I have these data, representing a time series of the sales of a product:
1485, 1068, 1368, 1236, 1926, 1550, 2249, 800, 1712, 1734, 1348, 1875
The skewness of the data is -0,0512 (Excel) so I think it could be evaluated to see if the data are normally distributed. Then I made a frequency table for the data like this:
salesrange frequency 750 - 1000- 1 1000 - 1250- 2 1250 - 1500- 3 1500 - 1750- 3 1750 - 2000- 2 2000 - 2250- 1
So the data are approximated by bin width of 250. Plotting the frequency data I got this:
From this graph, I would say that the time series can be approximated by a normal distribution because the frequencies of their values (sales) are normally distributed (Gaussian shape).
Does this approach (check skewness and plot frequency table) make sense to say that the time series is approximately normally distributed? I know there are some normality tests but I neither know how to use them, nor am I able to because they are not part of my class program, so I'd like to know if this analysis would be acceptable.