MIS771 Descriptive Analytics and Visualization Assignment 2 - Future Business Decision of AusPaper

This assignment analyzes the future business decision of AusPaper, an Australian paper giant, using descriptive analytics and visualization techniques. The study identifies key factors affecting customer satisfaction and strategic alliance, and provides regression and logistic models for predicting customer behavior. The assignment covers tasks such as correlation analysis, regression analysis, and maximum likelihood estimation. The course code for this assignment is MIS771.

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MIS771 Descriptive Analytics and
Visualization
Assignment 2
Future Business Decision of AusPaper
Student Name: Student ID:
Due Date: Unit Name:

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Table of Contents
Assignment 2...............................................................................................................................................1
Future Business Decision of AusPaper.........................................................................................................1
Introduction.................................................................................................................................................3
Research direction...................................................................................................................................4
Task 1.......................................................................................................................................................4
Task 2.......................................................................................................................................................5
2.1 Correlation Analysis.......................................................................................................................5
2.2 Regression Analysis........................................................................................................................6
2.3 Outside ANZ Model........................................................................................................................8
Task 3.......................................................................................................................................................8
3.0 Logistic Regression.........................................................................................................................8
3.1 Maximum Likelihood...................................................................................................................10
3.2 Predicted probability visualization...............................................................................................11
Task 4.....................................................................................................................................................13
Forecasting of Sales...............................................................................................................................13
Conclusion.................................................................................................................................................14
References.................................................................................................................................................16
Appendices................................................................................................................................................17
Appendix 1.............................................................................................................................................17
Appendix 2.............................................................................................................................................22
Appendix 3.............................................................................................................................................30
Appendix 4.............................................................................................................................................31
Appendix 5.............................................................................................................................................33

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Introduction
The Australian paper giant Auspaper has a long history of success in production of paper
products. The company is an affiliated franchise of Pinnon Paper Industries. The historical
backdrop of the company is very encouraging, and they used to produce almost equal amount of
paper products compared to the total products all the similar companies. The major customers
belonged to two different market sectors. The company used to sell their products to the
magazine and newspaper sectors through direct sales or through dealer. The company also
exported paper products to various countries of Asia, Middle East, USA, and Europe and in
different provinces of India as well as Africa.
The future problem areas were identified by the research and development wing of Auspaper and
some of the concerns bothered the management. The expansion of social media, along with
increasing market of electronic gadgets was two of the most important factors for probable future
decline in sale. The company indented to perform a market research and they approached the
research team manager Hugo Barra, a PhD in Data Science and a Master in Digital Marketing at
ANALYTICS7. The job of analysing the project was given to the scholar after a long
conversation on the scope of the research.
Research direction
The key factor was identified as edification of strategic alliance in a business to business
environment with respect to the customer satisfaction with Auspaper’s functioning and products.
The likelihood of the customers was also evaluated with a logistic model for further

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understanding the trend of strategic alliance. Furthermore a forecasting for last three quarters of
sales was conducted for the year 2017.
Task 1
The first task was to assess the dependent factor of the collected data fields of 200 customers.
The descriptive values of strategic alliance were found for satisfaction level of the customers.
The job was performed using Microsoft Excel Tool pack facility. The average satisfaction score
was found as 6.95 in scale of 10. Indication of almost 70% likely future purchase of Auspaper
products was observed. The deviation of satisfaction score of 1.24 was also noticed. This factor
was a very disturbing figure as 12% deviation in the negative side could deter the future planning
of the company. The median was 7.05 and a Gaussian nature of the distribution of satisfaction
score was seen with a very minor skewness of 0.09. The mode of the data was at 5.4; this
indicated the satisfaction level for most of the customers. This trend was not at all encouraging
for the company. The range of the satisfaction was noted to be 5.2 with minimum satisfaction
level at 4.7 and maximum at 9.9. Total 200 samples were analyzed for this study and the
standard error was calculated as 0.08, which provided an approximate estimate for the population
mean. The 95% confidence interval for the satisfaction score was between 6.77 and 7.12, the
compactness of the confidence interval was a useful result to analyse the customer likelihood for
the paper products of Auspaper.
The entire data was split into two subcategories based on the strategic alliance choice of the
customers. The first part consisted of 86 customers who preferred to continue future alliance
with the company. The average satisfaction level for those customers was found to be 7.94,
which was expectedly greater than that of the total data set. The median was at 7.9 and the mode

