BLB20005-M MTI - Managing Through Information

MP1461TM 14A1 1521 MGMFMH

Statistics Assignment

D.G.L.R. DE SILVACB006262MBAAPIIT

Contents

1.0 Introduction of organization chosen and the issue/area.......................................................5

2.0 Identification of objectives and formulation of hypotheses.................................................7

2.1 Investigation 1 – Forecasting Demand.............................................................................7

2.2 Investigation 2 – Testing whether Varying Shelf Visibility has an Impact on Sales at Supermarket...........................................................................................................................9

3.0 Selection and application of appropriate statistical techniques..........................................11

3.1 Interpretation of Findings - Investigation 1 (Demand Forecasting)...............................11

3.2 Interpretation of Findings - Investigation 2 (Testing whether Varying Shelf Visibility has an Impact on Sales at Supermarket)..............................................................................15

4.0 Conclusion and Recommendations....................................................................................18

4.1 Recommendations - Investigation 1 (Demand Forecasting)..........................................18

4.2 Recommendations - Investigation 2 (Testing whether Varying Shelf Visibility has an Impact on Sales at Supermarket)..........................................................................................20

5.0 References..........................................................................................................................22

6.0 Appendix............................................................................................................................23

6.1 Data used for Investigation 1 (Demand Forecasting)....................................................23

6.2 Descriptive Statistics of Investigation 1.........................................................................24

6.3 Data used for Investigation 2 (Hypothesis Testing).......................................................24

6.4 Descriptive Statistics of Investigation 2 (Home Shelf)..................................................25

6.5 Descriptive Statistics of Investigation 2 (Brand Specific Gondola Shelf).....................25

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TABLE OF FIGURES

Figure 1 - Scatter Diagram between Quantity Sold and Time Frame……………………..11

Figure 2 - Sales over Time…………………………………………………………………12

Figure 3 - Sales Trend over Time…………………………………………………………..12

Figure 4 - Summary Output from MS Excel (Linear Regression)……………………....13

Figure 5 - Linear Regression Equation Derived…………………………………………13

Figure 6 - Sales Trend over Time (Trend & Equation)………………………………….14

Figure 7 - Sales Trend……………………………………………………………………14

Figure 8 - Sales Comparison……………………………………………………………15

Figure 9 - Summary Output from MS Excel (Hypothesis Testing) …………………….15

Figure 10 - Normal Distribution (Rejection and Acceptance Regions)…………………16

Figure 11 - Extrapolation using the Linear Regression Equation……………………......17

Figure 12 - Data used for Investigation 1 (Demand Forecasting)……………………….22

Figure 13 - Descriptive Statistics of Investigation 1……………………………………..23

Figure 14 - Data used for Investigation 2 (Hypothesis Testing)…………………………23

Figure 15 - Descriptive Statistics of Investigation 2 (Home Shelf)……………………...24

Figure 16 - Descriptive Statistics of Investigation 2 (Brand Specific Gondola Shelf)…..24

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1.0 Introduction of organization chosen and the issue/area

Being a fully state owned Regional Plantation Company, Chilaw Plantation Limited (CPL)

has a prime duty to strengthen the Country’s Economy adhering to the economic policy of the

State apart from making profit for its own benefit.

In view of the vision and the guidance of the Ministry of Coconut Development and Janatha

Estate Development its National Coconut Sector Development Plan, 2011-2016 has set

targets approaching multiple angles of the economy to increase production of coconut in

sustainable manner up to 3650 million nuts at the end of 2016 that enable to cope with the

demand of the domestic culinary use and an input of industrial products.

