# data[].attributes.body
Good analysis, may I know if you found any factors that were only really strong in certain industries due to the industry characteristics?

# data[].attributes.body
Thank you for the presentation. May I ask why you use your judgement rather than the VIF measure to find collinearity between the variables and remove those variables in the multiple variable regression?

# data[].attributes.body
Thanks for the presentation. Im wondering how did you guys decide on the key industries mentioned in the first slide? Would some industries be more significant than others in determining escore?

# data[].attributes.body
Thank you for the great presentation! I think your group mentioned that classification tree cannot be applied to e-score since its a continuous variable. Perhaps you could consider breaking down e-score into a few classes (discrete) to further supplement your regression analysis.

-on behalf of group 7

# data[].attributes.body
Hi Group 8! Thanks for the insightful presentation :) Just wanted to ask if you had developed hypotheses before selecting your IVs?

# data[].attributes.headline
Hi Group 8, thank you very much for the insightful presentation. There seems to be quite a lot of key insights into the various independent variables you have chosen. I would like to ask if there there could be possible multicollinearity between your independent variables. Would like to ask if there are any issues with the VIF between these Independent variabels also. Would also like to know if you run a multiple linear regression with all the variables and noticed any insignificant variables? Thank you!

# data[].attributes.subject
Hi Group 8, thank you very much for the insightful presentation. There seems to be quite a lot of key insights into the various independent variables you have chosen. I would like to ask if there there could be possible multicollinearity between your independent variables. Would like to ask if there are any issues with the VIF between these Independent variabels also. Would also like to know if you run a multiple linear regression with all the variables and noticed any insignificant variables? Thank you!

# data[].attributes.body
Great presentation with a logical flow of sequence! While some slides on coding were a bit cluttered, the presenters did well in bringing the class through the key points.

Wanted to understand if the group considered applying e_score together with the predicted IVs to a classification tree for further analysis?

# data[].attributes.body
Hi Group 8, thanks for your presentation! However I was wondering, if the classification tree was considered as it is possible to transform the continuous variable into a discreet one! Thank you!

# data[].attributes.body
Hi Group 8, thank you for your time in preparing the presentation! :)

I would like to commend your Box plot for each industry as it shows a good overview and the position of each industry as it is a different data visualisation we would have learnt in class

It was very thorough and precise as you had conducted a specific regression for each variable to show individual variable relation with ESG

Just for curiosity, I would like to ask if you had filtered out your data initially, would it have an impact on your further analyses?

# data[].attributes.body
Hi Group 8, thanks for your presentation! I like the boxplot you generated for each industry to see e-score, as it provides a clear visualization at a glance. I also like that your slides are detailed and easy to follow, eg: breaking the analysis of SLR into 4 clear and detailed steps. :)

# data[].attributes.body
Hi Group 8, thanks for your presentation! However I was wondering, if the classification tree was considered as it is possible to transform the continuous variable into a discreet one! Thank you!

# data[].attributes.body
Great insights! Would have been nice to see endogeneity-mitigating analysis such as with a lead variable for e_score.

# data[].attributes.body
Hi Group 8! I think it is insightful that you have separated the analysis into industries so that there is more specific recommendations tailored. Would like to know why y’all replaced N/A with 0 instead of industry average (if there were any particular reasons)?

# data[].attributes.body
Thank you for the presentation! I like that your slides were detailed and enjoy to follow, also the presentation was clear!

# data[].attributes.body
Hi group 8,

Thanks for your presentation, it was detailed and clear. However, noticed that yall choose a number of variables. Just curious would any multicollinearity be present among the variables?

# data[].attributes.body
Thank you for the presentation. May I ask whether there are multicollinearity for the financial measures used to predict whether the companies have an e-score?

# data[].attributes.body
Thank you for your presentation!

I just had one question for you guys:

When you mentioned how a company being very "out-there"/prominent was a factor in how likely they were to provide an e-score, how did you determine which companies were prominent and which companies weren't?

