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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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.
What year is the title of the grapth (image attached) most likely to be referring to when it says the future (Likelihood of industries becoming automated in the future")?
9. A wildlife researcher is interested in predicting a mammal’s lifespan (in years) based on its average number of hours of sleep per night. Data was obtained from a large random sample of mammals, and the regression equation turned out as follows: Predicted lifespan = 36.93 – 1.64 (average hours of sleep) Which one of the following statements is a correct interpretation of this equation? 1. Approximately 1.64% of the variability in lifespan can be explained by the regression equation. 2. As lifespan increases by one year, average hours of sleep is predicted to decrease by 1.64 hours. 3. As lifespan increases by one year, average hours of sleep is predicted to increase by 36.93 hours. 4. As average hours of sleep increases by one hour, lifespan is predicted to increase by 36.93 years. 5. As average hours of sleep increases by one hour, lifespan is predicted to decrease by 1.64 years.
9. Cell Phones The following table shows the num- ber, in millions, of cell phone subscribers in the United States at the end of the given year. Year Subscribers (millions) 2010 296.3 2011 316.0 2012 326.5 2013 335.6 2014 355.4 a. Plot the data points. b. Use exponential regression to construct an expo- nential model for the subscriber data. c. Add the graph of the exponential model to the plot in part a. d. What was the yearly percentage growth rate from the end of 2010 through the end of 2014 for cell phone subscribership? e. In 2014, an executive had a plan that could make money for the company, provided that there would be at least 380 million cell phone subscribers by the end of 2016. Solely on the basis of an exponen- tial model for the data in the table, would it be rea- sonable for the executive to implement the plan?
An analyst must decide between two different forecasting techniques for weekly sales of roller blades: a linear trend equation and the naive approach. The linear trend equation is F+ = 122 +1.9t, and it was developed using data from periods 1 through 10. Based on data for periods 11 through 20 as shown in the table, which of these two methods has the greater accuracy if MAD and MSE are used? (Round your intermediate calculations and final answers to 2 decimal places.) t 11 12 13 14 15 16 17 18 19 20 Units Sold 143 149 152 145 155 152 154 159 159 162 Click here for the Excel Data File MAD (Naive) MAD (Linear) MSE (Naive) MSE (Linear))
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 ???
Which of the following applies the concept of “layering” to increase the sensitivity of a research analysis? Group of answer choices a. Chi-square Contingency Tables b. Binary Logistic Regression c. Regression d. Answers 1 and 2 only e. Answers 1, 2 and 3
Average temperature in Winter and Summer in a region for 2016-2019 is given below. Year Winter Summer 2016 20 45 2017 35 44 2018 28 43 2019 29 48 Assume the additive time series model with no trend and using the above data, answer the following questions. 1. Compute two period moving averages MA(2). 2. What is the advantage of computing MA(2). 3. Do you need to compute CMA. Why or why not. 4. Estimate the seasonal factors for Winter and summer. 5. Forecast the average temperature for Fall and Spring 2020 assuming CL=2 and IR=0
Consider the following time series data. Week 1 2 3 4 5 6 Value 19 14 16 11 17 15 Develop the three-week moving average forecasts for this time series. Compute MSE and a forecast for week 7 (to 2 decimals if necessary). MSE The forecast for week Use alpha=.2 to compute the exponential smoothing forecasts for the time series. Compute MSE and a forecast for week 7 (to 2 decimals). MSE The forecast for week Compare the three-week moving average approach with the exponential smoothing approach using alpha=.21 . Which appears to provide more accurate forecasts based on MSE?- Select your answer -The three-week moving average provides a better forecast since it has a smaller MSEThe exponential smoothing approach provides a better forecast since it has a smaller MSEThe three-week moving average provides a better forecast since it has a larger MSE

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Month	Sales (in thousands of AUS dollars)
January	8.5
February	8
March	8
April	9