STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravaganza"

STATATHON
Group 1
Presented by :-
Vivek Sengar
Dhwani Patel
Prince Vadher
Lipi Oza
Sameer Shah
Dhruvi Dudhat

Objectives
 To summarise the data
 To identify patterns and links by utilizing recent
and historical data.
 To find trend pattern
 To compare and analyse the data

Discriptive Analysis
 Graph and tabular format
 Mean, Mode, Median
 Correlation and regression

Graph And Tabular Format
 This bar chart shows that the
age group between 48-57
shows highest frequency.
723 741 725 767 714
230
0
500
1000
18-27 28-37 38-47 48-57 58-67 68-77
FREQUENCY
AGE
BAR CHART
AGE FREQUENCY RF CF % F
18-27 723 0.19 723 18.5385
28-37 741 0.19 1464 19
38-47 725 0.19 2189 18.5897
48-57 767 0.2 2956 19.6667
58-67 714 0.18 3670 18.3077
68-77 230 0.06 3900 5.89744
GRAND TOTAL 3900 1

Graph and tabular format
 This pie chart shows that
maximum shopping behavior is
seen in males. 32%
68%
PIE
Female Male
GENDER FREQUENCY RF CF %F
FEMALE 1248 0.32 1248 32
MALE 2652 0.68 3900 68
GRAND TOTAL 3900 1

 This graph shows that
maximum review rating is given
to 4.5 – 5.
REVIEW RATING F RF CF LL UL MP
2.5-3 685 0.18 685 2.5 3 2.75
3-3.5 805 0.21 1490 3 3.5 3.25
3.5-4 766 0.2 2256 3.5 4 3.75
4-4.5 805 0.21 3061 4 4.5 4.25
4.5-5 839 0.22 3900 4.5 5 4.75
GRAND TOTAL 3900 1

 This line chart shows that
clothing is the category
showing maximum frequency.
0
500
1000
1500
2000
Accessories Clothing Footwear Outerwear
FREQUENCY
CATEGORY FREQUENCY RF CF %F
ACCESSORIES 1240 0.32 1240 31.7949
CLOTHING 1737 0.45 2977 44.5385
FOOTWEAR 599 0.15 3576 15.359
OUTERWEAR 324 0.08 3900 8.30769
GRAND TOTAL 3900 1

 This ogive shows relation
between upper limit and
cumulative frequency.
UL CF
3 685
3.5 1490
4 2256
4.5 3061
5 3900
0
500
1000
1500
2000
2500
3000
3500
4000
4500
3 3.5 4 4.5 5
CF

 This pie chart shows that the
shopping does not include
maiximum discount.
PIE CHART
No Yes
DISCOUNT F RF CF %F
NO 2847 0.73 2847 73
YES 1053 0.27 3900 27
GRAND TOTAL 3900 1

Mean, Mode, Median and quartile
FEMALE
Purchase Amount (USD)
Mean 59.764
Standard Error 0.379
Median 60.000
Mode 36.000
Standard Deviation 23.685
Sample Variance 560.998
Kurtosis -1.237
Skewness 0.013
Range 80.000
Minimum 20.000
Maximum 100.000
Sum 233081.000
Count 3900.000
MALE
Purchase Amount (USD)
Mean 59.76435897
Median 60
Mode 36
Standard Deviation 23.68539225
Sample Variance 560.9978061
Kurtosis -1.236593691
Skewness 0.012701758
Range 80
Minimum 20
Maximum 100
Sum 233081
Count 3900

INTERPRETATION
● Mean, mode and median of data given is
same as comparison is done between
purchase amount from male and female
point of view.
● There is a slight difference in skewness of
both the data otherwise whole data is same
means there is not much difference
between them.

CORRELATION
PURCHASE AMOUNT (USD) REVIEW RATING
PURCHASE AMOUNT (USD) 1
REVIEW RATING 0.030775923 1
INTERPRETATION
● Correlation between purchase amount and
review rating is positive, meaning that as
one variable increases, the other tends to
increase as well but in this case it is 0.0307
which is close to zero showing a negligible
relationship.

REGRESSION
SUMMARY OUTPUT
Regression Statistics
Multiple R 0.030775923
R Square 0.000947157
Adjusted R Square 0.000690859
Observations 3900
ANOVA
DF SS MS F SIGNIFICANCE F
REGRESSION 1 2071.746308 2071.746308 3.695519944 0.054631574
RESIDUAL 3898 2185258.7 560.6102359
TOTAL 3899 2187330.446
Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%
Intercept 55.94782751 2.021200579 27.68049253 0.00 51.98511672 59.9105383 51.98511672 59.9105383
X Variable 1 1.017755642 0.529426582 1.922373518 0.054631574 -0.020223692 2.055734977 -0.020223692 2.055734977

INTERPRETATION
● In above given regression table,
significance F shows value slight greater
than 0.05, which is borderline, and the p-
value for X Variable 1 is also close to the
common significance level which suggest a
very weak relation between selected
variables.

FORECAST
0
5
10
15
20
25
30
35
40
0 5 10 15 20 25
SCATTERED DIAGRAM
● The scattered diagram show random
fluctuation, upward trend with no
seasonality.
● So the best method to find forecast of next
year is moving average method as it shows
minimum error in comparisioin with the
other two.
INTERPRETATION

Conclusion
The data is helpful in knowing
consumer‘s shopping behavior.
This analysis can be used in predicting
future scope and to determine the
probability of events.
This can further be used in different
departments for eg: in making
marketing strategy and financial
analysis.

STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravaganza"

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