Linear Regression Analysis Apple

Everything Apple Touches Turns to Gold

Within the last decade Apple has become one of the largest growing companies in the world and the largest valued company in the United States. According to a recent article in The Guardian, a global financial news website, “Apple set a record by becoming the first company to be valued at over $700bn (£446bn).” (Fletcher, N. 2014) This comes as no surprise to the average computer aficionado and shareholder as Apple has been making a name for itself since its inception. From its earliest Macintosh models to today’s iPhones, Apple has been a trailblazer for software, technology and revolutionizing the way we communicate on a Macro level. Their dedication to innovation, quality and service has made them

The coded years (0-7) will be placed in the X ranges representing the independent variable. With this model the first cell title will also be included. The Excel QM provides the analysts with the simple linear regression analysis as seen in FIGURE 1-3.

FIGURE 1-3 Simple Linear Regression Analysis Analysts will input the following information into a simple linear regression model provided in Excel QM using a simple linear regression formula Yi =b_0+ b_1 X_1. In FIGURE 1-3 the highlighted Coefficients are provided. The b_0 is -18.3975 and the b_1 is 26.3479, these coefficients are added to the formula that is represented in figure 1-4.

FIGURE 1-4 (Simple Linear Regression Model)

Once the coded year is put in, the formula can forecast each year’s global iphone sales to include 2015 which is forecasted to be 192.39 Million units. A scatter plot can also be viewed with this information. This is highlighted in figure 1-5.

FIGURE 1-5 Scatter Plot (Simple Linear Regression) The next model is the Quadratic Trend Model. The quadratic formula uses the least-squares method to forecast and can be written as Yi =b_0+ b_1 X_1+ b_2 X_2. In this formula the only difference is b_2 X_2 represents the estimated quadratic effect on Y. Figure 1-6 represents the comparison between the linear and quadratic