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Looking at differences between income levels of different racial groups, trying to identify best predictor variables
The variation of income in different racial groups
The distribution through the different counties in New York.
uncover patterns and correlations that may explain variations in income among different racial groups
uncover patterns and correlations that may explain variations in income among different racial groups.
Team 7 studies the income disparities that affects minority groups.
Uncover pattern and correlation between income and differrnt racial groups.
2. What was the best part of the teams work?
I think looking at changes across different counties is a good idea, adds another level of analysis for more useful information
They did a map to show the average income of different cities to show what place has the higher income. Also the education level.
There is a graph shows different levels of concentration in New York. The team not only included the income of the New York but also the whole United States.
has a very good graphical representation that shows the map and dispersion of income across different regions.
the visualization of the dataset is nice, and make a map to show the education status. They use good technique to clear the datasets.
The interactive graph is nicely done very effective to show their thesis. The predictors are nicely selected.
The team has a interactive and clear mapping of income levels across different counties in New York State. With coloring that represents different income levels.
3. How would you suggest improving the team's work?
possibly find more predictor variables to see if you can find more interesting connnections between race + income levles
I think they can do the interaction term race and education level to show how might education level might influence the relationship between race and income
Do more research on the data sides, seems like the research is not quantitate enough. It will be better be supported by more data.
still needs to work on the code to create more meaningful analysis across various counties and support the initial thesis.
it would be better to discover the income disparities across the world, and talk about the situation of many other countries.
They might need to work on the display of the race distribution among counties, but the graphs are already very nice from my perspective.
The team already has sufficient analysis to support their main thesis. However, they can build a regression model to explore other variables that affects income level
4. Do you have any other comments or ideas?
N/A
Feedback from presentation by Kexin Lin
1. Describe the main idea of the project
They analyze the racial disparity reflection on education level.
Investigated racial disparity in education levels. They also correlated this with average income.
racial disparities reflecting on education level, household on New York State.
New York State income analysis with socieoeconomic data
The relationship between racial difference and education level.
Racial disparity in NY. education vs income data comparison
2. What was the best part of the teams work?
I think they did very well on the graph about average income vs average income by education level and race.
I thought that the color-coded state maps that they created were extremely cool! They also had labels and the ability to zoom in. They also ran a range of statistical tests.
Make use of heat-map,
Make use of interactive map
Make use of multi-nomial regression to build model since linear regression does not work on categorical target
good interactive maps, different scales for different characteristics, good anova test and interpretation
They try different types of regression models, and they decided to go with the model that has better performance.
multinomial regression model visualization was pretty good and it is interactive. There is a lot of coding done so I would say it is a good job done.
3. How would you suggest improving the team's work?
I think they did very well, so they don't need to improve anything, just continue their project. I think it is very good to use ANOVA in their project
Some of the colors on their graphs were a little hard to see, particularly in one of the first bar charts.
The predicted probability plot was not clear to understand. Perhaps more details on it can be helpful.
include a demographic analysis, include scatterplots to aid with regression analysis, explain impact of analysis
I think they have a clear knowledge of what they are working on and what they can develop on. I think they can write their explanation of trying different models in their website page.
Make the blog post work so it is easier to present. Explore more options or add it more data, the project looks complete.
4. Do you have any other comments or ideas?
No.
Feedback from presentation by Siddhaarth Chakravarthy
1. Describe the main idea of the project
The main idea was to uncover patterns and correlations that may explain variations in income across different racial groups.
The primary goal was to identify trends and connections that could account for differences in earnings among various ethnic groups.
2. What was the best part of the teams work?
The team utilized a great data set that involved low to high income groups in the New York area. There is also a great focus on data visualizations, with heatmaps, statistical models, and graphs that make the analysis very clear. They clearly prove a correlation between race, income, and education.
The team worked with an extensive dataset covering income groups from low to high in the New York area. They placed a strong emphasis on data visualizations, including heatmaps, statistical models, and graphs, which significantly clarified the analysis. These visualizations effectively demonstrated the correlation between race, income, and education levels.
