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

• 1. STATATHON Group 1 Presented by :- Vivek Sengar Dhwani Patel Prince Vadher Lipi Oza Sameer Shah Dhruvi Dudhat
• 2. 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
• 3. Discriptive Analysis  Graph and tabular format  Mean, Mode, Median  Correlation and regression
• 4. 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
• 5. 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
• 6. Graph and tabular format  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
• 7. Graph and tabular format  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
• 8. Graph and tabular format  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
• 9. Graph and tabular format  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
• 10. 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 Standard Error 0.379269813 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
• 11. 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.
• 12. 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.
• 13. REGRESSION SUMMARY OUTPUT Regression Statistics Multiple R 0.030775923 R Square 0.000947157 Adjusted R Square 0.000690859 Standard Error 23.67720921 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
• 14. 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.
• 15. 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
• 16. 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.
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