Sales Analysis for Good Harvest Organics

This assignment analyzes sales data from Good Harvest Organics to identify trends, popular products, and factors influencing sales performance. The analysis examines the relationship between sales and various variables like product mix, location, payment methods, rainfall, and seasonal effects. It also includes correlation analyses and paired t-tests to determine statistical significance.

Running head: BUSINESS ANALYTICS AND STATISTICS
Business Analytics and Statistics
Name of the student
Name of the university
Author’s note

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Table of Contents
Introduction......................................................................................................................................4
Problem Definition and business intelligence required...................................................................4
Results of Technical Analysis and Selected Analytics Method......................................................5
Research Question 1.....................................................................................................................5
Research Question 2.....................................................................................................................8
Research Question 3...................................................................................................................12
Research Question 4...................................................................................................................14
Research Question 5...................................................................................................................16
Discussion of the results and recommendations.............................................................................17
References......................................................................................................................................18

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Table of Tables
Table 1: Descriptive Statistics
Table 2: One-Sample Statistics
Table 3: One-Sample Test
Table 4: Descriptive Statistics Location based on Total Sales
Table 5: one-way ANOVA
Table 6: Multiple Comparisons
Table 7: Equality of means
Table 8: Chi-Square Tests
Table 9: Regression Coefficients
Table 10: ANOVA
Table 11: Model Summary
Table 12: Descriptive Statistics of Seasons in terms of Gross Sales
Table 13: one-way ANOVA
Table 14: Correlation between Total Profit and Rainfall
Table 15: Paired Samples Descriptive Statistics
Table 16: Paired Samples Correlations
Table 17: Paired Samples Test
Table of Figures
Figure 1: Product Class with Mean Total Sales
Figure 2: Means Plot
Figure 3: Scatter Plot of Dependent Variable
Figure 4: Scatter plot for Total Profit and Rainfall

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Introduction
The organic range of products in a small health shop on Sunshine Gold Coast named as Good
Harvest Kitchen deals from producer to customer homes (Good Harvest Organics 2017).
Although, the organizations aims to deal with quality but has also been following its functions of
wholesale and retail in the local community. Basically, the shop is known for its fruits that are
pesticides free and organic vegetables in bulk. It also helps in educating people about its seasonal
nature.
The data encompasses the whole year data of its sales and production of its product mix and
fruits shop business. The organization’s retail business needs to be analysed because as it is in its
start up phase. The cost of goods is reportedly tend to be high and has been considered as a main
challenge. On the contrary, the data needs to be analysed based on the research questions
highlighted for the running of the business.
Problem Definition and business intelligence required
The data has been taken from the “Health Food Shop” in the Sunshine Coast. Firstly, the data
will be examined using descriptive statistics followed by the other measures like One-way
Anova, regression, t – test, etc.
1. What are the top/worst selling products in terms of sales?
2. Are the differences in sales performance based on where the product is located in the
shop? How does this affect both profits and revenue?
3. Is there a difference in sales and gross profits between different months of the year?
4. Are their differences in sales performance between different seasons? How does this
related to rainfall and profits?