# data[].attributes.body
Hi group 4! I liked the organisation of your presentation and in-depth analysis of what kind of companies report e_scores. It was good that y’all explored both internal and external factors in detail. I would also like to know if you have any recommendations for smaller companies that don’t have as much incentive to report e_score?

# data[].attributes.body
Hi, thank you for the presentation. Your slides are nice! I see that you used log for the scalling, were they right skewed? If they are not, any particular reason for using log instead of squared?

# data[].attributes.body
Hi Group 4! Thank you for the insightful presentation. I just wanted to check, if you changed the NA score to 0, would it be more meaningful to drop the data entirely? Also, understood that you logged the mv_crsp variable, but was the purpose to reduce the complexity of the data or because you identified that the variable was skewed?

# data[].attributes.body
Thank you for your presentation! Can I ask when your group chose to remove the N.A. values for the financial ratios, will it affect any regression / analysis models that you do on the dataset thereafter?

# data[].attributes.body
Hi Group 4, thank you for your time in preparing the presentation! :)

The presentation was very clear and precise as it shows good thinking from your team, to replace missing values with industry average, however, I would like to ask will the original data still be considered as accurate since the average will be affected?

# data[].attributes.body
Great analysis and insights Team 4. Perhaps you can also explain how your 6 recommendations at the end were derived from your two regression results and classification tree.

# data[].attributes.body
Great presentation with a lot of insights from the team!

Wanted to ask whether it would be better to drop missing values instead of replacing it with industry average?

# data[].attributes.body
Hi, thanks for the presentation. The suggestions for improving escore were insightful. Could I clarify what criteria did you use for selecting significant variables before doing the regression? ie. why would assets or income be selected over another variable like staff expense(xlr)?

# data[].attributes.body
Thank you for the presentation! It was insightful and clear. I'm just wondering why did your group decide to use minsplit of 500?

# data[].attributes.body
Hi Group 4, Thank you for your presentation. May I check whether the data has been cleaned as this would affect classification tree results. Personally, I feel changing to zero may affect the integrity of the results.

# data[].attributes.body
Thanks for the presentation! I felt that the flow of the slides was really smooth. In particular, the recommendations given were interesting and the links were well drawn. I also like that there were many model companies given across the various industries.

# data[].attributes.body
Thank you for the presentation! May I ask if it would be better to use the industrial average for the N.A. values instead of dropping them as dropping the companies with no e_scores already reduces the sample size quite significantly?

# data[].attributes.body
Hi Group 8, thanks for the presentation. I would just like to enquire why did you choose not to adopt the classification tree method given that you can convert the e_score into a dummy variable for analysis?

# data[].attributes.body
Hello group 7!
Thank you for the engaging presentation!
Why you choose to use clustering for in your analysis? We felt that it would not have been the most appropriate since it cannot really help us to predict e-score or ways to improve a company's e-score. Thank you!

# data[].attributes.body
Hi thank you for your presentation. May I ask why your team chose to do winsorisation instead of logging the variables to maintain the integrity of the data?

# data[].attributes.body
Great explanations of data cleaning and scaling.

The recommendations were detailed and industry specific which was very nice :)

# data[].attributes.body
Thank you for the presentation. Maybe you can try to cut down the number of variables in the multi regression model to obtain more representative results for each variable?

# data[].attributes.body
Hi group 7! Thank you for the presentation. It interesting to see y’all use clustering as a method of analysis. The winsorization plots were useful to visualise the effects of the technique as well.

# data[].attributes.body
Hi, thank you for your presentation! Good logical flow! Any reason to use Adj R^2  and MAPE to compare the various models? As MAE (and ME) were usually compared with during the selection of models.

# data[].attributes.body
Great in-depth visualization and data exploration team 7. May I know why you chose not to do regressions by industry since you saw different trends of e score in different industries?

# data[].attributes.body
Thanks for the presentation! May I ask why did you group not consider creating summary variables for score and thus use classification tree? But analysis via industry was good! thank you!

