A pharmacist has been monitoring sales of a certain over-the-counter pain reliever. Daily sales during the last 15 days wereDay:1Number sold: 36238Day:1011Number sold: 48 523 2 12 55MSE42Click here for the Excel Data File4441354548145664915577950849952a. Assume the data refer to demand rather than sales. Using trend-adjusted exponential smoothing with an initial forecast of 50 forDay 8, an initial trend estimate of 2, and a = B = .3, develop demand forecasts for Days 9 through 16. Then compute the resultant MSEusing the error values from Days 8 through 15. (Round your intermediate period-by-period forecast and error values to 3 decimalplaces. Round your final MSE answer to 3 decimal places.)

Using trend-adjusted exponential smoothing, we can solve this problem. Trend-adjusted exponential…

Answered: A pharmacist has been monitoring 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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Below are given the annual production figures (in thousand tonnes) of a fertiliser factory : Year 2011 2012 2013 2014 2015 2016 2017 Production : 70 75 90 91 95 98 100 (1) Fit a straight line trend by the method of least squares and tabulate the trend values. (i) Convert your annual trend equation into a monthly trend equation.
The table lists the cost in millions of dollars for a 30-second commercial for selected years. Year 1994 1998 2004 2008 2011 Cost 33.7 34.2 35.0 35.5 35.9 a. Find a linear function f that models the data. b. Estimate the cost in 2010 and compare the estimate to the actual value of $36.6 million. Did your estimate involve interpolation or extrapolation? a. Use linear regression to determine the equation of the line that best fits the data. The linear function is f(x) = (Type an expression using x as the variable. Use integers or decimals for any numbers in the expression. Round to three decimal places as needed.)
A small detergent company in Karachi is confident that their sales are growing significantly week by week. Treating sales for the previous eight weeks as a typical example of this increasing trend, they recorded them in PKR 1000's and examined the results. Week 1 2 3 4 5 6 Sales (in PKR) 2.59 2.55 2.63 2.68 2.74 2.83 2.86 2.99 a) Plot a scatter diagram between week and sales. b) Find the equation of regression line. c) Find the expected sales for 12 week d) Find the correlation coefficient between the two variables.
Consider the following data: Month Jan-14 Feb-14 Profit ($) 16,113 16,263 Monthly Profit of an Auto Repair Shop Mar-14 Apr-14 May-14 Jun-14 Jul-14 Aug-14 Sep-14 15,055 17,117 18,763 17,210 19,141 18,468 20,254 Step 3 of 4: Determine the exponential smoothing forecast for the next time period using a smoothing constant of 0.35. If necessary, round your answer to one decimal place.
Describe the patterns in quantity sold and own and rival prices during this time period using basic descriptive statistics. Graphs are welcome as well. Take the logs of the variables, and estimate the demand function. Interpret the R-square. Interpret the coefficients for logP and logPsub Interpret the p-values associated with each independent variable Are consumers price sensitive? Why or why not? (be as precise as you can – you have estimates!). Does this price sensitivity make sense given the good we are examining? How sensitive are our consumers to changes in the rival good’s price? Explain in detail. Suppose we decide to charge a per ounce price of $2, while at the same time our rival charges a price of $2.15. All else equal, what would you expect sales to be? How confident are you in your forecast? Suppose we are charging a price of $2 and our current marginal cost is $1.50 Are we maximizing profits at this price? If not, should we raise or lower price? Why?
A soda company is monitoring the number of cases of soda it produces as a function of time. A manager checked the line several times a day for a week. She then graphed the combined data. Assume that we should see a linear (best straight line) relationship between time and production. What is the best estimate of the number of cases that will be produced in 1 hr on a typical day? 8. 7 1 0.5 1.5 2 2.5 Time in hours Select one: а. 1 b. 2 C. 2.5 d. 4.5 е. 7 Cases of soda produced
Find forecasted value for 2017using α = 0.5 and α = 0.7 using exponential smoothing method.Which α is better Years Sales Forecasted sale 2012 25 ……. 2013 17 2014 21 2015 30 2016 27 2017 ?
The below data represent the advertising expenditure and sales of Liu Liu Ltd from year 2010 to 2019. Table: Advertising Expenditure vs. Sales from 2010 to 2019 Year Advert. Expenditure Sales Growth rate of Ad. Expenditure Growth rate of Sales (x, £000) (y, £000) 2010 19 84 2011 22 108 18% 29% 2012 32 82 46% -24% 2013 42 86 32% 6% 2014 47 84 11% -3% 2015 46 94 -2% 11% 2016 58 103 27% 10% 2017 63 115 8% 12% 2018 70 106 11% -8% 2019 80 142 15% 34% Total 479 1004 a. What is the statistical relationship shown in the table and list out 3 characteristics of this relationship?
The following table contains the demand from the last 10 months: MONTH ACTUAL DEMAND 1 31 2 34 3 33 4 35 5 37 6 36 7 38 8 40 9 40 10 41 a. Calculate the single exponential smoothing forecast for these data using an α of 0.30 and an initial forecast ( F1�1 ) of 31. Note: Round your intermediate calculations and answers to 2 decimal places. b. 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�1 ) of 1, and an initial exponentially smoothed forecast ( F1�1 ) of 30. Note: Round your intermediate calculations and answers to 2 decimal places. c-1. Calculate the mean absolute deviation (MAD) for the last nine months of forecasts. Note: Round your intermediate calculations and answers to 2 decimal places. c-2. Which is best? multiple choice Exponential smoothing with trend forecast Single exponential smoothing forecast
Month Actual demand Forecasted Demand 1 950 1000 2 1070 1000 3 1100 1000 4 960 1000 5 1090 1000 6 1050 1000 For the dataset above, compute the overall MAPE.

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