# Findings and Conclusions After processing the raw data and using the ARIMA package to model ride-share data, I was able to make predictions for the 25 days beyond the current data set. Qualitatively the data shows that as the weather gets warmer, the number of bike rentals increases, and over the course of two years, the number of rentals increases over the number of rentals from the previous year. As the data terminates at the end of one cycle, I expect the number of rentals to increase to a level higher than it was a year before, which is what the models are predicting. Therefore the results were what I expected the data appears to oscillate up and down over a 1-year period, with the overall data moving towards higher rental numbers.

Estimation of the Marginal Propensity to Consume (MPC): This report presents a statistical analysis of income, consumption, and gender data submitted for the IIM-A Research Intern position. The analysis includes: Exploratory Data Analysis: Summary statistics and normalised histograms to understand data distribution. Distribution Fitting: Application of Maximum Likelihood Estimation (MLE) to fit and compare Lognormal and Gamma distributions to income data using goodness-of-fit statistics (AIC, BIC, KS test). Econometric Analysis: Estimation of the Marginal Propensity to Consume (MPC) using linear regression models controlling for gender.

This is the Economic Trend Analysis Visualisation of Economics data from the ggplot2 package of RStudio. Using Economics data of the USA, I analyse the Us Unemployment trend from 1967 to 2015, predict the personal savings rate of US citizens, and using Time series forecasting, I forecast the unemployment trend in the US. After analysis I combined the seasonal pattern + Trend + Residuals, I derived the Trend composition and decomposed the unemployment time series.