Data Analysis of Apple Inc.

INTRODUCTION: This project aims to research and understand an individual stock price change, Apple (identified as AAPL in the stock exchange market); by studying the statistics generated by market activity (prices) of the stock along with the price changes in Dow Jones Industrial Average (DJIA) and the Dow Jones U. S. Technology Index (DJUSTC). Assuming that the changes in DJIA and DJUSTC could be estimated by market moving economic indicators (e. g. updates of GDP, Jobless claims, Consumer confidence index, etc.

, this analysis will help an individual investor to identify patterns and trends that may suggest the daily price change of the AAPL stock. DATA DESCRIPTION: The data used in this analysis is a time series data of the daily stock/index price starting from February 1st, 2013 to May 24th, 2013 (accessed 5-27-2013 from www. finance. yahoo. com). The three variables (AAPL, DJI, and DJUSTC) show the percent changes in daily stock/index prices of each of the variables.

The percent change was calculated as follows: % Change in Price= (Today^’ s Closing Price-Yesterday^’ s Closing Price)/(Yesterday^’ s Closing Price) The independent variable in this analysis is the daily % change in AAPL stock price. The two dependent variables are the daily % changes in DJI and DJUSTC index price. RESULTS: Below are the results from StatPad DISCUSSION: The prediction equation is Change in AAPL stock price = +0. 00026 – 1. 66237*DJI change + 2. 3693*DJUSTC change The analysis shows that the changes in DJI and DJUSTC together explain a very highly significant proportion of the variation in AAPL.

The coefficient of correlation is 0. 56 which equals the proportion of variance explained by the regression model. The standard error of estimate is 0. 011796385, which indicates the typical size of errors made in predicting AAPL using the regression model. Figure below shows the actual change in stock price vs. he predicted change in stock price from this analysis.

In general, both curves show a similar trend which confirms the fact that our regression model provides a good estimate of the variation in AAPL. The predicted equation also shows that by holding the DJUSTC constant, we estimate that -1. 662374469 is the increase in AAPL associated with an increase in DJI of 1 unit. Similarly by holding the DJI constant, we estimate that 2. 236928699 is the increase in AAPL associated with an increase in DJUSTC of 1 unit.