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at 7.6. Almost perfect normal distribution for the satisfaction curve was seen with a negligible
skewness of -0.08. The consistency and homogeneity for the customers was evident from the
descriptive figures. The 95% confidence interval was calculated as [7.75, 8.13]. The second
section of customers with preference of discontinuing their strategic alliance with Auspaper was
114 in numbers. It was noticed that most of the customers were unhappy with services in the
collected sample data. The average satisfaction was 6.2 and the median was at 6.3, the most
important observation was for the median which was 5.4 for the data set. The distribution had a
skewness of 0.26 with kurtosis of -0.77.
Task 2
2.1 Correlation Analysis
The satisfaction of customers was the dependent variable and it was cross checked with the
independent factors. Multiple correlation technique was used for the purpose and association
levels among the factors were noticed.
The probable dependent variable was customer satisfaction score, which was continuous in
nature. The strategic alliance was a binary dependent variable. Initially correlation with these
dependent variables was noticed. In order to narrow down on the search of independent factors,
and finalize the list for most significant independent factors multiple correlation matrix was
constructed using the excel tool pack using this set of 15 variables.
For the purpose of visual influence, initially scatter diagrams were drawn. The diagrams
represented the trend of the dependent factor based on the independent factors. The delivery
speed was found to have a high positive (R = 0.63) correlation with customer satisfaction; price

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flexibility had a low level of correlation (R = 0.03) with satisfaction. The dependent variable had
a high positive correlation (R = 0.54) with billing, image (R = 0.48), product line (R = 0.65),
complaint readdress (R = 0.59) and product quality (R = 0.51). Some of the variables such as e-
commerce (R = 0.34) and technical support (R = 0.2) also had low level of positive correlation
with satisfaction of customers. The scatter diagrams clearly reflected the trend of these
associations. Pricing was the only factor which was negatively (R = -0.28) correlated with the
dependent variable. The purchase intention of the customers was hampered by the higher prices
of paper products of Auspaper compared to other rival companies. On the positive side the fast
delivery of the paper products, product line variability, complaint readdress system and correct
billing were the major factors for customer retention research work. The sales channel ways of
distribution, the different regions, the industry types did not come into the correlation analysis.
2.2 Regression Analysis
After the correlation analysis, the scholar went ahead with multiple regression technique using
excel tool pack. The first model consisted of fourteen variables including the strategic alliance of
the customers with the company. It was observed that advertising was not a significant factor for
the regression model with p value of 0.98 and coefficient of -0.001. The model was restructured
by removing advertisement factor and warranty on products was found to be insignificant with a
p value of 0.85 and likelihood coefficient of -0.015. The model was reconstructed again without
the factor warranty, and gradually, delivery speed of the company (p value of 0.66), billing
transparency (p value of 0.58), new product launching in market (p value of 0.21), technical
support for customers (p value of 0.22), pricing of paper products (p value of 0.09), and
complaint readdress system (p value of 0.06) were also eliminated from the model due to level of
insignificance levels. The final model consisted of six factors, which were product quality, e-

