statistical measurement project present
- 1. Statistical Measurement, Analysis & Research Zhang Kexin (kz2159) Final Project Presentation
- 2. Self-Introduction Hi, this is Roxie Zhang. I like rock music and stand-up show, and I’m currently considering forming a rock band. I would like to pursue a career in brand marketing and it is rather necessary to utilize data analyzing tools or learn how to work data analysts nowadays. It was such a pleasure to study with my classmates this semester. Now here’s my presentation for reviewing this course, Statistical Measurement, Analysis & Research. Github Repo link: https://colab.research.google.com/github/kexinez/NYU_Integrated_Marketing Kaggle Notebook link: https://www.kaggle.com/kexinezhang/women-clothing- ecommerce-analysis Linkedin URL: www.linkedin.com/kexin-zhang-972823166
- 3. Lessons What I’ve learned: • I think hypothesis testing will be useful in finding correlation of two items co-occurrence in a customer’s basket. Regression models can be utilized in predicting the amount of items sold or other figures. More importantly, I’m happy to learn how to find the most valuable customer segment. I can even visualize the analysis result to others now. • I guess the most valuable treasure I obtained from this class is that I’m no more that unconfident in data analysis. I used to freak out when I heard of the potential risk of getting in touch with data processing stuff. Now I would really get on hand and try if I can make some progress or learn it by searching on Internet.
- 4. Research Design • Dataset: Individual medical costs billed by health insurance of over 1330 beneficiaries in the US and their basic information. • URL: https://www.kaggle.com/artaseyedian/predicting-health-insurance- charge-with-tidymodels • Key variables include: Age of the insurant, sex, BMI, number of children, smoke or not, region, individual medical costs billed by health insurance • Research Design: To explore whether the medical costs are correlated with the number of children and BMI index or not, I will conduct linear regression models using Github. The research will help marketers find the customers with lower medical costs so that the insurance company is more likely to find this group so as to maintain the costs low.
- 5. Data Preparation • Sample: 13,38 insurants of a health insurance • From this research, I conducted a preliminary data inspection which categorized our customers according to their region in the US. What’s more, we can see that southeastern region not only occupied the largest share in our customers, it also has the highest percent of smokers compared to the non-smokers • https://datastudio.google.com/reporting/4c2b0b69-782f-4690-ab78-42b944d1fa27
- 6. Reproduce Regression analysis The 1st graph shows there is no clear linear relationship between bmi and charges. The 2nd graph shows there is no linear relationship between age and bmi.
- 7. • According to the regression results, the p value of the variable (children) is 0.015 (<0.05), thus we can reject the null hypothesis that the charges are not correlated with children. • the p value of the variable (bmi) is 0 (<0.05), thus we can reject the null hypothesis that the charges are not correlated with bmi. • It seems that children and bmi are positively correlated with charges • children is more influential on the charges but it also has higher stand error.
- 8. Insights • With 95% confidence level, we can say the two variables (children and bmi) have influence on charges. • This gives us clues on what insurants of high charges look like in the two aspects (more children and higher in BMI index). That is to say, in order for insurance company to make profits, we should absorb insurants who have fewer children and lower BMI.
- 9. Assumptions Check Assumptions: • 1. Satisfied: The error term is almost normally distributed. • 3. Not satisfied: The mean of the error term is not 0.
- 10. Assumptions Check • 2. Satisfied. The means of all these normal distributions of Y, given X, lie on a straight line with slope b. • 4. Not satisfied: The variance of the error term is not constant. • 5. Satisfied: The error terms are uncorrelated. In other words, the observations are not drawn independently. 6. Satisfied: The independent variables in X are not correlated. This is no issues of multi-collinearity. P-value = 0.631 > 0.05, we can conclude that at 0.05 significant level, we cannot reject the null hypothesis that the independent variables are not correlated.
- 11. Further Research • From the linear regression analysis I’ve done, no clear linear relationship can be obtained, but they do have a positive effect on the decision variable. Therefore, a guessing is that there are other variables are relevant in the level of insurants’ medical bills having not be inspected. • The recommendation is to investigate on more variables such as smoker or non-smoker, diet, sleeping habit and daily workout and also analyze these aspects in combination. Because some diseases do not generate just for one reason. Instead, they are results of convoluted factors. We need to obtain a more accurate statistical model using variables in order to predict customers’ medical bills.
- 12. Appendix Recap on Previous Assignments
- 13. Milestone 2
- 14. Milestone 3 Limitations of the research: Even though the p-value is under 0.05, the statistical power is too low. So the deviation of statistics can be large. This research only implies marital condition is correlated to the duration of calls, but did not find the quantitative relationship between them. Besides, duration’s relationship with other dimensions of information is also important for us to predict duration and target at valuable customers, which needs further research such as regression analysis.
- 15. The plots represent the relationship between the number of total international charge and the total international minutes. Result: There seems to be a linear relationship between x and y and they are positively correlated. Milestone 4: Regression
- 16. Milestone 5: Clustering Kaggle Notebook URL: https://www.kaggle.com/kexinezhang/customer- segementation-kz2159
- 18. Milestone 5: clustering Lowest recency: Cluster 1&2 Highest frequency: Cluster 1 Highest amount: Cluster 1 By the RFM criteria, we should choose the customer clusters with a lower recency, higher frequency and amount. From the K-means clustering results, we can see that customer with Cluster Labels=1 best fit the criteria.