Use the explore command in SPSS and explain whether the trading volume of the stock is normally distributed. Make sure to discuss, Skewness, kurtosis, results from the test of normality as well as the Q-Q plots.
Select a random Sample of exactly 125 observations. Then run the descriptive command and calculate the mean and standard deviation of the sample. Repeat this process (i.e., selection of a random sample and descriptive command) exactly 50 times. Hint: Use SPSS syntax to repeat the command. List both values (mean and the standard deviation) in a new excel file with proper column headings.
Upload the newly created excel file into SPSS and create a histogram of both the calculated means and standard deviations.
Run the explore command similar to what you did in step 1 for both variables and make your observations. Does the Central Limit Theorem (CLT) apply to both measurements?
Suppose you believe that the true average daily trade volume for General Electric stock is 49,829,719 shares. Based on a recent sample you have also calculated a standard deviation of 21,059,637 shares. Considering a 95% confidence level, what is the minimum required sample size if you like your sampling error to be limited to 10,000,000 shares. What sample size would offer a sampling error of not more than 20,000,000 shares? Assuming N=2013 represents the total population size, how will your calculations change for the finite sample?
Is there a statistically significant difference between the average trading volume in 2017 and 2018? Hint: While technically, this can be carried out as a paired sample t-test since volume data are reported for the same stock, we will treat this as independent samples. Complete your calculations by hand assuming M2017=46108055, S2017=34099055, n2017=251, M2018= 87241844, S2018=50977722, n2018=238.
Repeat the test, this time by using SPSS. Hint: Create a new grouping variable for 2017 and 2018 and use it to run your test.
Sample Solution