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commerce, and product line variability, image of the company, price flexibility and type of
future strategic alliance possibility with the customers. The multiple-linear regression model was
finalized as below (Ton & Raman, 2010).
Y =0 . 34 X1−0 . 33 X2+0 . 4 X3+0. 57 X 4+0 . 25 X5+ 0. 59 X6−1 .2
The dependent variable Y was the satisfaction level of the customers and the independent
variables were as follows.
X1 = Product quality
X2 = E-commerce
X3 = Product line
X4 = Image
X5 = Price flexibility
X6 = Strategic alliance level
The model described the fact that for one unit level of increase in product quality (keeping other
factors same) will increase the satisfaction level of the customers by 34%. The similar decision
was possible for other five factors (Woodside, 2013).
2.3 Outside ANZ Model
For outside ANZ region a regression model with previously identified variables was found, and
five variables were found to be significantly correlated to customer satisfaction. Product quality,

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complaints readdress system, image of the company, pricing of paper products, and strategic
alliance type were used to build up the new regression model. The final model was as below,
Y =0 . 32 X1+0 . 0 .39 X2 +0 .28 X3−0. 14 X4+ 0 .89 X5 +1. 55
Y = Customer satisfaction
X1 = Product quality
X2 = Complaint readdress system
X3 = Image
X4 = Price of products
X5 = Strategic alliance level
Task 3
3.0 Logistic Regression
The course coordinator of the scholar and an analytical expert at ANALYTICS7, Dr. Hugo Barra
analyzed and predicted that Product Line, Product Quality, Price Flexibility, Competitive
Pricing, and Personnel Image were the important and decisive factors for strategic alliance of the
customers with the firm. The scholar was guided to cross check the likelihood estimation for the
above predictive factors. The scholar used a logistic regression model as the factor strategic
alliance was binary in nature. Due to incapability of linear regression model to handle binary
factors the scholar went ahead with his proposed logistic model. The logistic regression model
with Dr. Hugo’s prescribed variables revealed that competitive pricing of the paper products was

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not a significant factor of the study (p value of 0.06). The model was restructured with remaining
four variables. The model was finalized with Product Line, Product Quality, Price Flexibility,
and Personnel Image. The equation of the model has been provided below as,
Y = 1 . 09 X1+0 . 94 X2+0 . 87 X3+1 . 14 X 4−24 .32
Where the dependent variable was Y = strategic alliance and the independent factors were as
follows.
X1 = Product quality
X2 = Product line
X3 = Image
X4 = Price flexibility
The model described the fact that for one unit level of increase in product quality (keeping other
factors same) will increase the satisfaction level of the customers by almost 100%. The similar
decision was possible for other three factors (Kleinbaum et al., 2013).
3.1 Maximum Likelihood
The maximum likelihood probabilities for the continuous variables were evaluated and are
reported in table 11 in appendix. The four significant variables Product Line, Product Quality,
Price Flexibility, and Personnel Image from the logistic model were used to find the likelihood
estimate probabilities. The likelihood function was constructed. Initially the constant term and
coefficients of the models were assumed to be 0.5. The values were then multiplied with the four
factors and the constant term was added to find the likelihood function. The exponential value

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was evaluated for each value of the likelihood function as an intermediate step. The likelihood
probabilities were evaluated as the ratio p=eL / ( 1+eL ) where L was the likelihood functional
values (Kleinbaum & Klein, 2010). The Logloss vales for each subject were valuated from the
probabilities. The sum of the Logloss value was found. Afterwards excel solver was used to
minimize the value of the Logloss value by changing the initial values of the factors of the
logistic regression model. The solver values were taken and a new model was prescribed for
predicting the likelihood estimates of the factors (Pascal et al., 2008). The final model has been
given below as,
Y = 4 . 46 X 1+2. 98 X 2+3 .69 X 3+4 .73 X 4−91 . 43
Where the dependent variable was Y = strategic alliance and the independent factors were as
follows.
X1 = Product quality
X2 = Product line
X3 = Image
X4 = Price flexibility
The model was able to described the increase the satisfaction level of the customers due to
increase in product quality (keeping other factors same). The slope of the model indicated that
customer satisfaction will touch level zero and hypothetical negative customer satisfaction level
will be attained if all the four factors were made zero.