With a Commissioning of the Cashew Processing Centre at Thmabapanni Area Estate and a

strong distribution network the company has over the years evolved its business operations

and visibility of products throughout Sri Lanka as well as the export market. A range of

products especially under the Thambapanni processed cashew brand are exported around the

world and have attracted lucrative opportunities for the company. With strong market growth

rates, numerous milestones, achievements and a widespread outreach of product distribution

the company aims at striving further in the competitive and volatile business environment

(Plantation Industry) of Sri Lanka as well as continue to explore lucrative opportunities for its

products in the export segment. Considering the nature of the business, two areas of

investigation have been identified.

The first area of concern would be covering the aspects of generating demand. After strong

growth success in the domestic market, this product was exported as trial by the company

approximately 5 years ago. Over time, this product as gained high levels of consumption and

demand in the export market proving to be highly lucrative.

As a result of growing export demand the company is faced with managing challenges

affecting its supply chain processes. For instance in keeping pace with the growing domestic

demand, managing production capacity constraints, for this product as well as adhering to

shipping vessel schedules are complexities faced by the company.

The company needs to plan production capacity to balance supply of this product to serve

both domestic and export demand while preventing any form of out of stock situation on both

segments. Therefore, production capacity needs to be optimized as well as allocated

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efficiently. This has been an area of concern since the processing centre is unable to cope up

with sudden demand from export customers.

Furthermore, since export market requirements are much more complex for e.g. customized

labeling, numerous languages, and country regulatory requirements on packaging it becomes

difficult to cope up with unanticipated demand. In addition, some of the packaging materials

of “Thambapanni Cashew” are imported. Hence, arranging for raw materials at short notice

from suppliers to manage unanticipated demand has become a cumbersome process.

The company is also challenged with making prior bookings with shipping lines on

containers and cargo vessels to “New Zealand”. Thereby making it significantly important for

the company to plan production to meet vessel schedules, manage transit time periods to

destination as well as stock levels at the export market.

Thus, having prior information / forecasting the demand of “Thambapanni Cashew” to “New

Zealand” has become an area of growing concern to support the company’s supply chain

functions of planning, production, procurement and logistics. –Investigation 1 of the Study

The second area that would be investigated focuses on the shelf visibility of the company’s

products at supermarkets. Operating in a highly volatile and competitive environment, shelf

visibility of products has become significantly important in influencing consumer buying

behavior. The modern trade sector (distribution via supermarkets) has become one of the high

contributors to the company’s revenue over the past few years.

The issue on this area is to determine if sales of a particular product are higher while being

placed on the home-shelf of supermarkets or on a brand specific gondola shelf. The home-

shelf could be described as the area (shelves) of the supermarket which have products of all

brands kept beside each other. For e.g. all cashew brands such as Royal Cashew, MA’S,

Ranscrip, etc. kept on one shelf.

The brand specific gondola shelf is generally a standalone shelf in the supermarket which has

products only of the single brand for example only Thambapanni Cashew packets on a single

stand. Since a lot of money is allocated on marketing and advertising it has become an area of

concern to measure its effectiveness in generating sales.

Thus, determining whether there is any sales impact on placing a particular product

(Thambapanni Cashew) on the home shelf or a brand specific gondola shelf at a supermarket

is another area that would be highlighted upon. -Investigation 2 of the Study.

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2.0 Identification of objectives and formulation of hypotheses

Having looked upon the company’s functions and analysed two areas of concern, the

objectives of this study would be categorized as per the respective areas of investigation.

2.1 Investigation 1 – Forecasting Demand

The objective of this investigation is to generate a demand forecasting mechanism to

anticipate the sales of “Thambapanni Cashew” to “New Zealand”. The purpose of this is to

assist the company in its supply chain functions of production, planning, procurement and

logistical arrangements. A strong forecasting technique would ensure the company has

sufficient knowledge well in advance to organize its other functions to meet shifts in demand.

Some of the immediate benefits that could be derived are:

Provide the company with an outlook on sales figures of the future on the assumption

that it would not be affected by external environmental factors.

Assist the factory team to optimize and allocate production lines to produce the

quantity anticipated (thereby supporting their plans on allocating production for the

domestic market as well).

