Forecasting Nike Quarterly Revenue with Regression Analysis

Running Head: REQUIREMENT ANALYSIS AND MODELLING
Requirement Analysis and Modelling
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1REQUIREMENT ANALYSIS AND MODELLING
Table of Contents
Forecast of Nike Quarterly Revenue with respect to E-commercial sales:.....................................2
Forecasting of Nike Quarterly Revenue:.........................................................................................3
Regression re-integration and Nike revenue forecast:.....................................................................4
1. Double Exponential Smoothing for Nike Revenue (in B).......................................................4
2. Forecast of Nike Revenue Q1 2018 thru Q4 2019 (Time Series Plot)....................................6
Regression:......................................................................................................................................7
1. Regression of “US GDP” and “China GDP”...........................................................................7
2. Regression of “Nike Revenue” and “US Employment Rate”..................................................8
3. Regression of “Nike Revenue” and “China GDP”..................................................................9
4. Regression of “Nike Revenue” and “Consumer Price Index”...............................................10
5. Regression of “Nike Revenue” and “US GDP”.....................................................................11
References:....................................................................................................................................13

2REQUIREMENT ANALYSIS AND MODELLING
Forecast of Nike Quarterly Revenue with respect to E-commercial sales:
Dependent Variable: NIKE_REVENUE__IN_B_
Method: Least Squares
Date: 07/04/18 Time: 16:13
Sample: 2011Q2 2018Q2
Included observations: 29
Variable Coefficient Std. Error t-Statistic Prob.
C 12.57812 0.763803 16.46775 0.0000
E_COMMERCE_SALES__IN_B_ 0.205620 0.009304 22.10104 0.0000
R-squared 0.947619 Mean dependent var 28.81759
Adjusted R-squared 0.945679 S.D. dependent var 4.818429
S.E. of regression 1.123023 Akaike info criterion 3.136397
Sum squared resid 34.05187 Schwarz criterion 3.230693
Log likelihood -43.47776 Hannan-Quinn criter. 3.165930
F-statistic 488.4562 Durbin-Watson stat 0.108579
Prob(F-statistic) 0.000000
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2011 2012 2013 2014 2015 2016 2017 2018
Residual Actual Fitted

3REQUIREMENT ANALYSIS AND MODELLING
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2011 2012 2013 2014 2015 2016 2017 2018
NIKE_REV ENF
Actuals
± 2 S.E.
Forecast: NIKE_REVENF
Actual: NIKE_REVENUE__IN_B_
Forecast sample: 2011Q2 2018Q2
Included observations: 29
Root Mean Squared Error 1.083606
Mean Absolute Error 0.898438
Mean Abs. Percent Error 3.185658
Theil Inequality Coef. 0.018559
Bias Proportion 0.000000
Variance Proportion 0.013450
Covariance Proportion 0.986550
Theil U2 Coefficient 1.494416
Symmetric MAPE 3.164273
(Newbold & Bos, 1994)
Forecasting of Nike Quarterly Revenue:
Dependent Variable: NIKE_REVENUE__IN_B_
Method: Least Squares
Date: 07/05/18 Time: 09:50
Sample: 2011Q2 2018Q2
Included observations: 29
Variable Coefficient Std. Error t-Statistic Prob.
C -4506.605 86.43905 -52.13621 0.0000
QUARTERS 0.006166 0.000118 52.46963 0.0000
R-squared 0.990288 Mean dependent var 28.81759
Adjusted R-squared 0.989928 S.D. dependent var 4.818429
S.E. of regression 0.483568 Akaike info criterion 1.451221
Sum squared resid 6.313617 Schwarz criterion 1.545517
Log likelihood -19.04270 Hannan-Quinn criter. 1.480753
F-statistic 2753.062 Durbin-Watson stat 0.304929
Prob(F-statistic) 0.000000