Question threea. The time plot below is for the natural logarithm of quarterly investment in West Germanybetween 1960.1 - 1982.4. 6log investment6.86.66.46.265.85.65.45.250Model A:20Model B:40Observation60Avt = a + pvt-1 +Does the series look stationary? Justify your answer.b. A researcher decides to test whether or not the log investment series in part (a) is stationaryusing the Augmented Dickey-Fuller (ADF) test. He considers basing the test on one of thefollowing two models:Pi=1Ave = a + ßt + pvt-1+0₁ Vt-1 + etР80i=1E0₁ Vt-i + et100[CI-EwhereVtthe basis for the ADF test: Model A or Model B? Be sure to justify your answer.@y₁-₁+1=0denotes the natural logarithm of investment. Which model should the researcher use as

The series does not look stationary. It is because from the given time-series plot it is evident…

Answered: Model A: Observation Does the series…

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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A school board has a plan to increase participation in the PTA. Currently only about 27 parents attend meetings. Suppose the school board plan results in logistic growth of attendance. The school board believes their plan can eventually lead to an attendance level of 81 parents. In the absence of limiting factors, the school board believes its plan can increase participation by 10% each month. Let m denote the number of months since the participation plan was put in place, and let P be the number of parents attending PTA meetings. (a) What is the carrying capacity K for a logistic model of P versus m? K = (b) Find the constant b for a logistic model. b = (c) Find ther value for a logistic model. Round your answer to three decimal places. r = (d) Find a logistic model for P versus m. P =
3
The following table shows the average price of a two-bedroom apartment in downtown New York City during the real estate boom from 1994 to 2004.† t 0 (1994) 2 4 6 8 10 (2004) Price($ million) 0.38 0.40 0.60 0.95 1.20 1.60 (a) Use exponential regression to model the price P(t) as a function of time t since 1994. (Round the coefficients to 3 decimal places.) P(t) = Select a sketch of the points and the regression curve. b) Extrapolate your model to estimate the cost of a two-bedroom downtown apartment in 2005. (Round your answer to one decimal place.) $ million
Consider the following time series data. Week 1 2 3 4 5 6 Value 17 11 15 10 16 13
The following table contains the demand from the last 10 months. Calculate the exponential smoothing with trend forecast for these data, using an α of 0.30, a δ of 0.30, an initial trend forecast (T1) of 1, and an initial exponentially smoothed forecast (F1) of 30. Round your intermediate calculations and answers to two decimal places.
Actual sales of a company (in millions of dollars) for January through April are shown below. Month Sales January 17 February 26 March 31 April 39 May (a) Use a = 0.3 to compute the exponential smoothing values for sales. Compute MSE and forecast sales for May. Assume the forecast for the initial period (January) is 17. Show all of your computations from February through May. (Round your squared forecast error values and MSE to two decimal places.) Month Sales Forecast Squared Forecast Error January 17 February 26 March 31 April 39 May MSE = (b) Use a = 0.1 to compute the exponential smoothing values for sales. Compute MSE and forecast sales for May. Assume the forecast for the initial period (January) is 17. Show all of your computations from February through May. (Round your squared forecast error values and MSE to two decimal places.) Month Sales Forecast Squared Forecast Error January 17 February 26 March 31 April 39 Мay MSE =
How do you calculate an exponential smoothing model using alpha = 0.5 and 0.8. What is the prediction for 7/1/2021 for each technique? Forecasts Date Day of Week Rooms Sold MA 2 MA 4 ES (0.8) ES (0.5) 6/1/2021 Tuesday 123 6/2/2021 Wednesday 109 6/3/2021 Thursday 140 116.0 6/4/2021 Friday 199 124.5 6/5/2021 Saturday 179 169.5 142.8 6/6/2021 Sunday 140 189.0 156.8 6/7/2021 Monday 117 159.5 164.5 6/8/2021 Tuesday 132 128.5 158.8 6/9/2021 Wednesday 108 124.5 142.0 6/10/2021 Thursday 151 120.0 124.3 6/11/2021 Friday 182 129.5 127.0 6/12/2021 Saturday 168 166.5 143.3 6/13/2021 Sunday 161 175.0 152.3 6/14/2021 Monday 119 164.5 165.5 6/15/2021 Tuesday 133 140.0 157.5 6/16/2021 Wednesday 139 126.0 145.3 6/17/2021 Thursday 146 136.0 138.0 6/18/2021 Friday 168 142.5 134.3 6/19/2021 Saturday 188 157.0 146.5…
Using the time series data in the table, respond to the following items. Period Sales 1 $ 1,001 2 1,129 3 841 4 1,044 5 1,014 6 703 7 682 8 712 9 646 10 686 11 909 12 469 13 566 14 488 15 688 16 675 17 303 18 381 d-1. Compute all possible forecasts using a trend forecasting model using simple linear regression? (Round your answers to 2 decimal places.) Period Sales Predicted Sales Absolute Error 1 1,001 1,026.85 25.85 2 1,129 990.60 138.40 3 841 954.35 113.35 4 1,044 918.10 125.90 5 1,014…
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The following time-series diagram tracks the performance of two stocks during the month of October. Describe the overall trend of the two stocks. By what percentage did the value of each stock increase or decrease during the month of October? Stock 2 $200 Stock 1 T$150 Stock 2 -$100 Stock 1 T$ 50 1 5 11 16 21 26 31 October a. Stock 2 increased by 200%. Stock 1 decreased by 50%. b. Stock 2 increased by 100%. Stock 1 decreased by 50% c. Stock 2 increased by 200%. Stock 1 decreased by 33% d. Stock 2 increased by 100%. Stock 1 decreased by 33%.
Customer interarrival times at a store are exponentially distributed with rate 8 customers per hour. Customers are served by a single customer service representative (CSR), with an average service time of 5 minutes. Service times are also exponentially distributed. Which of the following statements are true? (Please tell me why option B is the right option) A. The average time spent waiting for the CSR is 15 minutes. B. The store is busy (has customers) 33% of the time C. The probability of having one or more customer in the store is 0.222. D. None of the answers are correct. E. The average number of customers in the store is 2.0.
The world population over a period of 250 years has been estimated by the U.N. (see table below). Year Population [billion] 1800 1.00 1850 1.30 1900 1.70 1950 2.50 1970 3.70 1990 5.30 2005 6.50 2020 7.60 2050 9.10* *) prediction Source: U.S Census Bureau and UN. A suggested model to describe the population over time is: P= L 1+ e-k(x-xo) Where p is the population, x is the year and L, k and x, are constants. I represents the all time maximum of the population. a) Fit the model to the data using non-linear regression using initial guesses L = 20, k = 0.01 and x) = 2050. What will be the all time maximum of the world population according to this model? b) Find the coefficient of determination (R²) for the fit. (If you did not answer question a), you may use the initial guesses as parameter values).

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