Support the procurement team in purchasing raw and packing materials for the

anticipated demand quantity.

Reducing storage costs of having overproduction as well as the risk of losing

opportunities while having under produced (which may result in an out of stock

situation).

Establish prior bookings with shipping lines to manage logistical arrangement and

meet vessel schedules.

In simple terms demand forecasting is used to determine the number of products that will be

purchased by consumers in the future. Most demand forecasting methods fall under four basic

categories of analysis namely quantitative, qualitative, time series methods, and casual

methods. For the purpose of this investigation a linear regression technique would be used.

Linear regression attempts to represent the relationship between two variables by fitting a

linear equation in the form Y = a + bX, where X is the explanatory variable and Y is the

dependent variable. The slope of the line is b, and a is the intercept.

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The slope basically describes rate of change in Y as X changes. Because Y is dependent on X,

the slope describes the predicted values of Y given any X value (Levine et al., 2006). The y-

intercept is the place where the regression line crosses the y-axis (where X = 0). In the

scenario being investigated the X value would be the time frame i.e. Month and Y value

would be quantity of sales in cases.

For the purpose of using this technique sales data of “Thambapanni Cashew” to “ New

Zealand” over the past 28 months (starting January 2013) has been obtained. The data

represents the number of cases of “Thambapanni Cashew” exported on a monthly basis. The

case configuration of this product is 36 packets per carton and has remained static over the

time frame. The objective would be to forecast the sales of the next quarter (Q2 – 2015) using

the regression equation derived and assess its credibility.

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2.2 Investigation 2 – Testing whether Varying Shelf Visibility has an Impact on Sales at Supermarket

The objective of this investigation is to test whether there is any impact on sales by

displaying the product “Thambapanni Cashew” on the home-shelf of a supermarket or on a

brand specific gondola shelf. As highlighted in the previous section of this report as well, the

company incurs high financial expenditure to gain shelf space at supermarkets to improve

product visibility. In order to support the allocation of Product Fixed Marketing Expenses

(PFME) such information would be vital. Some of the immediate benefits that could be

derived are:

Ensure PFME allocated to supermarket visibility is utilized in an efficient manner in

order to improve sales

Influence future marketing decisions on advertising and promotions while gaining a

competitive advantage over competitor product visibility

Negotiate on shelf space with future prospects prior to entering into contracts and

agreements.

Better visibility leads to better knowledge of the product’s presence. This would

significantly impact consumer buying patterns and brand loyalty.

Considering the nature of this investigation, a two sample test of hypotheses would be

considered most appropriate. Two-sample hypothesis testing is statistical analysis designed to

test if there is a difference between two means from two different populations. In this

situation, two supermarket locations which are relatively close to each other in the same

district were selected.

Both supermarkets belonged to the same corporate chain and yield a relatively similar rate of

revenue for the company. “Thambapanni Cashew” was displayed on the home shelf of one

supermarket while on a brand specific gondola shelf at the other location. Data of weekly

mean sales for 12 weeks at each location was collected for the interpretation. The objective of

this investigation is to test whether varying product visibility has or does not have any impact

on sales. Thus, the hypotheses could be stated as:

H0: Varying Product Visibility between Home Shelf and Brand Specific Gondola Shelf Does

Not Have an Impact on Sales (µH = µG)

H1: Varying Product Visibility between Home Shelf and Brand Specific Gondola Shelf Has

an Impact on Sales (µH ≠ µG)

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*Where µH is the mean sales of product visibility on the Home Shelf and µG is the mean

sales of product visibility on the Brand Specific Gondola Shelf.

Since the sample size is relatively small a t test statistic mechanism would be applied and

tested at a 0.05 level of significance. Furthermore, since we are testing if visibility has or

does not have an impact to sales a two-tailed test would be used. This is because the results

could be either positive or negative. By using a one tail test we would be able to only test for

the possibility of the relationship in one direction and completely disregarding the possibility

of a relationship in the other direction. Hence a two-tailed test would be applied.