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5. Do net sales and gross sales differ from GST inclusive or exclusive?
6. What location in the shop makes the most amount of Profit?
Results of Technical Analysis and Selected Analytics Method
Research Question 1
What are the top/worst selling products in terms of sales?
Table 1: Descriptive Statistics
Total Sales ($)
Product Class N Mean Median Mode Std.
Deviation
Variance Minimum Maximum
Valid Missing
Ayurvedic 3 0 226.25 164.75 10a 252.677 63845.688 10 504
Bakery 44 0 432.67 66.43 10 884.016 781484.689 7 3793
Chocolates & Slices 5 0 37.01 31.00 19a 16.029 256.937 19 61
Coconut Water 11 0 514.23 380.55 21a 562.668 316595.521 21 1794
Dairy 66 0 619.05 259.43 26a 1473.793 2172066.887 10 10814
Drinks 59 0 574.25 136.77 18 1729.244 2990286.476 5 11910
Dry Goods 84 0 341.26 121.99 10 604.439 365346.512 2 3300
Freezer 62 0 202.45 91.63 20 421.301 177494.835 5 3252
Fridge 51 0 354.21 211.95 14a 389.326 151574.955 9 1535
Fruit 54 0 1048.68 356.47 3a 2469.413 6098002.002 3 17276
Grocery 64 0 108.74 72.73 54a 108.260 11720.212 5 597
Harvest Kitchen 4 0 44.97 38.15 24a 24.197 585.501 24 80
Health products 17 0 332.78 79.90 18 757.809 574274.090 15 2914
Herbal Teas 4 0 17.96 8.13 2a 24.373 594.032 2 54
Household 25 0 196.23 46.50 7a 255.520 65290.340 7 987
Juicing 1 0 5.00 5.00 5 5 5
Market 2 0 88.75 88.75 20a 97.227 9453.125 20 158
Meats Smallgoods 34 0 176.72 97.98 5a 259.025 67093.975 5 1423
Milks non dairy 9 0 224.55 120.75 12a 297.149 88297.765 12 968
Oils & Vinegars 25 0 310.81 97.30 80a 421.687 177820.285 9 1815
Other 9 0 33.53 33.90 34 25.949 673.334 0 88
Packaging 8 0 62.27 28.41 2a 105.620 11155.505 2 320
Pasta 15 0 114.22 59.50 8a 138.521 19188.046 8 488
Pastas 1 0 35.80 35.80 36 36 36

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Personal Products 96 0 84.38 46.71 44 114.318 13068.525 2 676
Salad Greens 1 0 24.50 24.50 25 25 25
Snacks 2 0 20.33 20.33 20a .742 .551 20 21
Snacks &
Chocolates 110 0 246.14 78.00 14 481.385 231731.518 4 2972
Spices 14 0 18.99 11.40 8 32.060 1027.817 4 129
Spreads, Sauces,
Sweeteners 28 0 113.60 25.53 7a 296.333 87813.485 6 1310
Stocks Sauces 6 0 32.29 30.28 20 12.168 148.064 20 49
Tea Coffee 24 0 88.55 29.55 5 147.157 21655.100 5 583
Tinned Goods 8 0 48.09 44.32 6a 32.507 1056.731 6 109
Vegetable 76 0 871.49 271.33 4a 1226.302 1503816.938 4 5554
Water 12 0 1866.88 446.25 630a 2541.630 6459885.188 15 6500
a. Multiple modes exist. The smallest value is shown
The products class has been organized with the total sales group. Table 1 analysis the total sales
based on the descriptive statistics. The maximum total sales has been seen in Water being the
essential product giving average sale of $1866.68. However, the least variables in total sales has
been seen in Snacks which is 0.742 because the amount of sales from snacks is more or less the
same. On the contrary, the lowest average sales in seen in Juicing which is only $5.00.
Best Selling Product = Water
Worst Selling Product = Juicing
Also, figure 1 explains the mean sales of every product as given below.

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Figure 1: Product Class with Mean Total Sales
Part a)
The hypothesis that can be framed is:
H0: There is no difference in the payment methods
H1: There is difference in the payment methods
Table 1: One-Sample Statistics
N Mean Std. Deviation Std. Error Mean
Cash_Total 366 404.29 153.643 8.031
Credit_Total 366 584.80 228.860 11.963
Visa_Total 366 555.85 244.870 12.800
Mastercard_Total 366 22.09 67.823 3.545

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Table 3: One-Sample Test
Test Value = 0
t df Sig. (2-tailed) Mean Difference 95% Confidence Interval of the
Difference
Lower Upper
Cash_Total 50.340 365 .000 404.287 388.49 420.08
Credit_Total 48.885 365 .000 584.798 561.27 608.32
Visa_Total 43.427 365 .000 555.849 530.68 581.02
Mastercard_Total 6.232 365 .000 22.094 15.12 29.07
Two analyze the different payment methods, one sample t –test is done. As per the t statistics, all
the payments individually are above the t critical value =1.96. Moreover, all p-values are less
than 0.05 level (95% level) which states that null hypothesis will be rejected. The conclusion will
be drawn is that there is difference between payment methods.
Research Question 2
Are the differences in sales performance based on where the product is located in the shop? How
does this affect both profits and revenue?
The sales performance based on location of the product in the store can be highlighted using the
following hypothesis.
H0: The five different locations of the store have total sales performance
H1: The five different locations of the store do not have total sales performance
Table 2: Descriptive Statistics Location based on Total Sales
Statistics
Total Sales ($)

