WEBVTT 00:00:06.900 --> 00:00:09.020 So this is part two of lesson two. 00:00:09.020 --> 00:00:13.260 And for this lesson or for this presentation, 00:00:13.260 --> 00:00:16.970 we're going to learn about the mean or the average, 00:00:16.970 --> 00:00:19.860 and so a practice using descriptive statistics 00:00:19.860 --> 00:00:21.710 to analyze our data. 00:00:21.710 --> 00:00:24.110 And finally, we're just going to create a table 00:00:24.110 --> 00:00:28.253 and line graph in Excel using the data from the software. 00:00:29.450 --> 00:00:33.000 Now that bad data has been identified and eliminated, 00:00:33.000 --> 00:00:35.650 we can learn how to compare more than one genotype 00:00:35.650 --> 00:00:37.560 for all three repetitions. 00:00:37.560 --> 00:00:39.980 So our goal is to create a line graph, 00:00:39.980 --> 00:00:43.840 but instead of just looking at one genotype 00:00:43.840 --> 00:00:46.320 and how it did at all three repetitions, 00:00:46.320 --> 00:00:50.450 we wanna look at a line graph with three different genotypes 00:00:50.450 --> 00:00:55.310 and how they did throughout the 2015 year. 00:00:55.310 --> 00:00:58.190 So if I want to compare this genotype 00:00:58.190 --> 00:00:59.957 to two other genotypes, 00:00:59.957 --> 00:01:03.210 and this genotype meaning Ne-9-3565. 00:01:03.210 --> 00:01:05.710 If I wanna compare him to two others, 00:01:05.710 --> 00:01:09.340 I need to start by finding the mean, or average score, 00:01:09.340 --> 00:01:11.643 at each date of this genotype. 00:01:14.270 --> 00:01:16.360 So the mean is the average number found 00:01:16.360 --> 00:01:19.410 by adding all the data points and dividing by the number 00:01:19.410 --> 00:01:20.800 of data points. 00:01:20.800 --> 00:01:23.420 So if we look at the percent greenness scores 00:01:23.420 --> 00:01:27.080 for all three repetitions for Ne-9-3565, 00:01:27.080 --> 00:01:30.490 let's focus on May 11th and figure out how we can find 00:01:30.490 --> 00:01:32.710 the mean or the average. 00:01:32.710 --> 00:01:36.680 So our first step is going to be to add each -- all three 00:01:36.680 --> 00:01:38.340 of the scores together. 00:01:38.340 --> 00:01:40.270 And so then we'll get that sum. 00:01:40.270 --> 00:01:42.810 And then we're gonna divide that by three 00:01:42.810 --> 00:01:45.260 because there are three repetitions. 00:01:45.260 --> 00:01:47.990 And then we will have our mean. 00:01:47.990 --> 00:01:51.120 And so we need to do that for each date 00:01:51.120 --> 00:01:55.320 to find our mean score, our average score for each date. 00:01:55.320 --> 00:02:01.140 And once we do that, we can now make a line graph 00:02:01.140 --> 00:02:06.140 and we can represent any 9-3565 00:02:06.290 --> 00:02:08.763 with one value at each date. 00:02:11.140 --> 00:02:13.530 So now that the average has been calculated 00:02:13.530 --> 00:02:15.700 for one of our genotypes, 00:02:15.700 --> 00:02:18.410 we can go ahead and calculate the average 00:02:18.410 --> 00:02:22.030 for the other two genotypes I would like to compare it to. 00:02:22.030 --> 00:02:23.860 So in this nice table, 00:02:23.860 --> 00:02:28.380 I have, you can see, the dates for 2015 00:02:28.380 --> 00:02:30.700 that greenness scores were given, 00:02:30.700 --> 00:02:33.340 and this would be the average scores 00:02:33.340 --> 00:02:36.360 for all three of my genotypes. 00:02:36.360 --> 00:02:38.160 And so once I create that table, 00:02:38.160 --> 00:02:41.840 I can create a nice line graph in Excel. 00:02:41.840 --> 00:02:45.690 And this line graph is a nice and organized and easy way 00:02:45.690 --> 00:02:48.200 for me to compare these three genotypes. 