• This is an interesting datasets from Kaggle. The link for dataset is https://www.kaggle.com/borapajo/food-choices. What kind of things affect weight and GPA? We mey explore some patterns from this dataset.
• Because of the limiation of this dataset, here I only explore some factors for fun. In the future, with data quality checking and larger data volume, we will be able to run some more thorough data analysis.

This is a scatter plot of GPA VS weight. There is no pattern observed from this scatterplot. We may assume the independence between two variables here.

The median of female students is slightly higher than male's. However, there is no strong evidence in the graph shows that gender affects students' GPA.

In the graph, it is obvious that male students' weights are higher than female students' which agrees with common sense.

We saw that more people state that they eat cereal as breakfast. Which group is heavier?

1 here represents cereal and 2 represents donut. The legend of this plot shows pretty well in ggplot but not in plotly. Same things happened in the late slides. The median of donut group is 8.5 pounds higher than cereal group's.

From this graph, we can see that drinking frappuccino group's weight is no larger than drinking espresso.

veggies_day - How likely to eat veggies in a day? 1 - very unlikely 2 - unlikely 3 - neutral 4- likely 5 - very likely

Here, I only run some basic exploratory data analysis here but no statements can be made here. With some data quality states in the documentation and more data, we can do some statistics testing there to test some assumptions such as t_test, rank_sum test, or ANOVA. In the future, this may be one of our direction we can take to get more information from data.