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3.0 Selection and application of appropriate statistical techniques

Having identified the objectives of both investigations in the previous section as well as

highlighted upon the statistical technique to be applied the results of findings interpreted

using MS Excel will be analysed in this section.

3.1 Interpretation of Findings - Investigation 1 (Demand Forecasting)

Having obtained export sales data for 28 months of “Thambapanni Cashew” to “New

Zealand”, a scatter diagram on the data was developed to visualize the presence of a

correlation between the two variables (refer Appendix 6.1 for data values).

Oct-12 Jan-13 May-13 Aug-13 Nov-13 Mar-14 Jun-14 Sep-14 Dec-14 Apr-15 Jul-150

1000

2000

3000

4000

5000

6000

7000

Scatter Diagram Between Quantity Sold and Time Frame

Time Frame Monthly

Qua

ntity

in C

ases

Figure 1 - Scatter Diagram between Quantity Sold and Time Frame

Based on the scatter diagram we can clearly see that a positive linear correlation between the

sales of “Thambapanni Cashew” over the period of time. Thus, over the time span the sales of

this product have been on an increasing trend indicating levels of growth.

In order to gain some insight into the data being analysed a descriptive statistics tool was

used for a preliminary understanding (findings in Appendix 6.2). The results simply describe

that the average sales of “Thambapanni Cashew” is 3887 cases. The lowest quantity sold has

been 2,612 cases and the highest been 6,101 cases over the time span. The standard error

which measures the standard deviation of the data being analysed was 162 cases. To further

visualize the sales on a monthly basis a bar chart of sales over time was developed as

presented on the next page.

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Figure 2- Sales over Time

In order to portray the movement of sales over the time span a line chart was also developed

as shown below.

Figure 3 - Sales Trend over Time

From this diagram it is clearly visible that although there are fluctuations in sales a positive

linear trend exists for “Thambapanni Cashew” over the time period. Furthermore, we could

notice the absence of any outliers and influential observations in this data set which may

hinder the accuracy of findings. All data points are placed close to the trend line. Thereafter,

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a regression analysis was conducted using MS Excel at a 95% confidence level. Findings

derived are on the next page.

SUMMARY OUTPUT

Regression StatisticsMultiple R 0.65820533R Square 0.43323425Adjusted R Square 0.41143557Standard Error 605.734709Observations 28

ANOVAdf SS MS F Significance F

Regression 1 7292181.281 7292181.28 19.87433 0.000140511Residual 26 9539777.969 366914.537Total 27 16831959.25

Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%Intercept 3150.68254 235.2198601 13.3946281 3.53E-13 2667.181193 3634.1839 2667.18119 3634.18389X Variable 1 63.1770662 14.17141401 4.45806369 0.000141 34.04730755 92.306825 34.0473075 92.3068249

Figure 4 – Summary Output from MS Excel (Linear Regression)

Identifying the Slope and Intercept by the least-square method

The slope interception is the rate of change or rather the mean amount of change in Y when X

increases by one. According to the findings the slope of the coefficient X is 63.17. The

intercept of a regression line is the value of Y when X=0. This is calculated at 3,150 cases.

Therefore, based on the finding the regression equation would be written as Y = 63.17X +

3150

CoefficientsIntercept 3150.68254X Variable 1 63.1770662

Figure 5 - Linear Regression Equation Derived

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Slope

Slope 63.17y-intercept 3150.68

Correlation Coefficient

0.658205

R Square 0.433234252

Equation Derived Y = 63.17x + 3150

Figure 6 - Sales Trend over Time (Trend & Equation)

3.2 Interpretation of Findings - Investigation 2 (Testing whether Varying Shelf Visibility has an Impact on Sales at Supermarket)

In this situation, two supermarket locations which are relatively close to each other in the

same district were selected. Both supermarkets belonged to the same corporate chain and

yield a relatively similar rate of revenue for the company. “Thambapanni Cashew” was

displayed on the home shelf of one supermarket while on a brand specific gondola shelf at the

other location. Data of weekly mean sales for 12 weeks at each location was collected for the

interpretation (Appendix 6.3). In order to gain a preliminary understanding a sales trend chart

was developed.

