I\'ve completed a regression analysis on excel using two variables one being mon
ID: 3314432 • Letter: I
Question
I've completed a regression analysis on excel using two variables one being monthly stock returns and the other being market returns. how expand on the relationship between these two variables using the information generated in the excel regression function while sticking to a maximum word limit of 500 words. It can't include any basic statistics. For reference sake the information is below
Regression Statistics Multiple R 0.727995336 R Square 0.529977209 Adjusted R Square 0.529221546 Standard Error 5.799766521 Observations 624 ANOVA df SS MS F Significance F Regression 1 23591.18105 23591.18105 701.3400859 4.6893E-104 Residual 622 20922.39544 33.6372917 Total 623 44513.57649 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept -0.235491695 0.233680108 -1.007752419 0.313965199 -0.694389241 0.223405851 -0.694389241 0.223405851 X Variable 1 1.384598004 0.052282864 26.48282625 4.6893E-104 1.281925688 1.48727032 1.281925688 1.48727032Explanation / Answer
The multiple correlation coefficient is 0.727995336 this indicates that the correlation among the independent and dependent variable is positive this means as one variable increases then another varible may also increases.
The coefficient of determination R2 is 0.529977209 i.e. 52.99 % this means that is close to 53% of the variation in the dependent variable is explained by the independent variable.
The adjusted R- square a measure of explainatory power is 0.529221546 this statistic is not generally interpritated because it is neither a percentage nor a test significance.
The Standard error of the regression is 5.799766521 which is an estimate of the varation.
The analysis of variance information provide the breakdown of the total varition of the dependent variable in to explained and unexplained portions.
The F statistic is calculated using the ratio of the mean square regression to the mean square residule this statistic can then compared with critical F value to test the significance of theslope of the regression coefficient.
The P- value associated with calculated F statistic is the probability beyond the calculated value which is compared with level of significance( 5%) indictes rejection of the null hypothesis stated about slope parameter.(Ho : beta = 0)
The result of the estimated regression line include the estimated coefficients , the standard error of the coefficients,the calculated t statistics and corresponding p value and lower and upper bounds of the coefficients.
The independent variable is statistically significant if the p-value is less than the significance level (alpha = 5%).
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