In this research project, I aim to analyze the factors influencing the indicator of the Sustainable Development Goals related to "Zero Hunger," which focuses on ending hunger, achieving food security, improving nutrition, and promoting sustainable agriculture. I will consider countries and years as observations to understand what drives this indicator's variation.

Selecting the Best Model Using AIC (Economic and Unemployment Data As case Study)

Data Description: Daily Count of Tweets for "Dove_real_beauty_sketches" Date Range: The data covers a period from April 15, 2013, to May 5, 2013, representing 21 consecutive days. Count of Tweets: Each entry in the dataset represents the daily count of tweets that included the hashtag or topic "Dove_real_beauty_sketches." The counts vary from day to day.

Weekly - Gasoline Data Analysis with R is a data analysis project that aims to analyze weekly gasoline sales data using the R programming language. The project involves retrieving the gasoline sales data, cleaning and pre-processing the data, and then performing various data analysis tasks using R. The project will cover a variety of data analysis techniques, including descriptive statistics, data visualization, and time series analysis. The project will begin by retrieving the weekly gasoline sales data from a public data source. The data will then be cleaned and pre-processed to remove any missing or erroneous values. After cleaning the data, the project will use R to explore the data using various descriptive statistics, such as mean, median, and standard deviation. The project will also use R to create various visualizations, such as bar charts, line charts, and scatter plots, to help identify any patterns or trends in the data. The project will also involve time series analysis to help understand the patterns and trends in the gasoline sales data over time. This will involve using R to create time series plots, identifying any seasonal patterns or trends, and using forecasting techniques to predict future gasoline sales. Throughout the project, the code used for data analysis will be shared along with explanations and interpretations of the results. The goal of the project is to provide readers with an understanding of how to use R to analyze weekly gasoline sales data and draw insights from the data.