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Figure 7 - Sales Trend

From the above we could notice several fluctuations in sales as well as identify slightly

higher sales of the product while being on display on the gondola shelf. However a clear

judgment cannot be derived. To further understand the nature of the data a descriptive

statistics analysis was conducted (Appendix 6.4 & 6.5). The mean sale of displaying on the

home shelf was calculated at 56 units while the minimum being 19 units and maximum 87

units. The mean sale of displaying on the gondola shelf was 68 units while the minimum

being 47 units and maximum being 91 units. A bar chart was also developed to gain a clearer

understanding.

Figure 8 - Sales Comparison

As mentioned in the previous section a two tail test using a t test statistic at 95% confidence

level was computed in MS excel (assuming equal variances).The results derived are shown

below.

t-Test: Two-Sample Assuming Equal Variances Variable 1 Variable 2

Mean 55.8333333 68.25Variance 325.060606 149.6590909Observations 12 12Pooled Variance 237.359848

Hypothesized Mean Difference 0df 22t Stat -1.9741358P(T<=t) one-tail 0.03052643t Critical one-tail 1.71714437P(T<=t) two-tail 0.06105286

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t Critical two-tail 2.07387307

Figure 9 - Summary Output from MS Excel (Hypothesis Testing)

The test statistic and critical values for the test have been calculated. The critical values for

this test are based on the degree of freedom which is 22. The critical t test values at this

degree of freedom and at 0.05 level of significance are -2.07 and 2.07. The test statistic is -

1.974 which falls into the rejection region. Thus, we reject the null hypothesis and accept the

alternative hypothesis. In other words, the results indicate that varying shelf visibility

between home shelf and brand specific shelf has an impact on sales.

H0: Varying Product Visibility between Home Shelf and Brand Specific Gondola Shelf Does

Not Have an Impact on Sales (µH = µG)

H1: Varying Product Visibility between Home Shelf and Brand Specific Gondola Shelf Has

an Impact on Sales (µH ≠ µG)

Figure 10 - Normal Distribution (Rejection and Acceptance Regions)

Source: http://prpklm.files.wordpress.com/2011/11/normal-distribution.gif

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4.0 Conclusion and Recommendations

Having conducted a statistical study on both areas being investigated, this section would

interpret and recommend to the company the outcomes derived and facilitate management

decision making.

4.1 Recommendations - Investigation 1 (Demand Forecasting)

Predicting the Future

From the linear regression formula derive Y = 63.17X + 3150 the company would be able to

extrapolate/predict the sales of the future. Using the mentioned formula a sales prediction of

the next quarter has been derived as mentioned below:

Jan-15 25 4780Feb-15 26 6101Mar-15 27 4260Apr-15 28 5754

May-15 29 4983Jun-15 30 5025Jul-15 31 4851

Aug-15 32 4678

Figure 11 - Extrapolation using the Linear Regression Equation

This information would be vital for the company to gain an outlook on sales and growth as

well as support company decisions and strategies. For instance the increasing trend in sales

indicates the company should focus more on developing this product as it shows potential

scope for growth.

Supporting Decisions

Having noticed the trend and predicted demand for the future, Organizational functions such

as supply chain and factory production would be at an advantage of having such futuristic

information. For instance factory team would be able to optimize and allocate production

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Y = 63.17x + 3150

lines to produce the quantity anticipated (thereby supporting their plans on allocating

production for the domestic market as well).

