11 Jun Mini Case 1 Eli Orchid Has Designed A New Pharmaceutical Product, Orchid Relief, Which Improves The Night Sleep. Before Initiating Mass Production Of The Product, Eli Orchid Has Been Market-Testing Orchid Relief In O
Mini Case 1
Eli Orchid has designed a new pharmaceutical product, Orchid Relief, which improves the night sleep. Before initiating mass production of the product, Eli Orchid has been market-testing Orchid Relief in Orange County over the past 8 weeks. The daily demand values are recorded in the Excel file provided. Eli Orchid plans on using the sales data to predict sales for the upcoming week. An accurate forecast would be helpful in making arrangements for the company’s production processes and designing promotions.
Before a forecasting model is built and a forecast for the next week is generated, the COO of the company has asked the data analyst for an exploratory analysis of the demand.
Specifically, the COO has asked the analyst :
To provide a bar chart (with data labels rounded to two decimal points) showing the
Mini Case 1
Eli Orchid has designed a new pharmaceutical product, Orchid Relief, which improves the night sleep. Before initiating mass production of the product, Eli Orchid has been market-testing Orchid Relief in Orange County over the past 8 weeks. The daily demand values are recorded in the Excel file provided. Eli Orchid plans on using the sales data to predict sales for the upcoming week. An accurate forecast would be helpful in making arrangements for the company’s production processes and designing promotions.
Before a forecasting model is built and a forecast for the next week is generated, the COO of the company has asked the data analyst for an exploratory analysis of the demand.
Specifically, the COO has asked the analyst :
To provide a bar chart (with data labels rounded to two decimal points) showing the average demand for each week day (Sun., Mon., etc.)
To fit a simple linear regression model to the data and to provide its equation (d = a*t + b), along with R2 d =
R2=
To fit a multiple regression model with a dummy variable representing the weekend, and to provide the regression equation (d = a*t + b*w + c), along with Adjusted R2. d =
Adjusted R2=
To provide a run-series plot of the actual demand with simple regression and multiple regression overlay. Please explain to me how you get the chart. [add chart here]
To write a short paragraph explaining the observations and providing general recommendations for the next seven days demand forecast.
Data:
Day Date Weekday Daily Demand Weekend 1 4/25/2016 Mon 297 0 2 4/26/2016 Tue 293 0 3 4/27/2016 Wed 327 0 4 4/28/2016 Thu 315 0 5 4/29/2016 Fri 348 0 6 4/30/2016 Sat 447 1 7 5/1/2016 Sun 431 1 8 5/2/2016 Mon 283 0 9 5/3/2016 Tue 326 0 10 5/4/2016 Wed 317 0 11 5/5/2016 Thu 345 0 12 5/6/2016 Fri 355 0 13 5/7/2016 Sat 428 1 14 5/8/2016 Sun 454 1 15 5/9/2016 Mon 305 0 16 5/10/2016 Tue 310 0 17 5/11/2016 Wed 350 0 18 5/12/2016 Thu 308 0 19 5/13/2016 Fri 366 0 20 5/14/2016 Sat 460 1 21 5/15/2016 Sun 427 1 22 5/16/2016 Mon 291 0 23 5/17/2016 Tue 325 0 24 5/18/2016 Wed 354 0 25 5/19/2016 Thu 322 0 26 5/20/2016 Fri 405 0 27 5/21/2016 Sat 442 1 28 5/22/2016 Sun 454 1 29 5/23/2016 Mon 318 0 30 5/24/2016 Tue 298 0 31 5/25/2016 Wed 355 0 32 5/26/2016 Thu 355 0 33 5/27/2016 Fri 374 0 34 5/28/2016 Sat 447 1 35 5/29/2016 Sun 463 1 36 5/30/2016 Mon 291 0 37 5/31/2016 Tue 319 0 38 6/1/2016 Wed 333 0 39 6/2/2016 Thu 339 0 40 6/3/2016 Fri 416 0 41 6/4/2016 Sat 475 1 42 6/5/2016 Sun 459 1 43 6/6/2016 Mon 319 0 44 6/7/2016 Tue 326 0 45 6/8/2016 Wed 356 0 46 6/9/2016 Thu 340 0 47 6/10/2016 Fri 395 0 48 6/11/2016 Sat 465 1 49 6/12/2016 Sun 453 1 50 6/13/2016 Mon 307 0 51 6/14/2016 Tue 324 0 52 6/15/2016 Wed 350 0 53 6/16/2016 Thu 348 0 54 6/17/2016 Fri 384 0 55 6/18/2016 Sat 474 1 56 6/19/2016 Sun 485 1
verage demand for each week day (Sun., Mon., etc.)
To fit a simple linear regression model to the data and to provide its equation (d = a*t + b), along with R2 d =
R2=
To fit a multiple regression model with a dummy variable representing the weekend, and to provide the regression equation (d = a*t + b*w + c), along with Adjusted R2. d =
Adjusted R2=
To provide a run-series plot of the actual demand with simple regression and multiple regression overlay. Please explain to me how you get the chart. [add chart here]
To write a short paragraph explaining the observations and providing general recommendations for the next seven days demand forecast.
Data:
Day Date Weekday Daily Demand Weekend 1 4/25/2016 Mon 297 0 2 4/26/2016 Tue 293 0 3 4/27/2016 Wed 327 0 4 4/28/2016 Thu 315 0 5 4/29/2016 Fri 348 0 6 4/30/2016 Sat 447 1 7 5/1/2016 Sun 431 1 8 5/2/2016 Mon 283 0 9 5/3/2016 Tue 326 0 10 5/4/2016 Wed 317 0 11 5/5/2016 Thu 345 0 12 5/6/2016 Fri 355 0 13 5/7/2016 Sat 428 1 14 5/8/2016 Sun 454 1 15 5/9/2016 Mon 305 0 16 5/10/2016 Tue 310 0 17 5/11/2016 Wed 350 0 18 5/12/2016 Thu 308 0 19 5/13/2016 Fri 366 0 20 5/14/2016 Sat 460 1 21 5/15/2016 Sun 427 1 22 5/16/2016 Mon 291 0 23 5/17/2016 Tue 325 0 24 5/18/2016 Wed 354 0 25 5/19/2016 Thu 322 0 26 5/20/2016 Fri 405 0 27 5/21/2016 Sat 442 1 28 5/22/2016 Sun 454 1 29 5/23/2016 Mon 318 0 30 5/24/2016 Tue 298 0 31 5/25/2016 Wed 355 0 32 5/26/2016 Thu 355 0 33 5/27/2016 Fri 374 0 34 5/28/2016 Sat 447 1 35 5/29/2016 Sun 463 1 36 5/30/2016 Mon 291 0 37 5/31/2016 Tue 319 0 38 6/1/2016 Wed 333 0 39 6/2/2016 Thu 339 0 40 6/3/2016 Fri 416 0 41 6/4/2016 Sat 475 1 42 6/5/2016 Sun 459 1 43 6/6/2016 Mon 319 0 44 6/7/2016 Tue 326 0 45 6/8/2016 Wed 356 0 46 6/9/2016 Thu 340 0 47 6/10/2016 Fri 395 0 48 6/11/2016 Sat 465 1 49 6/12/2016 Sun 453 1 50 6/13/2016 Mon 307 0 51 6/14/2016 Tue 324 0 52 6/15/2016 Wed 350 0 53 6/16/2016 Thu 348 0 54 6/17/2016 Fri 384 0 55 6/18/2016 Sat 474 1 56 6/19/2016 Sun 485 1
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