1. Can you find an error in equation 1 2. Look at Figure 1, how well does the regression line predict the actual results? What does the R2 in the regression equation (table1) mean? Does this give you...

Business Case Studies:
1. Yes, the equation is wrong. The co
ect equation is given below.
2. From the figure, one can see that the points are quite scattered around the line and the line doesn’t fit the data points quite closely. Hence in this particular case, the regression model doesn’t predict the data quite well. However, that should not imply that the regression model is a bad one as the significant F-statistic reveals that the model fits the overall data quite well.
The R-squared of the regression model comes out to be 0.2433 which means that 24.33% of the variation in the dependent variable, the return of Coca-Cola can be explained by the independent variable S&P500 return. There is no relation with the R-squared and the beta coefficient. There might be low R-squared for a model with significant betas which is the case here and some important conclusions can be drawn from the same model, described in the next section.
3. Frequent return data ensures that there is no occu
ence of structural change in that particular estimation window. Monthly data involves lesser observations (60 data points for 5 years data) which have happened in this particular case. Whereas if weekly data were taken, it could provide 260 data points which would capture much more structural change. Though one should keep in mind that the window period shouldn’t be much less. Because in the case of daily data one could get a large number of data points but at the same time this would require liquid underlying shares for the accurate estimation of the betas. On the other hand, the time horizon shouldn’t be as big as 10 years also. Because in that case, some potential shifts in the economic structures and that could affect the beta estimation. The asset risk relative to the market might face some instability which would affect the estimation again. In the case of the 5-year window, the bets could be shown to be less stable. Hence the betas face a distinct shift over time. One year data on the other hand are highly sensitive to some movement and relatively e
atic also. The analysis or the estimation depends on the underlying industries also. For example in telecom industries, one year weekly data is much more reliable than the 5-year data as that could involve the risk of involving unimportant data with respect to the future. The main disadvantage of the daily returns involves measurement e
ors compared to the weekly betas. There might be some additional sampling e
ors for monthly betas of 5-years compared to the weekly or daily data as the estimates could be less stable.
4. The slope for the regression line or the beta is 0.765 with a p-value of 0.0000 implying that one unit change in the market index return co
esponds to the increase of 0.765 units in the return for the Coca-Cola stock market.
5. The cost of equity determined in this case study is found to be 10.77% and the recent stock price of the Coca-Cola company is 53.49 USD. The average annual sale growth of Coca-Cola is -7.43% and from the formula, the return on equity for Coca-Cola is 49.6%. Hence the values have a little bit different and it should be as the estimation...