The procurement team would be able to place prior orders on purchasing raw and packing

materials for the anticipated demand quantity. Reducing storage costs of having

overproduction as well as the risk of losing opportunities while having under produced

(which may result in an out of stock situation) are significantly reduced. The logistics team

would be able to anticipate the number of containers to be shipped and establish prior

bookings with shipping lines to manage logistical arrangements and meet vessel schedules.

Correcting Errors and Gaining New Insights

By having empirical data from the results of the linear regression model the risk of making

decisions upon personal intuition and perception is significantly reduced. This would create a

more rational decision making foundation. Also, new insights such as the growth rate

anticipated and months of low sales and variations can be easily identified and planned for in

advance.

Having mentioned the above recommendations it is also important to state that the company

should also consider some of the limitations of the model applied in the study. For instance

the linear regression model only examines the relationship between two variables. Operating

in a volatile business environment it is recommended that the company also consider

statistical techniques such as multiple linear regressions, moving averages and time series

analysis for a stronger evaluation in the future.

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4.2 Recommendations - Investigation 2 (Testing whether Varying Shelf Visibility has an Impact on Sales at Supermarket)

According to the results derived from the analysis, it was identified that varying shelf

visibility to a brand specific gondola shelf compared to the home shelf of a supermarket does

have an impact on sales (alternative hypothesis of the investigation).

Some of the recommendations purposed to the company from these findings are:

The company should provide greater emphasis on the manner in which its products

are displayed at supermarkets as varying shelf visibility has a direct impact on sales.

The marketing team should look at more attractive shelving strategies to boost sales

as well as collaborate with supermarkets on gaining popular shelf space and visibility.

For instance negotiating on shelf space with future prospects prior to entering into

contracts and agreements

Provide a more rational manner of allocating PFME (marketing budget) for product

visibility at supermarkets. The marketing team should consider adopting brand

specific gondola shelves wherever appropriate to gain customer attraction and sales

rather than racking the product with competitor brands on the home shelf.

In terms of a marketing view shelf visibility has a direct relationship on building

brand loyalty and trust. Gondola shelving would display the company’s products at a

more eye catching level. It also increases the forward stock share of the product

compared to competitor brands. The stock depth displayed on the shelf has a strong

impact on consumer loyalty. It could also be used as a secondary display for potential

customer attraction and retention. This form of shelving may also have cost

advantages for the company.

The results also act as a new marketing venture for the company when planning future

promotion campaigns at supermarkets.

The above are some of the recommendations the company should take into account for the

hypothesis testing results derived. However it should be mentioned that prior to taking long

term decisions the company should identify if other factors such as media advertising have

influenced consumer behaviour during the time of testing the sample. Furthermore,

supermarket locations in other regions of the country should also be analysed and a larger

sample size should be evaluated to derive a more convincing decision.

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The recommendations mentioned would provide an empirical approach to support

management decision making on the investigations selected. As profit maximization, growth

and building competitive advantage are key objectives of the company such statistical tools

provide new insights and knowledge to decision makers. As also mentioned above, the

company should look at the limitations of the models adopted and apply other statistical

techniques as well to support the findings and provide a more rational and conclusive

judgment for long term strategic decision making.

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5.0 References

Viswanathan P.K, Krehbiel T.C, Berenson M.L, Levine D.M, 2006, Business Statistics – A First Course, Fourth Edition, Pearson Education in South Asia, India

Chilaw Plantations Limited for the year 2013http://www.parliament.lk/uploads/documents/paperspresented/annual_report_chilaw_plantation_limited_2013.pdf

Chilaw Plantations Limited for the year 2012

StatSoft Inc. (2014) Demand Forecasting [Online] Available From:https://www.statsoft.com/Textbook/Demand-Forecasting[Accessed 16 April 2014]

S. David (2006) 1- vs 2-Tailed Tests [Online] Available Fromhttp://www.chem.utoronto.ca/coursenotes/analsci/StatsTutorial/12tailed.html [Accessed 19 April 2014]

