BA 5iphon scam group project #3

 BA 5iphon scam group job 3 Article

п»їBA 5iphon scam Practical Organization Analysis

Group Project several: Time Series Analysis and Forecasting

Due: March 16, 2013 at the start of the class

TERM

NAME

NAME

1 . Place a time series plot. Touch upon the actual trend and seasonal habits. This is the own declaration. There is no need to operate any predicting model here.

(Insert the plot in this article. )

(Insert your comments here. )

2 . Forecasting using a Multiplicative Model:

a. Utilize the time series decomposition technique (textbook Phase 18. six; packet pp. 78-80) to deseasonalize time series and obtain seasonal crawls. Fit a linear tendency model to the deseasonalized period series. Report the installed model as well as the seasonal indices.

Seasonal Pattern

Month

In season Index

January

1 . 154008

February

1 . 00262

March

1 . 087917

April

0. 923024

May

1 . 058668

June

zero. 882363

September

0. 997589

August

1 . 118552

September

0. 826317

October

zero. 888524

The fall of

0. 908866

December

1 . 039627

Thready Trend

m. Describe the underlying trend and in season patterns based upon the multiplicative model designed in Question 2-a. Quantify the statements. For example , " There may be an way up trend. ” and " Sales in December can be higher. ” are not satisfactory and will bring about point deduction. " Revenue goes up by about $20, 000 every month, give or have $1, 500 for a 95% confidence. ” and " Sales in December is often 16% greater than the average sales in a common year. ” are what I am looking for. Does this version support the observation involved 1?

Product sales goes up by about $183, 010 every month, give or have $262 for a 95% confidenc Sales in December is generally 16% more than the average sales in a typical year;

c. Use this model and prediction sales to get January through December from the fourth season. Report the forecasts in the table below.

Sales Predictions

Month

Sales Forecasts

January

Forecast

February

March

286. 75

04

267. 08

May

272. 42

Summer

216. forty two

July

220. 08

Aug

174. '08

September

180. 42

March

186. seventy five

November

143. 75

January

160. 42

3. Forecasting using an Additive Model:

a. Suit the time series data using the seasonal regression model having a linear tendency (textbook Chapter 18. a few; packet pp. 74-77). Statement the fixed model. Tip: you need to make dummy parameters.

Seasonal Regression with Geradlinig Trend

(Insert the regression output here. )

n. Describe the underlying tendency and periodic patterns based upon the additive model developed in Question 3-a. Quantify your statements (see examples I actually listed in Question 2-b). Does this model support your remark in Question 1?

(Insert the comments in this article. )

c. Use this model and prediction sales to get January through December from the fourth year. Report the forecasts in the table beneath.

Sales Predictions

Month

Product sales Forecasts

January

February

Drive

April

May

June

September

August

Sept

October

November

December

4. If you need to choose one style and use its forecasts, which of the two designs will you select? Provide numerical evidence to support your choice.

(Insert your feedback here. )


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