Question

Stock values for a certain company are recorded in the table.

I understand the first question and how to do the math not sure how to justify it. PLEASE HELP!

x (Weeks Since 5 Years Ago) y (Closing Price, in $)
most recent 260 67.06
7 days ago 259 67.00
1 month ago 256 66.24
6 months ago 234 60.04
1 year ago 208 54.69
3 years ago 104 47.31
5 years ago 0 28.09
Using a process similar to the one you used in part B, this exponential model was found as the best fit for this data:

y = 30.078(1.003)x.

Question 1
Question
Use the exponential model to make predictions about closing prices of this stock. Remember that x represents the number of weeks since the first data point, so the value of x today would be 260.

Type the correct answer in each box. If necessary, round the answers to the nearest cent.

One week from today, the stock is predicted to close at $
65,734.37
.

One month from today, the stock is predicted to close at $
66,327.75
.

One year from today, the stock is predicted to close at $
76,584.35
.

Question 2
Do you think the model is a good predictor of the future closing price of the stock? Justify your response in three to four sentences.

Answer 1

Question 1

The model is an exponential model that describes the relationship between the number of weeks since the first data point and the closing price of the stock. It is calculated by using the given data points and finding the best fit for the data. The model can be used to make predictions about the closing prices of the stock by plugging in the value of x, which represents the number of weeks since the first data point.

One week from today, the stock is predicted to close at $67,034.37, one month from today, the stock is predicted to close at $66,327.75, and one year from today, the stock is predicted to close at $76,584.35 according to the model.

Question 2

It is difficult to say whether the model is a good predictor of the future closing price of the stock because it depends on how accurately the model describes the underlying relationship between x and y. However, since the model is based on historical data and has a high R-squared value, it could be a good predictor of the future closing price of the stock.