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3.2 Predicted probability visualization
Here the scholar evaluated the probabilities for the association strategic alliance level as
prescribed by Dr. Hugo Barra. The personal image and variability of product lines were
considered fixed at level 5. The intention was to keep the neutral responses of the customers on
these two factors. Then the probabilities for the purpose were evaluated for three levels of price
flexibility. The three levels of price flexibility were zero (Non flexible price level), five
(moderate flexible price level) and ten (highly flexible price level). The ten levels of product
quality of the paper product were used against three levels of price flexibility. The association
alliance probabilities were plotted in figure 1 for three levels of price flexibility.
1 2 3 4 5 6 7 8 9 10
0
0.2
0.4
0.6
0.8
1
1.2
Product Quality vs Predicted
Probability
Price_Flex=0
Price_Flex=5
Price_Flex=10
Quality
Probability
Figure 1: Product quality versus Predicted probability
For the price flexibility of level zero it was observed that the predicted probabilities for strategic
alliance of the customers with the firm were almost zero. Therefore it was observed that

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irrespective of high level of finished paper products, the probability of association of the
customers did not exist. For the second tier of flexibility (Price_Flex = 5) the trend showed that
customers started to respond from level 6 of the product quality. Hence for lower product levels
mediocre price flexibility was a non responsive factor. The probability sharply increased for high
level of products even for price flexibility of level 5. The third tier of price flexibility (Price_Flex
= 10) was an eye catcher for customers. Irrespective of the quality of product customers were
inclined to buy the products of Auspaper. The trends were in line with earlier research works and
general customer behavior of the Australian market. People preferred to buy the products for a
lower price level but the increase rate in the trend flattened out for higher end products.
Hence the scholar concluded that people were inclined for low priced products, but there were
some customers who were also inclined towards high end paper products for moderate price
flexibility (Silva & Tenreyro, 2010).
Task 4
Forecasting of Sales
For this purpose the scholar developed a time series model and later adjusted the seasonal
variation. It is a well know tool to forecast future trend and therefore seasonal adjustments were
also performed. The quarterly turnover for 37 quarters for previous ten years (2008-2017) was
used to find average sales for time series and then seasonal indices were calculated for the entire
time frame (De Livera, Hyndman & Snyder, 2011). The seasonal adjusted values for sales
turnover was recalculated using seasonal indices. The trend was smoothed out that way and
future forecast for last three quarters were done. Mean absolute percentage error (MAPE) was
2.68 % (Brockwell & Davis, 2016).

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Q1
Q2
Q3
Q4
Q1
Q2
Q3
Q4
Q1
Q2
Q3
Q4
Q1
Q2
Q3
Q4
Q1
Q2
Q3
Q4
Q1
Q2
Q3
Q4
Q1
Q2
Q3
Q4
Q1
Q2
Q3
Q4
Q1
Q2
Q3
Q4
Q1
Q2
Q3
Q4
2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
0.0
1000.0
2000.0
3000.0
4000.0
5000.0
6000.0
7000.0
8000.0
9000.0
Forecast of Auspaper Sale
Turnover($,000) CMA(4) Deseasonalised
Linear (Deseasonalised) Forecast
Figure 2: Time Series data for forecasting turnover of Auspaper
The trend forecasted the turnover figures for last three quarters of 2017 as $ 4575.44, $ 5043.32,
$ 5099.46 (Box et al., 2015).
Conclusion
The key findings of the scholar for future business prospect of Auspaper were submitted to Dr.
Hugo Barra. Some of them were as follows,
1. Auspaper should increase their price flexibility for their paper products.
2. The company should maintain the higher range of quality products as a market segment
was identified, who preferred to buy good quality products for mediocre price flexibility
also.