# data[].attributes.body
hey guys! thanks for the presentation u guys slayed!! :)) can i ask why ya'll used clustering?

# data[].attributes.body
Hi Team 7, thank you for the presentation! Just wanted to check if your conclusion based on the classification tree were that smaller companies with fewer assets had a higher e-score. or companies with more assets had a higher e-score as I think what you concluded differed from the results of the tree regarding the at variable haha

# data[].attributes.body
Great presentation!! I would just like to know on what criteria did your group use to determine the ratios to be used in your project to be profit margin, quick ratio, debt ratio?

# data[].attributes.body
Hi group 4, thankyou for your presentation!
Just wondering would it be better if you could provide suggestions specifically caters to different industries?

# data[].attributes.body
Hi Group 7, Thank you for your presentation. May I ask what is the purpose of the clustering as there does not seem to be any usable data from the results (at least, based on the presentation)

# data[].attributes.body
hello group 7! thank you for the presentation! just curious why the group decided to create debt ratio, quick ratio and profit margin as new IVs?

# data[].attributes.body
Hi Group 7! Good presentation with a breakdown of recommendations for each industry.

Wanted to understand the reason for using clustering in the analysis? Not too sure how the results can be used.

# data[].attributes.body
Hi I just wanna ask for the scatterplot non-logged variables, how did you guys conclude that it was a positive linear relation?

# data[].attributes.body
Hi Group 7, interesting and comprehensive presentation. I would just like to clarify how does an increase in total assets lead to a higher e_score (as per your class. tree slide)?

# data[].attributes.body
Thank you for the engaging presentation! The statistical analysis was very in-depth! May I ask why did your choose to use clustering for in your analysis and specifically, what insights did clustering provide towards your recommendations?

# data[].attributes.body
Hi group 7, thanks for your presentation!
Just wondering what is the rationale behind of yall using clustering as one of your approaches?

# data[].attributes.body
Hi! Thank you for the wonderful presentation! Just have a quick question here! Can I ask are there any particular reasons why you guys chose to analyze the three ratios: profit margin, quick ratio and debt ratio? instead of current ratio?

# data[].attributes.body
Thanks for the presentation. It was interesting to see the insights of e-score distribution by industry. However, how would this help in finding out the determinants of e-score?

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Hi group 7, thank you for the anlsysis, thank you for the industry analysis. Do you have any advice on for a company to improve its E-score as a non-fortune 100 company?

# data[].attributes.subject
Hi group 7, thank you for the anlsysis, thank you for the industry analysis. Do you have any advice on for a company to improve its E-score as a non-fortune 100 company?

# data[].attributes.body
Hi Group 7, thanks for the presentation! The presentation was thorough yet clear! Can I just clarify why did your team choose to use the 3 new ratios as your new independent variables? Thank you!

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Hi! Thank you for the presentation! It was very clear and insightful! I was wondering why your group chose to choose the variables first before conducting the data analysis, as it might impose certain biases on your regression model? -Samantha Tham

# data[].attributes.body
Hi! great presentation, love the graphics! may I ask why your group chooses to do clustering? Not sure if it gives insights on determinants of escore! -Samantha Tham

# data[].attributes.body
Thanks for the interesting presentation!

Can I check if there were other ratios that you guys considered before settling on the 3 that you guys have?

# data[].attributes.body
Hello group 6, thank you so much for the great presentation. For the selection of variables based on what appeared after looking at all 3 models, you explained that you dropped xrd & capx. However, we noted that sales was added into the final model as well. Is there a reason why that was so? Thank you!

- On behalf of group 7

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Thanks for the presentation. Insightful analysis of chosen variables through regression and classification trees. However, could i clarify how can the issue of endogeneity be addressed through your data analysis?

# data[].attributes.body
Hi group 6! Thank you for the great presentation! can i just ask why did you guys chose to replace the missing e_score values with industry average since the e_score value might be very different even within the same industry?

# data[].attributes.body
hello! thank you for the presentation! may i ask if the team used human judgement to choose your finals variables? thank you!

# data[].attributes.body
Hi guys, thanks for the great presentation! Just wanted to ask why weren't those variables that appeared in at least one model (eg: regression) taken into consideration at the end?

