The accompanying dataset shows the monthly number of new car sales in the last two years. Implement the Holt-Winters multiplicative seasonality model with trend. Find the best combination of a,B, and y to minimize MSE.α = 0.2, ß= 1, y = 0.2; α = 0.6, p=0.4, y = 0.6Click the icon to view the new car sales data.First find the MSE for each set of values, x, ß, and y.(Round to the nearest whole number as needed.)αВYMSE0.210.20.60.40.6kample Get more help.New Car Sales DataMonthJanFebMarAprMayJunJulAugSepOctNovDecJanFebMarAprMayJunJulAugSepOctNovDecPeriod128678 AWN34591011234567181920122 23 24Units39,80740,08447,43747,30049,20751,47846,46645,20544,80046,98642,16544,18442,22845,42754,07250,92353,57054,92554,45256,08252,17750,08748,50849,2760Xk answer

In this question, we are required to fit a Holt-Winters multiplicative seasonality model with trend…

Answered: The accompanying dataset shows the…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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STER. 1. Wine Consumption. The table below gives the U.S. adult wine consumption, in gallons per person per year, for selected years from 1980 to 2005. a) Create a scatterplot for the data. Graph the scatterplot Year Wine below. Consumption 2.6 b) Determine what type of model is appropriate for the 1980 data. 1985 2.3 c) Use the appropriate regression on your calculator to find a Graph the regression equation in the same coordinate plane below. d) According to your model, in what year was wine consumption at a minimum? A e) Use your model to predict the wine consumption in 2008. 1990 2.0 1995 2.1 2000 2.5 2005 2.8
Please solve for #3.
The table below shows the number of state-registered automatic weapons and the murder rate for several Northwestern states. xx11.68.46.73.52.72.62.30.7yy1410.99.976.26.35.74.3 xx = thousands of automatic weaponsyy = murders per 100,000 residents This data can be modeled by the equation y=0.87x+3.83.y=0.87x+3.83. Use this equation to answer the following;A) How many murders per 100,000 residents can be expected in a state with 8.6 thousand automatic weapons?Answer = Round to 3 decimal places.B) How many murders per 100,000 residents can be expected in a state with 6.6 thousand automatic weapons?Answer = Round to 3 decimal places.
The table below shows the number of state-registered automatic weapons and the murder rate for several Northwestern states. 11.8 8.6 6.8 3.7 2.8 2.6 2.6 0.9 14.1 11.5 9.8 6.9 6.6 6.5 6.2 4.6 thousands of automatic weapons y = murders per 100,000 residents %3D This data can be modeled by the equation y = 0.86x + 3.99. Use this equation to answer the following; A) How many murders per 100,000 residents can be expected in a state with 7.7 thousand automatic weapons? Answer = Round to 3 decimal places. B) How many murders per 100,000 residents can be expected in a state with 1.9 thousand automatic weapons? Answer = Round to 3 decimal places.
The table below shows the number of state-registered automatic weapons and the murder rate for several Northwestern states. 11.4 8.5 6.6 3.6 2.3 2.5 2.1 0.6 y 13.4 11.4 9.3 7.2 5.9 5.9 5.6 4.3 x = thousands of automatic weapons y = murders per 100,000 residents This data can be modeled by the equation y = 0.85x + 3.88. Use this equation to answer the following: Special Note: I suggest you verify this equation by performing linear regression on your calculator. A) How many murders per 100,000 residents can be expected in a state with 10 thousand automatic weapons? Round to 3 decimal places. B) How many murders per 100,000 residents can be expected in a state with 9 thousand automatic weapons? Answer = Answer= Round to 3 decimal places.
The accompanying dataset shows the monthly number of new car sales in the last two years. Implement the Holt-Winters multiplicative seasonality model with trend. Find the best combination of α, β, and γ to minimize MSE. α=0.2, β=1, γ=0.2; α=0.6, β=0.4, γ=0.6 First find the MSE for each set of values, α, β, and γ. (Round to the nearest whole number as needed.) α β γ MSE 0.2 1 0.2 enter your response here 0.6 0.4 0.6 enter your response here Month Period Units Jan 1 39,808 Feb 2 40,081 Mar 3 47,440 Apr 4 47,297 May 5 49,210 Jun 6 51,479 Jul 7 46,466 Aug 8 45,208 Sep 9 44,801 Oct 10 46,990 Nov 11 42,161 Dec 12 44,187 Jan 13 42,227 Feb 14 45,421 Mar 15 54,076 Apr 16 50,926 May 17 53,572 Jun 18 54,921 Jul…