3. How would you suggest improving the team's work?
I think the team already has covered a wide variety of issues surrounding their topic. They should prioritize more descriptives that detail their project rather than just have maps and figures. That way, the webpage would be easier to follow and understand, as every graph or map would have descriptives backing it.
It sounds like the team has already addressed many aspects of their topic comprehensively. They should focus on adding more detailed descriptions to their project, complementing the maps and figures with narrative explanations. This approach would make the webpage more accessible and easier to follow, ensuring that every visual element is supported by descriptive text to enhance understanding.
Feedback from presentation by Yuan Tian
1. Describe the main idea of the project
income and education level with racial disprete and how are they related to each other
They are analyzing low to median income information in New York, and are studying factors like what type of home they live in, what county they're from, race, and education level.
NY state for income for different ricial group- housing
The team is trying to find the relationship between native New York family educatioin level with their race
This project analyzes income information, ethnicity, and housing, and country/region that they're from in New York City.
2. What was the best part of the teams work?
create a map that interprate the racial data and the mapping and proprotion and the graph is interactive and can make the comparetion that is easier
They have a lot of visuals that are informative that they can analyze further to produce valuable insights. Their interactive map is also super cool.
Using table to demonstrate different counties and interactive table to let audience select the graphs.
The teams used different bar chart to find the correlation of each variable in the tables, which are helpful for their later study, and use tidycensus package to plot the heatmap for each area
I think they had a lot of data to analyze and they did a good job of handling the amount of data that they were working with.
3. How would you suggest improving the team's work?
have a clear thesis that can working with and better interprate the data and try to make the model and improve the statistical importance
They should pick specific factors they want to analyze further since their dataset is extremely large and they have a lot of variables they can look into.
Improve the significance of the model with ricial group and incomes becasue the graph has already demonstrate a strong correlation
The team should include more statistical model into the graph to have a better information regarding the correlation they are trying to find
I think that they have struggled to find statistical significance of some of their variables, and it would be interesting to know if that exists.
4. Do you have any other comments or ideas?
N/A
The overall is very good
The text was updated successfully, but these errors were encountered:
Feedback from presentation by Dmitriy Kazlouski
1. Describe the main idea of the project
Looking at differences between income levels of different racial groups, trying to identify best predictor variables
The variation of income in different racial groups
The distribution through the different counties in New York.
uncover patterns and correlations that may explain variations in income among different racial groups
uncover patterns and correlations that may explain variations in income among different racial groups.
Team 7 studies the income disparities that affects minority groups.
Uncover pattern and correlation between income and differrnt racial groups.
2. What was the best part of the teams work?
I think looking at changes across different counties is a good idea, adds another level of analysis for more useful information
They did a map to show the average income of different cities to show what place has the higher income. Also the education level.
There is a graph shows different levels of concentration in New York. The team not only included the income of the New York but also the whole United States.
has a very good graphical representation that shows the map and dispersion of income across different regions.
the visualization of the dataset is nice, and make a map to show the education status. They use good technique to clear the datasets.
The interactive graph is nicely done very effective to show their thesis. The predictors are nicely selected.
The team has a interactive and clear mapping of income levels across different counties in New York State. With coloring that represents different income levels.
3. How would you suggest improving the team's work?
possibly find more predictor variables to see if you can find more interesting connnections between race + income levles
I think they can do the interaction term race and education level to show how might education level might influence the relationship between race and income
Do more research on the data sides, seems like the research is not quantitate enough. It will be better be supported by more data.
still needs to work on the code to create more meaningful analysis across various counties and support the initial thesis.
it would be better to discover the income disparities across the world, and talk about the situation of many other countries.
They might need to work on the display of the race distribution among counties, but the graphs are already very nice from my perspective.
The team already has sufficient analysis to support their main thesis. However, they can build a regression model to explore other variables that affects income level
4. Do you have any other comments or ideas?
N/A
Feedback from presentation by Kexin Lin
1. Describe the main idea of the project
They analyze the racial disparity reflection on education level.
Investigated racial disparity in education levels. They also correlated this with average income.
racial disparities reflecting on education level, household on New York State.
New York State income analysis with socieoeconomic data
The relationship between racial difference and education level.
