It would be helpful to me if the formulas for variance, sum of squares, standard deviation, and standard errors could be put into words. I would also appreciate clarification as to what each symbol in the formula means.
Please see the text-based class handout on Central Tendency, Variability, and Graphs. Both (a) the explanation of the symbols used in the formulae and (b) the written description of each term are located in that handout. Be sure you are looking in the text-based handout, not the more abbreviated slide handout.
You will find the explanation for the symbols under Mean (note that the symbols used are the same across all formulae).
Each measure of dispersion (a/k/a variance) has a description of what it is in the paragraph describing these measures of variance.
You will want to start with sums of squares and work through that description carefully because each measure of variance builds on the preceding ones. That is, understanding that the variance is the sums of squares divided by the sample size minus one does you little good if you do not first understand what sums of squares are.
If the description in words provided in the handout is not helping you much, I would strongly recommend that you create a very small data set (N = 5, say), and use it to work through each of the examples of central tendency and variance, both by hand calculations and in SPSS.
1 comment:
Please see the text-based class handout on Central Tendency, Variability, and Graphs. Both (a) the explanation of the symbols used in the formulae and (b) the written description of each term are located in that handout. Be sure you are looking in the text-based handout, not the more abbreviated slide handout.
You will find the explanation for the symbols under Mean (note that the symbols used are the same across all formulae).
Each measure of dispersion (a/k/a variance) has a description of what it is in the paragraph describing these measures of variance.
You will want to start with sums of squares and work through that description carefully because each measure of variance builds on the preceding ones. That is, understanding that the variance is the sums of squares divided by the sample size minus one does you little good if you do not first understand what sums of squares are.
If the description in words provided in the handout is not helping you much, I would strongly recommend that you create a very small data set (N = 5, say), and use it to work through each of the examples of central tendency and variance, both by hand calculations and in SPSS.
Post a Comment