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Location of product
in shop
N Mean Median Mode Std.
Deviation
Variance Minimum Maximum
Valid Missing
Front 155 0 572.75 125.10 10 1430.657 2046779.018 7 11910
Left 376 0 218.22 76.75 18 427.614 182853.721 0 3300
Outside Front 12 0 3384.37 1735.80 435a 4719.347 22272239.937 435 17276
Rear 180 0 536.07 224.50 14a 1072.153 1149511.551 4 10814
Right 311 0 239.89 66.36 44 553.004 305813.096 2 4236
a. Multiple modes exist. The smallest value is shown
Table 3: one-way ANOVA
Sum of Squares df Mean Square F Sig.
Between Groups 134299725.024 4 33574931.256 37.176 .000
Within Groups 929333380.817 1029 903142.255
Total 1063633105.841 1033
The ne way ANOVA table studies the F statistics and its significance. The F statistics here is
37.176 which is greater than F value at df (1029,4). Also, the p value is less than 0.05 level
stating to reject null hypothesis stating that five different location stores have different total sales
performance.

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Table 4: Multiple Comparisons
The post hoc tests of multiple comparison state that total sales comparison of “Outside Front”,
and Outside Front in all 4 locations have significant vales. This further explains that p value is
less than 0.05 level in the respective stores giving significant results. Also, as per the descriptive
statistics, the outside front location is the best ($3384.37) in total sales and the least is the left
location (218.22)
Table 7: Equality of means
Test of Homogeneity of Variances
Total Sales ($)
Levene Statistic df1 df2 Sig.
47.870 4 1029 .000
Also, the results of homogeneity are showing significant that means rejecting the null hypothesis.
However, the mean plot is given below.

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Figure 2: Means Plot
Additional Part
How does this affect both profits and revenue?
The analysis has been drawn based on the profits using Chi square results.
Table 8: Chi-Square Tests
Value df Asymp. Sig. (2-sided)
Pearson Chi-Square 3872.520a 3688 .017
Likelihood Ratio 2655.795 3688 1.000
Linear-by-Linear Association 1.439 1 .230
N of Valid Cases 1034
a. 4615 cells (100.0%) have expected count less than 5. The minimum expected count is .01.

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The chi square results show that the results are 3872.520 significant based on the asymptotic
result which is 0.017 which is less than 0.05 levels. Also, this means that the results of location
of store on profits are significant.
Research Question 3
Is there a difference in sales and gross profits between different months of the year?
The hypothesis framed is:
H0: There is no association between gross sales, profits and between different months
H1: There is an association between gross sales, profits and between different months
Table 5: Regression Coefficients
Model Unstandardized Coefficients Standardized
Coefficients
t Sig.
B Std. Error Beta
1
(Constant) 6.075 .597 10.179 .000
Gross_Sales .000 .001 -.041 -.713 .476
Profit Total .029 .007 .252 4.414 .000
a. Dependent Variable: Month of the year
Regression Line
Different Months = 6.075 + 0*Gross Sales + 0.029*Profit Total
 Different Months = 6.075 + 0.029*Profit Total
This shows that one unit change in profit total would lead to 0.029 units change in different
months of the year.
Table 6: ANOVA
Model Sum of Squares df Mean Square F Sig.

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1
Regression 243.467 2 121.733 10.736 .000b
Residual 4115.965 363 11.339
Total 4359.432 365
a. Dependent Variable: Month of the year
b. Predictors: (Constant), Profit Total, Gross_Sales
The ANOVA table studies the significance value of F statistics which is 0.00 being less than p =
0.05 level. However, null hypothesis will be rejected in this case and would be stated that an
association between gross sales, profits and between different months.
Table 7: Model Summary
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .236a .056 .051 3.367
a. Predictors: (Constant), Profit Total, Gross_Sales
b. Dependent Variable: Month of the year
The model summary explains the model, this states that R square is 5.6% which states that model
is not a good fit and only 5.6% variations are explained by the regression line. The scatter plot of
dependent variable is given below.