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3. Outside ANZ sales depended on product quality and pricing as well, though image of the
company and customer satisfaction were important. Hence, Auspaper was advised to
think on those lines, by not compromising on quality and customer need satisfaction.
4. The descriptive for customer satisfaction revealed that more customers preferred non
alliance with the company. The main reason was later identified as low price flexibility
(Black, 2009).
5. Overall Auspaper should increase the quality of customer handling utilities with
flexibility in price of finished products.

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References
Box, G.E., Jenkins, G.M., Reinsel, G.C. and Ljung, G.M., 2015. Time series analysis:
forecasting and control. John Wiley & Sons.
Brockwell, P.J. and Davis, R.A., 2016. Introduction to time series and forecasting. springer.
De Livera, A.M., Hyndman, R.J. and Snyder, R.D., 2011. Forecasting time series with complex
seasonal patterns using exponential smoothing. Journal of the American Statistical
Association, 106(496), pp.1513-1527.
Black, K., 2009. Business statistics: Contemporary decision making. John Wiley & Sons.
Kleinbaum, D.G., Kupper, L.L., Nizam, A. and Rosenberg, E.S., 2013. Applied regression
analysis and other multivariable methods. Cengage Learning.
Ton, Z. and Raman, A., 2010. The effect of product variety and inventory levels on retail store
sales: A longitudinal study. Production and Operations Management, 19(5), pp.546-560.

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Silva, J.S. and Tenreyro, S., 2010. On the existence of the maximum likelihood estimates in
Poisson regression. Economics Letters, 107(2), pp.310-312.
Kleinbaum, D.G. and Klein, M., 2010. Maximum likelihood techniques: An overview.
In Logistic regression (pp. 103-127). Springer, New York, NY.
Pascal, F., Chitour, Y., Ovarlez, J.P., Forster, P. and Larzabal, P., 2008. Covariance structure
maximum-likelihood estimates in compound Gaussian noise: Existence and algorithm
analysis. IEEE Transactions on Signal Processing, 56(1), pp.34-48.
Woodside, A.G., 2013. Moving beyond multiple regression analysis to algorithms: Calling for
adoption of a paradigm shift from symmetric to asymmetric thinking in data analysis and crafting
theory.
Appendices
Appendix 1
Table 1: Descriptive for customer satisfaction
Sats
Mean 6.952
Standard Error 0.087760982
Median 7.05
Mode 5.4
Standard Deviation 1.241127709
Sample Variance 1.54039799
Kurtosis
-
0.769252402

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Skewness 0.089599855
Range 5.2
Minimum 4.7
Maximum 9.9
Sum 1390.4
Count 200
Largest(1) 9.9
Smallest(1) 4.7
Confidence Level(95.0%) 0.173060839
Table 2: Confidence Interval for Mean of Customer satisfaction
Confidence Interval for Mean
Data
Sample Standard
Deviation 1.241127709
Sample Mean 6.952
Sample Size 200
Confidence Level 95%
Intermediate Calculations
Standard Error of the
Mean 0.087760982
Degrees of Freedom 199
t Value 1.971956498
Margin of Error 0.173060839
Confidence Interval
Interval Lower Limit 7.125060839
Interval Upper Limit 6.778939161
Table 3: Strat_Alliance count
Strat_Alliance_recoded Count
No 86
Yes 114

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No Yes
0
20
40
60
80
100
120
Count of Strat Alliance
Figure 3: Count of Strat Alliance
Table 4: Descriptive for customer satisfaction for no strategic alliance
Sats
Mean 6.207017544
Standard Error 0.085701207
Median 6.3
Mode 5.4
Standard Deviation 0.915038497
Sample Variance 0.837295451
Kurtosis
-
0.766545663
Skewness 0.255806062
Range 3.7
Minimum 4.7
Maximum 8.4
Sum 707.6
Count 114
Largest(1) 8.4
Smallest(1) 4.7
Confidence Level(95.0%) 0.169789543

