1. What assumptions have you made in your model?
I calculated the seconds rate and determined the rate of 570 apps per second, then I calculated the app per minute and found that the rate was also close to 570, so I assumed the rate was constant all the time.Even though we know it was probably not the same because early on in the beginning of the app store, it was probably slower, and as time goes on more people buy I products and download apps from the app store, and the rate increases. I assumed it was constant all from day one till 25 B.
Below you see my calculations
|Seconds Rate||Data Point 1||Data||x seconds||y||Units|
|m=||570||apps per second|
|Minutes Rate||Data Point 2||Data||x minutes||y|
|m=||34,176||apps per minute|
|Hour Rate||2,050,579||apps per hour|
|Day Rate||49,213,890||apps per day|
a. Interpret the parameters in your linear model.
x=time in hour
b= at time 0, no apps
y= 2,050,579 * X+ B
using the hour rate because I do not want to bombard all day long.
final equation: y= 2,050,579 * X
b.What do the units of slope represent?
As you can see above the slope is the rate apps per time.
c.What does the y-intercept represent?
the number of apps downloaded at time zero.
a. According to your linear model, when did the app store sell its first app?
|10/10/2010||first day app store open||501.0477257||days earlier|
b. Calculate the answer mathematically then find the actual answer.
|When does it hit 25 B|
|341,492,345||left to go|
|3/1/2012||calculated date that it hit the apps store|
The reason why they are different is because I stated that my rate was constant, but when the 25 B mark got closer more people decided to buy apps so they can win the grand prize. At the end of the day my prediction was close.