In your calculations, try to not round numbers as much as possible or carryout calculations with as many decimals as you can. When entering numbers on the quiz as your final answer, round your answers to 2 decimal places. This means that if you round the final answer in one question and you need that answer for another question, you would use the unrounded version of that number for other questions.

Eli Orchid Company has designed a new pharmaceutical product, Orchid Relief that improves night sleep. Before mass production of the product, Eli Orchid has market tested Orchid Relief in only Orange County over the past 8 weeks. The daily demand as well as average daily price sold per package is recorded - there have been some variation in the prices as some retail stores have offered price promotions over time. Eli Orchid is now trying to use the sales pattern over the past 8 weeks to predict sales in US for the upcoming few months. An accurate forecast of demand and total revenue would be much helpful in arrangements for the company's production processes and design of price promotions over each week. The data is provided in file EliOrchid_OC demand.xlsx.

First, we would like to form forecasts for the daily demand. After reviewing the data, the marketing manager believes that there is a weekly repeating pattern in the demand likely due to changes in sleeping pattern of customers on different days.

1. Create a regression model that captures the impact of passage of time as well as the seasonality onDaily Demand. How many total predictor variables should be included inside the regression model?
2. What is the R-squared of the regression model that you developed in the last question (enter as a percentage, that is, enter a number between 0 and 100)?
3. From the regression model that you created, we can conclude that there is no significant linear trend, at 5% level of significance. Select one: True or false
4. What would be your forecast of daily demand for day 57 (the day after the data ends) using the regression method of Question 1?
5. What would be your forecast of daily demand for day 63 (one week ahead) using the regression method of Question 1?
6. What is the forecasting accuracy of the regression model you have created based on the mean absolute error?
7. Now, we would like to create a forecast of theaverage daily pricesfor the future. Looking at the data, we can conclude that the price data is a ... . (Use 5% level of significant where needed)

Select one:

Stationary time series

Linear trend series

Seasonal series with a significant linear time trend

Seasonal series without a significant linear time trend

1. What would be a forecast of prices for day 57 using the 5-day moving average method?
2. What would be a forecast of prices for day 63 using the 5-day moving average method?
3. What would be a forecast of prices for day 57 using the exponential smoothing method with smoothing constant of 0.7?
4. Which of the following methods provides the best accuracy for predicting prices based on the mean absolute percentage error?

Select one:

Exponential Smoothing method with smoothing constant of 0.5

Exponential Smoothing method with smoothing constant of 0.7

5-day moving average method