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Table 5: Confidence Interval for Mean of Customer satisfaction for no strategic alliance
Confidence Interval for Mean
Data
Sample Standard
Deviation 0.915038497
Sample Mean 6.207017544
Sample Size 114
Confidence Level 95%
Intermediate Calculations
Standard Error of the
Mean 0.085701207
Degrees of Freedom 113
t Value 1.981180296
Margin of Error 0.169789543
Confidence Interval
Interval Lower Limit 6.376807087
Interval Upper Limit 6.037228001
1 5 7 8 9 More
0
10
20
30
40
50
60
70
80
Sats for no Alliance
Frequency
Figure 4: Satisfaction curve for no alliance
Table 6: Descriptive for customer satisfaction for yes strategic alliance
Sats

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Mean 7.939534884
Standard Error 0.094141365
Median 7.9
Mode 7.6
Standard Deviation 0.873031105
Sample Variance 0.762183311
Kurtosis
-
0.079157673
Skewness
-
0.075576515
Range 4.2
Minimum 5.7
Maximum 9.9
Sum 682.8
Count 86
Largest(1) 9.9
Smallest(1) 5.7
Confidence Level(95.0%) 0.187178251
Table 7: Confidence Interval for Mean of Customer satisfaction for yes strategic alliance
Confidence Interval for Mean
Data
Sample Standard
Deviation 0.873031105
Sample Mean 7.939534884
Sample Size 86
Confidence Level 95%
Intermediate Calculations
Standard Error of the
Mean 0.094141365
Degrees of Freedom 85
t Value 1.988267868
Margin of Error 0.187178251
Confidence Interval
Interval Lower Limit 8.126713135
Interval Upper Limit 7.752356632

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1 5 7 8 9 More
0
5
10
15
20
25
30
35
40
Sats for Yes alliance
Frequency
Figure 5: Satisfaction curve for yes alliance
Appendix 2
Table 8: Correlation coefficient for all factors
Items
Prdct_Qu
al
E_Comm
Tch_Sup
p
Cmplnt_
Res
Advert
Prdct_Lin
e
Image
Pricing
Warrenty
New_Prd
ct
Billing
Price_Fle
x
Delvry_S
peed
Sats
Strat_Alli
ance_rec
Prdct_Qu
al
1.000
E_Comm
-0.034
1.000
Tch_Sup
p
0.087
0.041
1.000

22
Cmplnt_
Res
0.090
0.192
0.152
1.000
Advert
-0.054
0.505
0.028
0.234
1.000
Prdct_Lin
e
0.509
0.077
0.169
0.568
0.117
1.000
Image
-0.116
0.788
0.086
0.256
0.627
0.050
1.000
Pricing
-0.448
0.177
-0.092
-0.077
0.099
-0.483
0.200
1.000
Warrenty
0.109
0.103
0.838
0.181
0.035
0.237
0.163
-0.085
1.000
New_Prd
ct
0.136
-0.041
-0.038
0.090
0.063
0.126
0.009
-0.121
0.030
1.000
Billing
0.083
0.217
0.121
0.741
0.230
0.444
0.284
-0.060
0.204
0.137
1.000
Price_Fle
x
-0.487
0.186
-0.029
0.418
0.260
-0.349
0.272
0.470
-0.041
0.047
0.419
1.000
Delvry_S
peed
0.067
0.241
0.132
0.878
0.323
0.612
0.299
-0.055
0.183
0.147
0.773
0.513
1.000
Sats
0.521
0.342
0.203
0.598
0.354
0.646
0.478
-0.282
0.269
0.192
0.540
0.032
0.630
1.000

23
Strat_Alli
ance_rec
0.377
0.307
0.117
0.427
0.201
0.411
0.329
-0.177
0.181
0.126
0.397
0.114
0.471
0.693
1
1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6
0
2
4
6
8
10
12
Sats vs Delvry_Speed
Sats
Delvry_Speed
Sats
Figure 6: Satisfaction versus delivery speed
2 3 4 5 6 7 8
0
2
4
6
8
10
12
Sats vs Price_Flex
Sats
Price_Flex
Sats
Figure 7: Satisfaction versus price flexibility