Racial disparity in NY. education vs income data comparison
2. What was the best part of the teams work?
I think they did very well on the graph about average income vs average income by education level and race.
I thought that the color-coded state maps that they created were extremely cool! They also had labels and the ability to zoom in. They also ran a range of statistical tests.
Make use of heat-map,
Make use of interactive map
Make use of multi-nomial regression to build model since linear regression does not work on categorical target
good interactive maps, different scales for different characteristics, good anova test and interpretation
They try different types of regression models, and they decided to go with the model that has better performance.
multinomial regression model visualization was pretty good and it is interactive. There is a lot of coding done so I would say it is a good job done.
3. How would you suggest improving the team's work?
I think they did very well, so they don't need to improve anything, just continue their project. I think it is very good to use ANOVA in their project
Some of the colors on their graphs were a little hard to see, particularly in one of the first bar charts.
The predicted probability plot was not clear to understand. Perhaps more details on it can be helpful.
include a demographic analysis, include scatterplots to aid with regression analysis, explain impact of analysis
I think they have a clear knowledge of what they are working on and what they can develop on. I think they can write their explanation of trying different models in their website page.
Make the blog post work so it is easier to present. Explore more options or add it more data, the project looks complete.
4. Do you have any other comments or ideas?
No.
Feedback from presentation by Siddhaarth Chakravarthy
1. Describe the main idea of the project
The main idea was to uncover patterns and correlations that may explain variations in income across different racial groups.
The primary goal was to identify trends and connections that could account for differences in earnings among various ethnic groups.
2. What was the best part of the teams work?
The team utilized a great data set that involved low to high income groups in the New York area. There is also a great focus on data visualizations, with heatmaps, statistical models, and graphs that make the analysis very clear. They clearly prove a correlation between race, income, and education.
The team worked with an extensive dataset covering income groups from low to high in the New York area. They placed a strong emphasis on data visualizations, including heatmaps, statistical models, and graphs, which significantly clarified the analysis. These visualizations effectively demonstrated the correlation between race, income, and education levels.
3. How would you suggest improving the team's work?
I think the team already has covered a wide variety of issues surrounding their topic. They should prioritize more descriptives that detail their project rather than just have maps and figures. That way, the webpage would be easier to follow and understand, as every graph or map would have descriptives backing it.
It sounds like the team has already addressed many aspects of their topic comprehensively. They should focus on adding more detailed descriptions to their project, complementing the maps and figures with narrative explanations. This approach would make the webpage more accessible and easier to follow, ensuring that every visual element is supported by descriptive text to enhance understanding.
Feedback from presentation by Yuan Tian
1. Describe the main idea of the project
income and education level with racial disprete and how are they related to each other
They are analyzing low to median income information in New York, and are studying factors like what type of home they live in, what county they're from, race, and education level.
NY state for income for different ricial group- housing
The team is trying to find the relationship between native New York family educatioin level with their race
This project analyzes income information, ethnicity, and housing, and country/region that they're from in New York City.
2. What was the best part of the teams work?
create a map that interprate the racial data and the mapping and proprotion and the graph is interactive and can make the comparetion that is easier
They have a lot of visuals that are informative that they can analyze further to produce valuable insights. Their interactive map is also super cool.
Using table to demonstrate different counties and interactive table to let audience select the graphs.
The teams used different bar chart to find the correlation of each variable in the tables, which are helpful for their later study, and use tidycensus package to plot the heatmap for each area
I think they had a lot of data to analyze and they did a good job of handling the amount of data that they were working with.
3. How would you suggest improving the team's work?
have a clear thesis that can working with and better interprate the data and try to make the model and improve the statistical importance
They should pick specific factors they want to analyze further since their dataset is extremely large and they have a lot of variables they can look into.
Improve the significance of the model with ricial group and incomes becasue the graph has already demonstrate a strong correlation
The team should include more statistical model into the graph to have a better information regarding the correlation they are trying to find
I think that they have struggled to find statistical significance of some of their variables, and it would be interesting to know if that exists.
4. Do you have any other comments or ideas?
N/A
The overall is very good
The text was updated successfully, but these errors were encountered: