(Please answer only question 5) The consumption of Electrical and Electronic Equipment (EEE) is strongly linked to widespread global economic development. It has become indispensable in modern societies and is enhancing living standards. Higher levels of disposable incomes, growing urbanization and mobility, and further industrialization in some parts of the world are leading to growing amounts of EEE. After its use, EEE is disposed of, generating a waste stream that contains hazardous and valuable materials. This waste stream is referred to as e-waste. In 2019, the world generated a striking 53.6 metric tons of e-waste, an average of 7.3 kg per capita (per person). The table below shows the per capita electronic waste generation worldwide from 2010 to 2019, in kilograms per capita. Year 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 Volume of waste (in kg per capita) 5.0 5.2 5.4 5.7 6.4 6.6 6.8 6.9 7.1 7.3 2. Using a smoothing coefficient of 0.6, exponentially smooth the series and forecast the volume of waste generated in 2020. Include a screenshot of your Excel output. 3. Compute a linear trend forecasting equation and forecast the volume of waste generated in 2020, 2021, and 2022. Include a screenshot of your Excel output. 4. Compute a quadratic trend forecasting equation and forecast the volume of waste generated in 2020, 2021, and 2022. Include a screenshot of your Excel output. 5. Compute the different measures of forecast accuracy MAD, MSE, and MAPE for the models in Problems 2, 3, and 4. Based on these measures, which model do you recommend to be used for forecasting? Include a screenshot of your Excel output for each model.
(Please answer only question 5) The consumption of Electrical and Electronic Equipment (EEE) is strongly linked to widespread global economic development. It has become indispensable in modern societies and is enhancing living standards. Higher levels of disposable incomes, growing urbanization and mobility, and further industrialization in some parts of the world are leading to growing amounts of EEE. After its use, EEE is disposed of, generating a waste stream that contains hazardous and valuable materials. This waste stream is referred to as e-waste. In 2019, the world generated a striking 53.6 metric tons of e-waste, an average of 7.3 kg per capita (per person). The table below shows the per capita electronic waste generation worldwide from 2010 to 2019, in kilograms per capita.
| Year | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 |
|
Volume of waste (in kg per capita) |
5.0 | 5.2 | 5.4 | 5.7 | 6.4 | 6.6 | 6.8 | 6.9 | 7.1 | 7.3 |
2. Using a smoothing coefficient of 0.6, exponentially smooth the series and forecast the volume of waste generated in 2020. Include a screenshot of your Excel output.
3. Compute a linear trend
4. Compute a quadratic trend forecasting equation and forecast the volume of waste generated in 2020, 2021, and 2022. Include a screenshot of your Excel output.
5. Compute the different measures of forecast accuracy MAD, MSE, and MAPE for the models in Problems 2, 3, and 4. Based on these measures, which model do you recommend to be used for forecasting? Include a screenshot of your Excel output for each model.
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