24
0 1 2 3 4 5 6 7 8 9 10
0
2
4
6
8
10
12
Sats vs New_Prdct
Sats
New Prdct
Sats
Figure 8: Satisfaction versus new product
3 4 5 6 7 8 9
0
2
4
6
8
10
12
Sats vs Warrenty
Sats
Warrenty
Sats
Figure 9: Satisfaction versus warranty

25
0 1 2 3 4 5 6 7 8
0
2
4
6
8
10
12
Sats vs Billing
Sats
Billing
Sats
Figure 10: Satisfaction versus billing
3 4 5 6 7 8 9 10 11
0
2
4
6
8
10
12
Sats vs Pricing
Sats
Pricing
Sats
Figure 11: Satisfaction versus pricing

26
1 2 3 4 5 6 7 8
0
2
4
6
8
10
12
Sats vs Advert
Sats
Advert
Sats
Figure 12: Satisfaction versus advertisement
1 2 3 4 5 6 7 8 9
0
2
4
6
8
10
12
Sats vs Prdct_Line
Sats
Prdct_Line
Sats
Figure 13: Satisfaction versus product line

27
2 3 4 5 6 7 8 9
0
2
4
6
8
10
12
Sats vs Image
Sats
Image
Sats
Figure 14: Satisfaction versus image
1 2 3 4 5 6 7 8 9 10
0
2
4
6
8
10
12
Sats vs Cmplnt_Res
Sats
Cmplnt_Res
Sats
Figure 15: Satisfaction versus complaint readdress

28
0 1 2 3 4 5 6 7 8 9 10
0
2
4
6
8
10
12
Sats vs Tch_Supp
Sats
Tech_Supp
Sats
Figure 16: Satisfaction versus technical support
2 2.5 3 3.5 4 4.5 5 5.5 6
0
2
4
6
8
10
12
Sats vs E_Comm
Sats
E_Comm
Sats
Figure 17: Satisfaction versus e-commerce

29
4 5 6 7 8 9 10 11
0
2
4
6
8
10
12
Sats vs Prdct_Qual
Sats
Prdct_Qual
Sats
Figure 18: Satisfaction versus product quality
Appendix 3
Table 9: Multiple linear regression model
Regression Statistics
Multiple R 0.909
R Square 0.826
Adjusted R
Square 0.820
Standard Error 0.526
Observations 200.000
ANOVA
df SS MS F
Significanc
e F
Regression 6.000 253.074
42.1
79
152.2
59 0.000
Residual 193.000 53.465
0.27
7
Total 199.000 306.539
Coefficie
nts
Standard
Error t Stat
P-
value Lower 95%
Upper
95%
Lower
95.0%
Upper
95.0%
Intercept -1.201 0.474 -2.53 0.012 -2.136 -0.266 -2.136 -0.266
Prdct_Qual 0.344 0.037 9.24 0.000 0.270 0.417 0.270 0.417
E_Comm -0.331 0.079 -4.17 0.000 -0.487 -0.174 -0.487 -0.174
Prdct_Line 0.401 0.036 11.2 0.000 0.331 0.471 0.331 0.471
Image 0.571 0.056 10.1 0.000 0.461 0.682 0.461 0.682
Price_Flex 0.246 0.040 6.11 0.000 0.167 0.326 0.167 0.326
Strat_Alliance_rec
oded 0.596 0.098 6.06 0.000 0.402 0.790 0.402 0.790

