Reference no: EM13245554
To manage an investment portfolio, suppose now you are required to analyze and predict prices of in-trading stocks of Apple Inc. You are given last 10 years' monthly stock closing prices of the company, listed in the attached Minitab dataset "Apple Stock Price.MTW", with variable name of AAPL.
Also based on experts' opinions, the following nine potential independent variables may have influences on Apple stock prices:
MSFT: monthly closing price of stock of Microsoft Corp.
HP: monthly closing price of stock issued by Hewlett & Packard
Dell: monthly closing price of Dell Inc. stock
Sony: monthly closing price of stock of Sony Corp.
IBM: monthly closing price of stock of International Business Machines Corp.
SP500: monthly closing index of S&P 500
CSCO: monthly closing price of stock of Cisco Systems, Inc.
INTC: monthly closing price of stock of Intel Corp.
TXN: monthly closing price of stock of Texas Instruments Inc.
Monthly closing prices of all nine variables are listed in the same Minitab dataset. Now use the data given,
(a). Obtain a scatter plot matrix of all variables included in the dataset.
(b). Use stepwise regression model, find the most appropriate regression model (here must be a multiple linear regression model based on the last step from stepwise regression algorithm). Write down the correct model estimate.
[Note: The model estimate should be in correct Time Series model format, and you should provide correct Minitab outputs
(c). What is the R2 of the regression model in (b)? Interpret its meaning.
(d). Analyze the residuals of the regression model identified in (b), using formal hypothesis test, check the adequacy of your regression model.
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