# data[].attributes.body
Great explanation on data cleaning Team 6. Perhaps you can try running a different classification tree with different factors since your accuracy and no data rate were very close.

# data[].attributes.body
Hi, thank you for your presentation! Nice slides :) The part on classification tree was quite fast, any variables that we should take note in the classification tree? (e.g. smaller size firm has lower e-score?)

# data[].attributes.body
Hi Team 6, thanks for your presentations, your slides were really nice and informative.

I just wanted whether your data analysis included the effects of size on the company?

For example, you could consider using dividend payout rate to see if how much dividends they pay out of their net income.

Further to this, you did mention in your presentation that environmentally friendly companies are more likely to be able to sustain their dividend payouts but how did you account for that/show that if you were simply using the raw dividend payout amount without showing the trend/taking into account the time of dividend payouts?

# data[].attributes.body
Thanks for the presentation! I found the recommendations really insightful on how to improve the escore. The analysis in the top factors such as having solar panels which are expensive PPE is quite perceptive. Good job :)

# data[].attributes.body
Hi Group 6, Thank you for your presentation! I like the slides design! Can I check with you on whether the multicollinearity has been eliminated from the selected variables?

# data[].attributes.body
Hello group 6! thank you for the presentation, it was very insightful! I like the flow of the presentation where the statistical insights was followed closely with interpretation and research insights that was aligned with your findings

# data[].attributes.body
Thank you for the presentation! Is there a reason for your decision to replace the 95/5 percentile values with the 95/5 percentile instead of removing them from the dataset entirely?

# data[].attributes.body
Thank you Group 1 for the interesting presentation! Why did your group choose to pick the prediction values when the regression/classification tree didn't provide a significant correlation? Thanks in advance!

# data[].attributes.body
Hi Group 1, great presentation and I would like to enquire (1) what's your team's rationale for selecting the independent variables for the regression, and (2) why did you use only 15 variables for classification tree when there's a probability that other variables in the raw dataset could be significant predictors?

# data[].attributes.body
Great insights into the ICMW Group 1. Perhaps for your classification tree, you can consider running it with different variables since your accuracy is very close to your no data rate and your type 1 error is high.

# data[].attributes.body
Hi Group 1, thank you for the insightful presentation. Not sure if I missed it, but did you consider the endogeneity issue? Eg: your prediction of companies with high debt has greater internal control weakness, could there be other explanatory variables?

# data[].attributes.body
Thank you for the presentation! I would like to ask if there is a specific reason why the rationale behind only the last few (5-7) hypotheses was explained while the first few (1-4) were not touched on at all? Thank you!

# data[].attributes.body
Hello Group 4, thank you for the interesting and captivating presentation! I thoroughly enjoyed listening to your analysis and it was helpful that you provided a comprehensive rationale behind each prediction/hypothesis.

# data[].attributes.body
Hi! Thank you for the presentation. I'm just wondering why did your group decide to replace missing numbers with industry average instead of removing it?

# data[].attributes.body
hi group 1! thanks for the interesting presentation. can I clarify why did your group chose number of employees as the key variable behind Kraft Heinz fraud? I felt that net income would be more appropriate in this case since they inflated their EBITDA. thank you! - trudy

# data[].attributes.body
Hi, thank you for presentation! Was wondering if there are any insights on the classification tree models? (i.e. what type of companies are likely to be issued internal control issues?)

# data[].attributes.body
Thanks for the presentation. Thoughtful analysis of ICMW through picking out the variables for regression and classification tree analysis. I would like to clarify if you guys considered including any more variables, on top of the ones you guys analysed, which could have provided more insights? Additionally, would analysis by industry have proved helpful?

# data[].attributes.body
Hi group 1, thank you for the presentation! I like that your slides were clear, concise and visually appealing.
However, how did you pick your independent variables under data preparation?
Thank you.

- Elizabeth

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Hi group 1, you mentioned that auditors should look at companies that have lesser inventory, employees/ Cash flow etc etc. It seems that are these are related to smaller market cap companies, so in other words would u say that auditors should play particular attention to smaller companies in general? if so, is this an accurate conclusion?

