adultliteracy.csv- Data Source: UNESCO. Describes % of people ≥15 yrs, who can, with understanding, read and write a short, simple statement on their everyday life across different countries.
badteeth.csv - Data Source: WHO Has DMFT score of the 12-year-old child of various nations. DMFT is one of the simple indices to measure caries. DMFT stands for Decayed, Missing, and Filled Permanent Teeth
sugar_consumption.csv- Data Source: FAO Has Grams of sugar consumed/person/day of various countries
Answer I am looking for doing this analysis: Can we predict avg sugar consumption from literacy rate? What is a good model to find this? Is this model a good fit? What is the variance in this data?
Can we predict avg sugar consumption from literacy rate and DMFT score?
List of analysis done:
- Cleaning of Data.
- Computing basic statistics for 3 different variables- adult literacy, DMFT score, and sugar consumption across various countries.
- Finding Pearson Correlation between those 3 different variables.
- Draw the scatterplots to represent each correlation and interpret them.
- Finding Pearson coefficient and p-value to find if the correlation is statistically significant between different variables.
- Finding a simple linear regression model to predict avg sugar consumption from literacy rate
- Forming a model to predict avg sugar consumption of a country based on literacy rate and DMFT score, using multiple linear regression. (MLR)
- visualizing MLR using a distribution plot
- Showing residual plot between avg sugar consumption and avg literacy and interpreting it.
- Finding goodness of fit of SLR and MLR using r square and Mean Squared Error
- Conducting the ANOVA test
- Training and Testing of Data- inconclusive