The table below shows the number of state-registered automatic weapons and the murder rate for several Northwestern states. x 11.9 8.6 7.1 3.8 2.3 2.3 2.2 0.3 y 14.3 11 9.9 7.3 5.7 5.7 5.6 4.1 x = thousands of automatic weaponsy = murders per 100,000 residentsThis data can be modeled by the equation y=0.87x+3.75. Use this equation to answer the following; Special Note: I suggest you verify this equation by performing linear regression on your calculator.A) How many murders per 100,000 residents can be expected in a state with 5 thousand automatic weapons?Answer = Round to 3 decimal places.B) How many murders per 100,000 residents can be expected in a state with 10.5 thousand automatic weapons?Answer = Round to 3 decimal places.
The table below shows the number of state-registered automatic weapons and the murder rate for several Northwestern states. x11.48.36.93.92.32.52.60.8y13.411.410.17.45.96.36.34.5x= thousands of automatic weaponsy= murders per 100,000 residents This data can be modeled by the equation y=0.85x+4.05.Use this equation to answer the following; Special Note: I suggest you verify this equation by performing linear regression on your calculator. A) How many murders per 100,000 residents can be expected in a state with 7.8 thousand automatic weapons? Answer = Round to 3 decimal places. B) How many murders per 100,000 residents can be expected in a state with 1.4 thousand automatic weapons? Answer = Round to 3 decimal places.
For the data set, perform exponential smoothing with alpha = 0.1 to determine the forecast for week 20. week Sales (1000s of gallons) 1 17 2 21 3 19 4 23 5 18 6 16 7 20 8 18 9 22 10 20 11 15 12 22 13 31 14 34 15 31 16 33 17 28 18 32 19 30
Use the data given in the three scenarios below to produce scatter plots, determine the type of model demonstrated in each example and then answer the questions based on the graph you created. Scenario 1 Rescue 911 In a rural community, fire and paramedic crews must service a large region. They often cover 2 or 3 towns several kilometres apart. These crews travel, on average, 70 km/h to their destination. The Chief of Station 42 wants to determine the time required to reach the patient's home (response time) for his region. The station is located next to the local hospital. The Chief knows the distances to each community that he services, including Deerborn, the furthest community, at 55 km away. Using the data below, create a scatter plot showing the relationship between distance to patient and response time. Distance to Patient (km) Response Time (minutes) 10 8.6 15 12.9 40 34.3 50 42.9 55 47.1
Does this graph have trends and/or seasonality? What forecasting method is suitable for this data?
We want to predict the selling price of a house in Newburg Park, Florida, based on the distance the house lies from the beach. Suppose that we're given the data in the table below. These data detail the distance from the beach (x, in miles) and the selling price (y, in thousands of dollars) for each of a sample of fifteen homes sold in Newburg Park in the past year. The data are plotted in the scatter plot in Figure 1. Also given is the product of the distance from the beach and the house price for each of the fifteen houses. (These products, written in the column labelled "xy", may aid in calculations.) Distance from the beach, x (in miles) Selling price, y (in thousands of dollars) xy 5.0 270.1 1350.5 11.5 205.9 2367.85 5.9 309.4 1825.46 12.2 200.6 2447.32 2.6 307.1 798.46 5.9 266.0 1569.4 8.3 297.3 2467.59 18.3 224.2 4102.86 6.5 242.4 1575.6 12.1 192.6 2330.46 11.6 229.4 2661.04 9.5 230.9 2193.55 10.1 277.0 2797.7 13.5 270.8 3655.8 6.2…

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