This essay focuses on the prediction components of your equation. the summary statistics (mean, median, standard deviation, skewness, kurtosis, minimum, maximum,
on a excel spreadsheet and need an explanation to help me learn.
Use the following information and perform the following analysis;
1.firstly, In a seperate excel sheet: Report the summary statistics (mean, median, standard deviation, skewness, kurtosis, minimum, maximum, 25th percentile, 75th percentile) for AAPL daily holding perdiod return from February 1, 2017 to December 29, 2017; [Hint: Express return in percentage with 2 decimals, round skewness and kurtosis to 2 decimals] Comment on the summary statistics.
up with a prediction equation like this is only a useful exercise. If the independent variables in your dataset have some correlation with your dependent variable. So in addition to the prediction components of your equation. The coefficients on your independent variables (betas) and the constant. The (alpha)–you need some measure to tell you how strongly each independent variable. It is associated with your dependent variable.
When running your regression, you are trying to discover whether the coefficients on your independent variables are really different from 0 (so the independent variables are having a genuine effect on your dependent variable) or if alternatively any apparent differences from 0 are just due to random chance. The null (default) hypothesis is always that each independent variable is having absolutely no effect (has a coefficient of 0) and you are looking for a reason to reject this theory.
2.secondly, In a seperate excel sheet: Estimate the CAPM relationship using the data given, and report the regression output.
3. Thirdly, In the regression output sheet: What is alpha and beta values from the regression output; Interpret the financial meaning of the alpha and beta you get; [Hint: Interpret beta from a risk perspective] Comment on the statistical significance of the intercept and the independent variable; What is the Adjusted R Square value in this regression and comment on the magnitude.