# data[].attributes.subject
Hi group 1, you mentioned that auditors should look at companies that have lesser inventory, employees/ Cash flow etc etc. It seems that are these are related to smaller market cap companies, so in other words would u say that auditors should play particular attention to smaller companies in general? if so, is this an accurate conclusion?

# data[].attributes.body
Hi Group 1, thanks for the great presentation! Since you ran both classification tree and regression, which of the two tools did you think was more insightful?

# data[].attributes.body
Dear Group 1, thank you for the detailed analysis! You presented many hypothesis which was in-depth. Just curious, why did your group have so many hypothesis (quite specific in nature) for regression instead of coming up with a more general hypothesis and derive insights from there? ie. a broader regression to which shows that a high total liabilities (one of the variables) affects icmw instead of having the hypothesis targeting whether total liabilities affect icmw? Thank you!

# data[].attributes.body
hey guys! ya'll slayed :) nice presentation! could i ask why no. of employees was used as the sole proxy variable of firm size for heinz?

# data[].attributes.body
Hi Group 1! Thanks for your presentation, it was really insightful with a fresh perspective. Can I just check what does the team think that it is concerning that the sensitivity value of the classification tree is low which affects the accuracy of the model in the end?

# data[].attributes.body
Really interesting analysis that offered a fresh perspective on control weaknesses.

There was a lot of careful explanation of insights for each factor as well as company-type-specific significance.

Great work!

# data[].attributes.body
Sorry I couldnt really see the slides from zoom but great breakdown and analysis of the results generated. I also feel that your group provided great insights and recommendation. :)

# data[].attributes.body
Great way to catch attention by utilising gg.plot to present your findings. In addition I like your group's breakdown of classification tree.

# data[].attributes.body
Insightful analysis on internal control by integrating real world examples into the project. In addition, thank you for explaining the reason behind why you chose certain variables to focus on. Great job!

# data[].attributes.body
Great and insightful presentation with a clear and detailed thought process.

# data[].attributes.body
Hi Group 1, Thank you for your presentation! I like that your group presented a different view on internal control associations and determinants

# data[].attributes.body
Hello Group 3, thank you for the insightful presentation! Since the difference was negligble between the regression models using MAE/RMSE, you can consider using Adjusted R-squared value to prioritse a model and pick a model that has higher R-Squared values and Adjusted R-Squared values.

# data[].attributes.body
Great insights into the Audit Fees Group 3. Perhaps for your classification tree, you can consider running it with different variables since your accuracy is very close to your no data rate and your type 1 error is high.

# data[].attributes.body
Thank you for the presentation. There was interesting analysis of chosen variables through the classification trees and log variables. However, how can the issue of endogeneity be addressed through your data analysis?

# data[].attributes.body
Thank you for the presentation! I would like to ask if your group found the MAPE of >200 too high?

# data[].attributes.body
Thanks for your presentation! I was just wondering if there’s any reason why you picked 97% for your significance level?

# data[].attributes.body
Hello! You guys slaaaaaaayed the presentation!! Just wanted to know how you guys will be classifying the companies into big and small ones, in order to vary the audit fees? (like are there any rules / benchmarks?)

# data[].attributes.body
Hello Group 3! Thanks for your insightful presentation! Just wondering based on your data analysis, what would be your recommendations for the management to better price their audit services?

# data[].attributes.body
Hi Group 3, thank you for your time in preparing the presentation! :)

It was quite a thorough analysis to go through all the financial ratios which will allow potential audit firm to know where to put their focus exactly and explaining to the client that main audit work will be focused on the more important sectors, thus  more audit fees will be allocated respectively It was also a good idea to raise various queries and potential problems for small audit firms!

# data[].attributes.body
Hi! thank you for the great presentation! May I ask, how did you pick the variables to run in your regression?

# data[].attributes.body
Hello group 6, thank you for your presentation. Your slides were very clear and I really like the design of your slides! Your presentation was also very insightful and were very detailed with your presentation and findings