars sold per day?For the E-Commerce Retail Sales (Million$) data given in the table below, provide estimates from the 1st Quarter (Q1) of 2016 to the 3rd Quarter (Q3) of 2017 by using two models: 1) Single Exponential Smoothing with α=0.3 2) Moving Average with k=2. Calculate MAPE for each model. Quarter Year Actual Q1 2016 86802 Q2 2016 92004 Q3 2016 93795 Q4 2016 124651 Q1 2017 99491 Q2 2017 106590 Q3 2017 108291 Compare two models above (Single Exponential Smoothing and Moving Average) based on their accuracies. Which forecasting method appears to be better? Using the model you choose, provide forecast for the 4th Quarter (Q4) of 2017. Assuming that this model is overestimating, find the actual value of the Q4 of 2017 based on MAPE value. Some residual graphs from the first forecast model (including wider range of E-Commerce Retail Sales data) are given below. What do these graphs tell about the model? Explain each graph.
- Which describes the random component of a time series? a. Climatic condition b. Recurring and periodic, possessing a regular period or variation in a time series c. Wars, strikes, and other highly unpredictable events d. Man‑made conventions causing short‑run upward and downward patterns in activities such as sales e. The long‑run general movement in a time series (e.g. sales)Use this data and build a model to forecast annual maintenance expenses based on the weekly usage in hours. If the machine is used for 38.4 hours, what should I expect my annual maintenance expenses to be? (Keep one decimal place) Weekly Usage (hours) Annual Maintenance Expense 13 17.0 10 22.0 20 30.0 28 37.0 32 47.0 17 30.5 24 32.5 31 39.0 40 51.5 38 40.0The following gives the number of accidents that occurred on Florida State Highway 101 during the last 4 months: Month Jan Feb Mar Apr Number of Accidents 25 45 60 95 Part 2 Using the least-squares regression LOADING... method, the trend equation for forecasting is (round your responses to two decimal places): y = + enter your response herex Part 3 Using least-squares regression, the forecast for the number of accidents that will occur in the month of May = enter your response here accidents (enter your response as a whole number).
- Room registrations in the Toronto Towers Plaza Hotel have been recorded for the past 9 years. To project future occupancy, management would like to determine the mathematical trend of guest registration. This estimate will help the hotel determine whether the future expansion will be needed. Given the following time-series data, develop a regression equation relating registrations to time (e.g., a trend equation). Then forecast year 11 registrations. Room registrations are in the thousands: Sr. No Year Registration (000) 1 2011 17 2 2012 16 3 2013 16 4 2014 21 5 2015 20 6 2016 20 7 2017 23 8 2018 25 9 2019 24An operation analyst is forecasting this year's demand for one of his company's products based on the following historical data: (4 year ago Quantity sold was 10842), (3 year ago Quantity sold was 11881 ), (2 year ago Quantity sold was 16064 ) and the last year Quantity sold was 19273 . The previous trend line had predicted 12250 for three years ago, 13000 for two years ago and 13750 for last year. What was the mean absolute percent error (MAPE) for these forecasts? a. 16.95 b. 2739 c. 0.17 d. 2985The following table gives the number of pints of type A blood used at Damascus Hospital in the past 6 weeks: Week of Pints Used August 31 360 September 7 370 September 14 412 September 21 383 September 28 371 October 5 371 a) The forecasted demand for the week of October 12 using a 3-week moving average= {__] Pints (round your response to two decimal places).