This R code analyzes and forecasts NVIDIA's stock prices using historical data. It includes time series visualization, linear regression for trend analysis, and exponential smoothing for short-term forecasting. Performance metrics like MSE and MAPE are used to compare models, with results visualized through bar charts to guide model selection. This script is ideal for understanding stock trends and making data-driven predictions.

This analysis explores the historical trends in 30-year fixed-rate mortgage interest rates using time series data. Key steps include data preprocessing, visualization of the interest rate trends over time, and fitting a linear regression model to estimate the trend equation. The analysis calculates model accuracy metrics such as Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE). Finally, the fitted model is used to forecast the average mortgage interest rate for a future period (Period 25, corresponding to 2024).

This document provides an analysis of logistic regression for predicting the probability of tire purchase based on performance ratings (Wet and Noise). The steps include data import, cleaning, model creation, and probability estimation