Question 5The following monthly sales of chocolate boxes (in thousands of AUS dollars) have beenrecorded for January, February, March, and April, respectively: 8.5, 8, 8, 9. Examining theforecasting accuracy for the month of April only, explain which of the following forecastingmethod would you recommend: the Naïve method, the Average method, or the Simpleexponential smoothing method (assuming alpha=0.85 and initial state of 8)?

The given data for sales of chocolate boxes are given as follows: Month Sales (in thousands of…

Answered: Question 5 The following monthly sales…

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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Please answer part e
1.8 Compute and compare the total electricity consumption of Sub-Saharan Africa to the United States in 2012. The population of Sub-Saharan Africa was 926 million, and the United States was 314 million. The per person consumption in the United States in 2012 was 12.96 MWh per year. Consult Fig. 1.11 for the per person consumption in Sub-Saharan Africa.
I have this much done, but need help with E, i,ii,iii Which asks: Choose a year other than the one from part 2c. A birth year or the first year might be a good choice(I picked 1990) .i. Use the trend line equation to estimate the y-value for the year of your choice. Find the relative change in the y-value for your chosen year compared to the last year’s value you found in part 2c iii. Interpret the meaning of your relative change value. F.Should we use the trend line model to estimate the y-value for 2020? Why or why not? Note: Hey there! Thank you for the question. As you have posted multiple sub-parts, we have helped you solve the first three parts, according to our policy. Please re-post the other parts with complete background information. Step 2 Calculation: As it is required to build a scatterplot with a trendline, we need to use Excel, where this feature is available. The independent or predictor variable is year, which we have denoted as x, and the dependent…
If a forecaster wanted to choose a forecasting model that avoided having a large forecasting error in any given period, which error metric should they use when evaluating their forecasting options? CFE O AFE MAD MAPE O MSE
In Table L-1, the total value of imports of Indonesian goods and services from China for the 2010-2019 period is reported on a quarterly basis. With this data you are asked to analyze it with the Trend model and Seasonal Index. Question: a. Calculate a moving average with the average period length of 4 or MA (4) [why four?] And put the results in a column. Beware of selecting the rows to place the average result. b. Calculate the moving average with the length of the average period is 2 from the data in column MA (4) and the result is given the symbol MA (2x4) and the result is placed in a column. Beware of selecting the rows to place the average result. c. Calculate the ratio to the Moving Average (Y / MA (2x4)) and place it in a new column! d. Calculate a seasonal index and interpret the results! e. Create a Deseasonalized data series and place it in a new column! f. Plot the Dessasonalized data series and read the long-term movement patterns of data! Remember that understanding of…
The data presented is a multiple logistic regression analysis and the models are shown below. In the models below, the data are coded as follows: p = the proportion of children with a diagnosis of ADHD, Child Exposed and Father’s Diagnosis are coded as 1 = yes and 0 = no. What is the odds ratio adjusted for father’s diagnosis? (Hint: Use only the appropriate model to find the odds ratio) (1) = –2.216 + 1.480 Child exposed (2) Father with diagnosis: = –1.665 + 1.297 Child exposed (3) Father without diagnosis: = –2.343 + 0.823 Child exposed (4) = –2.398 + 1.501 Child exposed + 0.906 Father’s diagnosis
Problem #1b: Use a two-month weighted moving average to create a forecast for May, using the weights: 0.6 and 0.4 (largest weight is for the most recent data). Calculate the forecast errors associated the weighted moving average forecasts. Month Demand January 18 February 23 March 20 April 16 May ???
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Month	Sales (in thousands of AUS dollars)
January	8.5
February	8
March	8
April	9