Assignment #5

# Assignment#5 - StatTools Assignment#5 This assignment has only one part Part I See the class web site for more specific directions INTERPRET and

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StatTools Assignment #5 – This assignment has only one part. Part I. See the class web site for more specific directions. INTERPRET and COMMUNICATE. Use StatTools to generate the required analyses to answer the questions below. Note – these are the same questions as are listed in the Moore and McCabe book, aside from the addition of part (f). a. Regress the MO returns on the market returns (i.e. – use StatTools to generate a regression analysis using the market returns to predict the MO returns). Make a scatterplot and draw the least-squares line on the plot. Explain carefully what the slope and intercept of the line mean, in terms understandable to an investor. Also, give a measure of the strength of the relationship and explain its meaning to an investor. Multiple R-Square Adjusted StErr of Summary R R-Square Estimate 0.5251 0.2757 0.2668 6.468314267 Degrees of Sum of Mean of  F-Ratio p-Value ANOVA Table Freedom Squares Squares Explained 1 1290.114718 1290.114718 30.8352 < 0.0001 Unexplained 81 3388.966246 41.83908946 Coefficient Standard t-Value p-Value Confidence Interval 95% Regression Table Error Lower Upper Constant 0.35368491 0.761229192 0.4646 0.6434 -1.160922403 1.868292223 1.169539302 0.210616198 5.5529 < 0.0001 0.750479134 1.58859947 y = 1.1695x + 0.3537 R 2 = 0.2757 -30 -20 -10 0 10 20 30 -10 -5 0 5 10 15 MO / Data Set #1 The slope of 1.1695 means that as the returns on the Standard & Poor’s 500-stock index increases by one percent the monthly returns on the common stock of Philip Morris will increase by 1.1695 percent. The intercept means that if the returns on the Standard &

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Poor’s 500-stock index is 0 percent, the monthly returns on the common stock of Philip Morris will be 0.3537 percent. The coefficient of determination is R 2 which is 0.2757. The coefficient of determination is the proportion of the variation in the response variable (y) that can be accounted for (predicted, explained) by the predictor variable (x). This model is not very strong because the strength of the model increases as R 2 increases from 0 to 1 and 0.2757 is close to 0.
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## This note was uploaded on 02/16/2010 for the course BUS MGT 330 taught by Professor Schroeder during the Winter '08 term at Ohio State.

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Assignment#5 - StatTools Assignment#5 This assignment has only one part Part I See the class web site for more specific directions INTERPRET and

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