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Figure 3: Scatter Plot of Dependent Variable
Research Question 4
Are their differences in sales performance between different seasons?
The null hypothesis being framed as:
H0: The gross sales performance is same for all seasons.
H1: The gross sales performance is different for all seasons.
Table 8: Descriptive Statistics of Seasons in terms of Gross Sales
N Mean Std.
Deviation
Std.
Error
95% Confidence Interval for
Mean
Minimum Maximum
Lower
Bound
Upper
Bound
Summer 91 1042.44 349.184 36.604 969.71 1115.16 0 1864
Autumn 92 1065.37 341.391 35.593 994.67 1136.07 0 1753
Winter 92 983.65 264.131 27.538 928.95 1038.35 61 1502
Spring 91 1088.88 339.446 35.584 1018.18 1159.57 0 2642
Total 366 1044.97 326.285 17.055 1011.43 1078.51 0 2642
As per the descriptive statistics, the mean of gross sales is maximum for spring season
($1088.88) and the least for winter season ($983.65).
Table 9: one-way ANOVA
Sum of Squares df Mean Square F Sig.
Between Groups 560240.410 3 186746.803 1.765 .153
Within Groups 38298267.520 362 105796.319
Total 38858507.929 365
The above table explains one-way ANOVA based on F statistics. The significance value of F
statistics is 0.153 >0.05 level stating that the null hypothesis will be accepted sating that gross
sales performance is majorly same for all seasons.

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Part a)
How does this relate to rainfall and profits?
Based on the results, the results can be analysed based on the correlation value of profits and
rainfall.
Table 10: Correlation between Total Profit and Rainfall
Rainfall Profit Total
Rainfall
Pearson Correlation 1 .008
Sig. (2-tailed) .885
N 365 365
Profit Total
Pearson Correlation .008 1
Sig. (2-tailed) .885
N 365 366
Figure 4: Scatter plot for Total Profit and Rainfall
The correlation value came out to be as 0.008 which states that there is negligible correlation
between the two. Profits and Rainfall are even not statistically valid because of p being 0.885
which is greater than 0.05 level.

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Research Question 5
Do net sales and gross sales differ from GST inclusive or exclusive?
The hypothesis framed would be:
H0: Net sales and gross sales do not differ from GST inclusive or exclusive.
H1: Net sales and gross sales do differ from GST inclusive or exclusive.
Table 15: Paired Samples Descriptive Statistics
Mean N Std. Deviation Std. Error Mean
Pair 1 GST_Inclusive 114.42 366 48.723 2.547
GST_Exclusive 930.56 366 303.827 15.881
Pair 2 Net_Sales 1014.26 366 313.986 16.412
Gross_Sales 1044.97 366 326.285 17.055
Table 16: Paired Samples Correlations
N Correlation Sig.
Pair 1 GST_Inclusive & GST_Exclusive 366 .398 .000
Pair 2 Net_Sales & Gross_Sales 366 .996 .000
As per Table 16, the correlation between GST inclusive and exclusive is 0.398 which is weak
and positive correlation but net sales and gross sales have 0.996 correlation which is high, strong
and positive correlation.
Table 17: Paired Samples Test
Paired Differences t df Sig. (2-
tailed)Mean Std.
Deviation
Std. Error
Mean
95% Confidence
Interval of the
Difference
Lower Upper
Pair
1
GST_Inclusive -
GST_Exclusive
-
816.140 287.938 15.051 -845.737 -786.543 -
54.226 365 .000

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Pair
2
Net_Sales -
Gross_Sales -30.710 30.057 1.571 -33.799 -27.620 -
19.547 365 .000
The paired t test came out to be significant as p = 0.00 which is less than p = 0.05 level. Also, it
can be stated that both the pairs have negative t statistics stating that they are directional in
nature. In addition, null hypothesis is rejected explaining that net sales and gross sales do differ
from GST inclusive or exclusive.
Discussion of the results and recommendations
The examination of the product mix and sales summary data depicts that water is the best
product and the location of the shop that is better to attract customers is outside front in all
directions of shop. Also, sales and profits affect different months of the nature with seasonality
in nature. The payment methods whether cash, credit, Visa or MasterCard all have differences
when the customers pay. However, rainfall does not decide the profits as the correlation is less.
Hence, the recommendation should be favourable for the business in increasing sales, profits
with the increase in sales of best product and less sales of Juicing product adding the location to
“outside front”.

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References
Good Harvest Organics. (2017). Home. [online] Available at: https://www.goodharvest.com.au/
[Accessed 8 Oct. 2017].