Boston University School of Public Health (2013) Hypothesis Testing for Means & Proportions [Online] Available From: http://sphweb.bumc.bu.edu/otlt/MPH-Modules/BS/BS704_HypothesisTest-Means-Proportions/BS704_HypothesisTest-Means-Proportions3.html [Accessed 19 April 2014]

Yale University (2014) Department of Statistics [Online] Available From:http://www.stat.yale.edu/Courses/1997-98/101/linreg.htm [Accessed 20 April 2014]

Chron (2014) The Advantages of Regression Analysis & Forecasting [Online] Available From:http://smallbusiness.chron.com/advantages-regression-analysis-forecasting-61800.html [Accessed 26 April 2014]

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6.0 Appendix

6.1 Data used for Investigation 1 (Demand Forecasting)

Month Time Frame Monthly (X Variable) Quantity in Cases (Y Variable)Jan-13 1- 2993Feb-13 2- 3574Mar-13 3- 3730Apr-13 4- 2612

May-13 5- 3014Jun-13 6- 3269Jul-13 7- 4412

Aug-13 8- 3725Sep-13 9- 4151Oct-13 10- 4771

Nov-13 11- 4310Dec-13 12- 3520Jan-14 13- 4142Feb-14 14- 4272Mar-14 15- 3529Apr-14 16- 4150

May-14 17- 3457Jun-14 18- 3366Jul-14 19- 4766

Aug-14 20- 3707Sep-14 21- 4109Oct-14 22- 5026

Nov-14 23- 4356Dec-14 24- 4013Jan-15 25- 4780Feb-15 26- 6101Mar-15 27- 4260Apr-15 28- 5754

Figure 12- Data used for Investigation 1 (Demand Forecasting)

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6.2 Descriptive Statistics of Investigation 1

Mean 4066.75Standard Error 149.212923Median 4125.5Mode #N/AStandard Deviation 789.560573Sample Variance 623405.898Kurtosis 0.79544963Skewness 0.6588062Range 3489Minimum 2612Maximum 6101Sum 113869Count 28Largest(1) 6101Smallest(1) 2612Confidence Level(95.0%) 306.159629

Quantity in Cases (Y Variable)

Figure 13 - Descriptive Statistics of Investigation 1

6.3 Data used for Investigation 2 (Hypothesis Testing)

Time Frame Sales (units) - Home Shelf Sales (units) - Brand Specific Gondola Shelf

Week 1 19 47Week 2 54 64Week 3 44 77Week 4 67 58Week 5 41 69Week 6 52 85Week 7 63 66Week 8 87 91Week 9 54 62Week 10 49 58Week 11 81 75Week 12 59 67

Figure 14 - Data used for Investigation 2 (Hypothesis Testing)

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6.4 Descriptive Statistics of Investigation 2 (Home Shelf)

Figure 15 - Descriptive Statistics of Investigation 2 (Home Shelf)

6.5 Descriptive Statistics of Investigation 2 (Brand Specific Gondola Shelf)

Sales (units) - Brand Specific Gondola Shelf   Mean 68.25Standard Error 3.531513968Median 66.5Mode 58Standard Deviation 12.23352324Sample Variance 149.6590909Kurtosis 0.063956847Skewness 0.319857491Range 44Minimum 47Maximum 91Sum 819Count 12Largest(1) 91Smallest(1) 47Confidence Level (95.0%) 7.772809837

Figure 16 - Descriptive Statistics of Investigation 2 (Brand Specific Gondola Shelf)

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Sales (units) - Home ShelfMean 55.83333333Standard Error 5.204650213Median 54Mode 54Standard Deviation 18.02943721Sample Variance 325.0606061Kurtosis 0.849186231Skewness -

0.135401034Range 68Minimum 19Maximum 87Sum 670Count 12Confidence Level(95.0%) 11.45535788

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