WEBVTT 00:00:00.000 --> 00:00:03.479 align:middle line:90% [MUSIC PLAYING] 00:00:03.479 --> 00:00:15.930 align:middle line:90% 00:00:15.930 --> 00:00:17.820 align:middle line:84% Hello, and welcome to a quick tutorial 00:00:17.820 --> 00:00:21.390 align:middle line:84% on how to calculate descriptive statistics in R-Commander Let's 00:00:21.390 --> 00:00:22.840 align:middle line:90% go ahead and do this. 00:00:22.840 --> 00:00:24.940 align:middle line:84% So we're going to start by going into R-console. 00:00:24.940 --> 00:00:28.320 align:middle line:84% We're going to type in that Library, R-Commander. 00:00:28.320 --> 00:00:30.450 align:middle line:84% Make sure you capitalize the letter R 00:00:30.450 --> 00:00:32.886 align:middle line:90% or it's not going to run. 00:00:32.886 --> 00:00:34.950 align:middle line:84% And once R-Commander is loaded, we're 00:00:34.950 --> 00:00:36.690 align:middle line:84% going to go ahead and open up a data set. 00:00:36.690 --> 00:00:40.230 align:middle line:84% So we're going to Open, Import Data from SPSS file. 00:00:40.230 --> 00:00:43.920 align:middle line:84% I'm going to open up the Textbook Dataset Shortened 00:00:43.920 --> 00:00:46.010 align:middle line:84% because what we're going to do in this example is 00:00:46.010 --> 00:00:49.690 align:middle line:84% we're going to go ahead and look at the PRCA 24. 00:00:49.690 --> 00:00:51.900 align:middle line:84% So I'm going to make this a little bit larger so we 00:00:51.900 --> 00:00:53.775 align:middle line:84% can look at it, which is this one right here. 00:00:53.775 --> 00:00:56.080 align:middle line:84% It's called Big CA in the data set. 00:00:56.080 --> 00:01:00.180 align:middle line:84% So this is where we've taken all 24 individual items on the PRCA 00:01:00.180 --> 00:01:02.520 align:middle line:90% 24 and added them together. 00:01:02.520 --> 00:01:05.430 align:middle line:84% So we can tell from this that the very first person 00:01:05.430 --> 00:01:11.590 align:middle line:84% in our dataset had a score of 61 on the PRCA 24. 00:01:11.590 --> 00:01:13.290 align:middle line:84% Whereas we have number six down here, 00:01:13.290 --> 00:01:17.220 align:middle line:84% that person maxed out their score on the PRCA 24. 00:01:17.220 --> 00:01:19.980 align:middle line:84% So that's how you can understand what it 00:01:19.980 --> 00:01:21.280 align:middle line:90% is that we're to be looking at. 00:01:21.280 --> 00:01:22.971 align:middle line:90% So let's go ahead and do this. 00:01:22.971 --> 00:01:24.220 align:middle line:90% So I'm going to X out of that. 00:01:24.220 --> 00:01:27.620 align:middle line:84% I'm going to come up here to Statistics, Summaries, 00:01:27.620 --> 00:01:29.951 align:middle line:84% and what we want to look at is Numerical Summaries. 00:01:29.951 --> 00:01:32.325 align:middle line:84% So what I'm going to do is I'm going to find that Big CA, 00:01:32.325 --> 00:01:33.575 align:middle line:90% and I'm going to highlight it. 00:01:33.575 --> 00:01:35.699 align:middle line:84% And then I'm going to come over here to Statistics. 00:01:35.699 --> 00:01:37.800 align:middle line:84% And it has a bunch of different options for me-- 00:01:37.800 --> 00:01:39.630 align:middle line:84% your mean, your standard deviations, 00:01:39.630 --> 00:01:40.800 align:middle line:90% your inner quartile range. 00:01:40.800 --> 00:01:42.630 align:middle line:84% We really aren't, for our purposes, 00:01:42.630 --> 00:01:44.460 align:middle line:84% interested in either of those, but we 00:01:44.460 --> 00:01:48.457 align:middle line:84% are going to go ahead and add skewness and Kurtosis for us. 00:01:48.457 --> 00:01:50.790 align:middle line:84% So we're going to have the mean, the standard deviation, 00:01:50.790 --> 00:01:52.350 align:middle line:90% the skewness, and Kurtosis. 00:01:52.350 --> 00:01:54.700 align:middle line:90% And then we can click OK. 00:01:54.700 --> 00:01:59.060 align:middle line:84% Now you can see here, the mean was a 63.66. 00:01:59.060 --> 00:02:02.610 align:middle line:84% The standard deviation was 17.00. 00:02:02.610 --> 00:02:07.350 align:middle line:84% Skewness is 0.20, so that's positive, which means, again, 00:02:07.350 --> 00:02:11.039 align:middle line:84% that it's going to be positively skewed or skewed off-- 00:02:11.039 --> 00:02:14.280 align:middle line:84% we're going to have fewer items on the right tail. 00:02:14.280 --> 00:02:16.880 align:middle line:84% And we have Kurtosis here, and that's positive, 00:02:16.880 --> 00:02:18.540 align:middle line:90% so it's going to be more peaked. 00:02:18.540 --> 00:02:21.630 align:middle line:84% So then we have, here we have the end, 00:02:21.630 --> 00:02:26.460 align:middle line:84% which was 644, which means we have 644 people that 00:02:26.460 --> 00:02:28.230 align:middle line:90% completed the PRCA 24. 00:02:28.230 --> 00:02:31.610 align:middle line:84% And then there's this NA right here, which is a 10 as well, 00:02:31.610 --> 00:02:39.510 align:middle line:84% and that lets us know that out of the entire data set, 00:02:39.510 --> 00:02:43.890 align:middle line:84% 10 people did not, which the entire dataset is 654, 00:02:43.890 --> 00:02:46.904 align:middle line:84% whereas we lost 10 of those who did not have complete data. 00:02:46.904 --> 00:02:48.570 align:middle line:84% And what that means is that at least one 00:02:48.570 --> 00:02:52.380 align:middle line:84% of the items when they completed the PRCA 24 was not filled, 00:02:52.380 --> 00:02:55.920 align:middle line:84% and as such, that missing data just does not get computed. 00:02:55.920 --> 00:03:00.000 align:middle line:84% So that is all it takes to run simple descriptive statistics 00:03:00.000 --> 00:03:01.860 align:middle line:90% using R-Commander. 00:03:01.860 --> 00:03:04.910 align:middle line:90% [MUSIC PLAYING] 00:03:04.910 --> 00:03:16.091 align:middle line:90%