The monthly sales for Yazici Batteries, Inc., were as follows:MonthSalesJan Feb Mar Apr May Jun19 23 16 15 11 15 17اللهAug Sept Oct Nov Dec18 22 22 20 24This exercise contains only parts b and c.b) The forecast for the next month (Jan) using the naive method=sales (round your response to a whole number).The forecast for the next period (Jan) using a 3-month moving average approach =sales (round your response to two decimal places)The forecast for the next period (Jan) using a 6-month weighted average with weights of 0.10, 0.10, 0.10, 0.20, 0.20, and 0.30, where the heaviest weights are applied to the most recent month=sales (round your response to one decimal place).Using exponential smoothing with a 0.35 and a September forecast of 18.00, the forecast for the next period (Jan) = sales (round your response to two decimal places).Using a method of trend projection, the forecast for the next month (Jan) = sales (round your response to two decimal places).c) The method that can be used for making a forecast for the month of March isa trend projectiona 3-month moving averagea 6-month weighted moving averageexponential smoothingthe naive method

Given the monthly sales for Yazici Batteries, Inc…

Answered: The monthly sales for Yazici Batteries,…

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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Read the following prompt and complete 3.a and 3.b
From the data in the table it shows the 2019 daily return data for the S&P 500 Index (stocks) and the US 30 year Bond (bonds). How do I test the hypothesis that mean daily return on stocks exceeds the mean daily return on bonds? Please show the steps. S&P500 dailt returns data: Date Price Daily Return 2019-12-31 3230.78 0.294602% 2019-12-30 3221.29 -0.578082% 2019-12-27 3240.02 0.003398% 2019-12-26 3239.91 0.512817% 2019-12-24 3223.38 -0.019545% 2019-12-23 3224.01 0.086614% 2019-12-20 3221.22 0.494478% 2019-12-19 3205.37 0.445929% 2019-12-18 3191.14 -0.043230% 2019-12-17 3192.52 0.033529% US 30 Year Bond Date Price Daily Return 31-Dec-19 2.389 2.32% 30-Dec-19 2.335 0.78% 29-Dec-19 2.317 -0.03% 27-Dec-19 2.318 -0.16% 26-Dec-19 2.321 -0.71% 25-Dec-19 2.338 0.15% 24-Dec-19 2.334 -0.83% 23-Dec-19 2.354 0.31% 22-Dec-19 2.346 0.12% 20-Dec-19 2.344 -0.43%
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
4. I was able to convince the plan manager to allow me to make the change to the larger production plant. My job's on the line now! So I am monitoring it pretty closely. Here are the results for the day: Table 4. Production rate from pilot plant demonstration tests. Run # 1 2 3 4 5 P (pounds per hour) 2200 2200 2300 2200 2300 From these data, please estimate: a. Estimate the Type A uncertainty in these measurements. Be sure to include the units. b. Estimate the Type B uncertainty associated with these measurements. Be sure to include units. C. Determine the uncertainty in the measurement of pressure in the cylinder. Be sure to include units. State your answer as average +/- uncertainty.
Consider the following time series data. Month 1. 3 4 5 7 Value 20 11 19 11 19 22 15 Round your answers to two decimal places. a. Compute MSE using the most recent value as the forecast for the next period. Mean squared error is What is the forecast for month 8? b. Compute MSE using the average of all data available as the forecast for the next period. Mean squared error is What is the forecast for month 8? c. Which method appears to provide the better forecast? - Select your answer -
Consider the following quarterly time series. Quarter 1 2 3 4 Year 1 923 Year 2 1,112 1,056 1,156 992 Year 3 1,078 1,243 1,124 1,124 1,254 1,301 1,198 a. Construct a time series plot. What type of pattern exists in the data? b. Use a multiple regression model with dummy variables as follows to develop an equation to account for seasonal effects in the data. Qtr1 = 1 if quarter 1, 0 otherwise; Qtr2 = 1 if quarter 2, 0 otherwise; Qtr3 = 1 if quarter 3, 0 otherwise. c. Compute the quarterly forecasts for next year based on the model developed in part b.
The figure below illustrates monthly data over 10 years. What method would you expect would perform the best at forecasting the data series in period 121? Explain the (i) strengths of your selected method for this data and the (ii) weaknesses of alternative methods for this data. Consider the concepts of level, seasonality, trend, and noise in your answer.
Week 1 2 3 4 5 6 Value 18 13 16 11 17 14 Using the naive method (most recent value) as the forecast for the next week, compute the following measures of forecast accuracy. d. What is the forecast for week 7 (to 2 decimals)?
Refer to the gasoline sales time series data in the given table. Compute four-week and five-week moving averages for the time series. Round your answers to two decimal places. Compute the MSE for the four-week and five-week moving average forecasts. Round your intermediate calculations and final answers to two decimal places. What appears to be the best number of weeks of past data (three, four, or five) to use in the moving average computation? Recall that MSE for the three-week moving average is 12.89.
or each predictor variable, state the P-value and determine whether the predictor variable is significant in explaining delivery charges. Predictor variables P-value Significant in explain Delivery charges Distance .................. .............. Weight .................. .............. Boxes ............... .................
Plot the sales revenue figures and appropriate trend line on a graph and identify from this the choice of model for working out the seasonal component. Using the appropriate seasonal model, complete the calculations that are required to produce the required forecasts for quarters 1, 2, 3 and 4 of 2021. On the basis of your analysis explain the recommendation that you would make to ALAW Ltd regarding its future plans.
The forecast for year 6 using a weighted 2-year moving average with weights of 0.350.35 and 0.650.65 (the weight of 0.650.65 is for the most recent period) is what?

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