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    Beta Value & Regression Analysis

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    I need help with problems 13.49 and 13.79. They are attached here. Thank you in advance!!

    13.49 The volatility of a stock is often measured by its
    beta value. You can estimate the beta value of a stock by
    developing a simple linear regression model, using the per-
    centage weekly change in the stock as the dependent vari-
    able and the percentage weekly change in a market index as
    the independent variable. The S&P 500 Index is a common
    index to use. For example, if you wanted to estimate the beta
    for Disney, you could use the following model, which is
    sometimes referred to as a market model: (%weeklychangeinDisney) = b0 + b11%weeklychangeinS&P500index2 + e
    The least-squares regression estimate of the slope is the
    estimate of the beta value for Disney. A stock with a beta
    value of 1.0 tends to move the same as the overall market. A
    stock with a beta value of 1.5 tends to move 50% more than
    the overall market, and a stock with a beta value of 0.6 tends
    to move only 60% as much as the overall market. Stocks
    with negative beta values tend to move in a direction oppo-
    site that of the overall market. The following table gives
    some beta values for some widely held stocks, using a year's
    worth of data ending in May, 2009. Note that in the first 10
    months of this time frame the S&P 500 lost approximately
    40% of its value and then rebounded by about 10% in the last two months

    Company Ticker Symbol Beta
    Procter & Gamble PG 0.54
    AT&T T 0.73
    Disney DIS 1.10
    Apple AAPL 1.52
    eBay EBAY 1.69
    Ford F 2.86
    Source: Data extracted from finance.yahoo.com, May 27, 2009.
    a. For each of the six companies, interpret the beta value.
    b. How can investors use the beta value as a guide for

    13.79 An accountant for a large department store would
    like to develop a model to predict the amount of time it takes
    to process invoices. Data are collected from the past
    32 working days, and the number of invoices processed and
    completion time (in hours) are stored in .
    (Hint: First, determine which are the independent and
    dependent variables.)
    a. Assuming a linear relationship, use the least-squares
    method to compute the regression coefficients
    b. Interpret the meaning of the Y intercept, and the slope,
    in this problem.
    c. Use the prediction line developed in (a) to predict the
    amount of time it would take to process 150 invoices.
    d. Determine the coefficient of determination, and inter-
    pret its meaning.
    e. Plot the residuals against the number of invoices
    processed and also against time.
    f. Based on the plots in (e), does the model seem appropriate?
    g. Based on the results in (e) and (f), what conclusions
    can you make about the validity of the prediction made

    Invoices Time
    103 1.5
    173 2
    149 2.1
    193 2.5
    169 2.5
    29 0.5
    188 2.3
    19 0.3
    201 2.7
    58 1
    110 1.5
    83 1.2
    60 0.8
    25 0.4
    60 1.8
    190 2.9
    233 3.4
    289 4.1
    45 1.2
    70 1.8
    241 3.8
    163 2.8
    120 2.5
    201 3.3
    135 2
    80 1.7
    77 1.7
    222 3.1
    181 2.8
    30 1
    61 1.9
    120 2.6

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    Solution Summary

    The solution provides step by step method for the calculation of regression analysis. Formula for the calculation and Interpretations of the results are also included. Attached in Excel and Word.