30
0.250 10.250 20.250 30.250 40.250 50.250 60.250 70.250 80.250 90.250
0.000
2.000
4.000
6.000
8.000
10.000
12.000
Normal Probability Plot
Series1
Sample Percentile
Sats
Figure 19: Normal probability plot of regression model
Appendix 4
Table 10: Logistic regression model
Deviance Table
Source DF Adj Dev
Adj
Mean
Chi-
Square
P-
Valu
e
Regression 4 107.95 26.98
76
107.95 0
Prdct_Qual 1 37.05 37.04
61
37.05 0
Prdct_Line 1 25.24 25.23
82
25.24 0
Image 1 21 21.00
34
21 0
Price_Flex 1 30.48 30.48
49
30.48 0
Error 195 165.38 0.848
1
Total 199 273.33
Coefficients
Term Coef SE Coef VIF
Constant -24.32 3.61
Prdct_Qual 1.089 0.206 2.04
Prdct_Line 0.937 0.208 1.48
Image 0.87 0.212 1.32
Price_Flex 1.144 0.241 2.24
Odds Ratios for
Continuous Predictors
Odds
Ratio 95% CI
Prdct_Qual 2.9698 (1.9831, 4.4476)

31
Prdct_Line 2.5518 (1.6990, 3.8327)
Image 2.3859 (1.5762, 3.6116)
Price_Flex 3.1391 (1.9582, 5.0322)
Regression Equation
P(1) = exp(Y')/(1 + exp(Y'))
Y' = -24.32 + 1.089 Prdct_Qual + 0.937 Prdct_Line
+ 0.870 Image + 1.144 Price_Flex
Goodness-of-Fit Tests
Test DF Chi-Square
P-
Value
Deviance 195 165.38 0.939
Pearson 195 202.96 0.333
Hosmer-Lemeshow 8 10.17 0.254
Table 11: Maximum likelihood estimates
Coefficient
s
Solver decision
variables
b0 -91.42665205
b1 4.456709169
b2 2.976163282
b3 3.687865674
b4 4.73298084
Logloss -0.324134

32
Appendix 5
Table 12: Time series data with seasonal variation
Year Quarter Deseasonalised Trend Forecast
2008 Q1 3819.09 3698.52 5228.36
Q2 3659.61 3751.23 3038.41
Q3 3716.74 3803.94 3207.02
Q4 3748.15 3856.66 3366.41
2009 Q1 3789.35 3909.37 5773.55
Q2 3827.89 3962.08 3123.80
Q3 3866.65 4014.79 3539.92
Q4 3944.91 4067.50 3406.88
2010 Q1 4062.14 4120.21 5957.88
Q2 4228.41 4172.92 3322.32
Q3 4418.08 4225.63 3824.43
Q4 4544.11 4278.35 3808.52
2011 Q1 4596.18 4331.06 6266.70
Q2 4629.68 4383.77 3407.27
Q3 4606.40 4436.48 4028.89
Q4 4562.00 4489.19 4062.61
2012 Q1 4531.16 4541.90 6395.83
Q2 4523.94 4594.61 3567.68
Q3 4581.39 4647.33 4079.99
Q4 4687.39 4700.04 4243.22
2013 Q1 4766.78 4752.75 6717.18
Q2 4781.21 4805.46 4024.81
Q3 4797.49 4858.17 4218.19
Q4 4847.71 4910.88 4258.68
2014 Q1 4947.43 4963.59 6917.64
Q2 5098.16 5016.31 4179.99
Q3 5237.96 5069.02 4567.28
Q4 5338.40 5121.73 4658.72
2015 Q1 5399.78 5174.44 7054.40
Q2 5411.85 5227.15 4428.71
Q3 5373.88 5279.86 4788.24
Q4 5305.54 5332.57 4822.85
2016 Q1 5228.66 5385.29 7328.33
Q2 5203.81 5438.00 4477.73
Q3 5246.69 5490.71 4770.83
Q4 5545.33 5543.42 4912.61
2017 Q1 5193.91 5596.13 7910.89
Q2 5648.84 4575.44
Q3 5701.55 5043.32
Q4 5754.27 5099.46