00:02:48.200 --> 00:02:52.070 I can easily tell how fast they greened up in the spring 00:02:52.070 --> 00:02:55.830 and how long they stayed green into the fall. 00:02:55.830 --> 00:03:00.360 And by looking at this, I can see that Ne-66-36, 00:03:00.360 --> 00:03:02.240 which is the orange line, 00:03:02.240 --> 00:03:05.120 greened up the fastest in the spring. 00:03:05.120 --> 00:03:07.160 And I can see that it stayed green 00:03:07.160 --> 00:03:12.160 just as long as Ne-9-3565 into the fall. 00:03:14.850 --> 00:03:17.130 So you're going to be given an assignment 00:03:17.130 --> 00:03:19.120 and you're going to have to look at data 00:03:19.120 --> 00:03:21.030 for three genotypes. 00:03:21.030 --> 00:03:24.030 But before you do, I just wanna point out 00:03:24.030 --> 00:03:26.410 that the data you've looked at previously, 00:03:26.410 --> 00:03:29.220 I rounded to the 10th decimal place. 00:03:29.220 --> 00:03:32.270 And so that's one thing you're going to have to do 00:03:32.270 --> 00:03:34.830 for the data you will have. 00:03:34.830 --> 00:03:37.550 And I also want to point out 00:03:37.550 --> 00:03:42.550 that if you see percentages over 100%, it's not bad data. 00:03:42.840 --> 00:03:46.280 This has to do with how the program calculated the plot. 00:03:46.280 --> 00:03:51.280 So for this, we will just keep the percentages over 100%. 00:03:54.040 --> 00:03:56.700 For your assignment, you are going to use Excel 00:03:56.700 --> 00:04:00.680 to graph the mean of three different buffalograss varieties 00:04:00.680 --> 00:04:04.390 for 2014 and 2015. 00:04:04.390 --> 00:04:07.510 So your final product will look like this. 00:04:07.510 --> 00:04:09.630 You'll have two line graphs 00:04:09.630 --> 00:04:12.500 and you'll have the buffalograss percent greenness scores 00:04:12.500 --> 00:04:15.750 in 2014 for all three genotypes 00:04:15.750 --> 00:04:19.970 and the buffalograss percent greenness scores in 2015 00:04:19.970 --> 00:04:22.560 for all three genotypes. 00:04:22.560 --> 00:04:26.220 And so you're going to be given actual data. 00:04:26.220 --> 00:04:31.220 And so this is what you are going to be looking at. 00:04:31.280 --> 00:04:33.680 And so when you're looking at this, 00:04:33.680 --> 00:04:36.490 you'll see we have ID here. 00:04:36.490 --> 00:04:39.120 So those are your three genotypes. 00:04:39.120 --> 00:04:41.198 So 11-3608, 00:04:41.198 --> 00:04:42.407 53-18, 00:04:42.407 --> 00:04:44.930 and 11-3622. 00:04:44.930 --> 00:04:47.140 And you have all three repetitions 00:04:47.140 --> 00:04:49.670 and their greenness scores. 00:04:49.670 --> 00:04:55.030 What may be confusing is that 2014 data is not all together, 00:04:55.360 --> 00:04:57.790 so you're really gonna have to pay attention. 00:04:57.790 --> 00:05:00.990 Here's some of the scores for 2014 00:05:00.990 --> 00:05:03.800 and then the other scores are over here. 00:05:03.800 --> 00:05:05.900 So that's what you're gonna have to do first 00:05:05.900 --> 00:05:08.563 is organize that data. 00:05:10.600 --> 00:05:12.550 And then the next thing you should do 00:05:12.550 --> 00:05:16.750 once you organize your data, you want to calculate the mean. 00:05:16.750 --> 00:05:20.170 So you can organize it and you can see here, 00:05:20.170 --> 00:05:24.750 I have one of my genotypes and I have their data, 00:05:24.750 --> 00:05:25.950 so I've organized it. 00:05:25.950 --> 00:05:28.560 And this is just for 2015. 00:05:28.560 --> 00:05:31.820 So one table for all the dates of 2015. 00:05:31.820 --> 00:05:33.930 And then I calculated their mean. 00:05:33.930 --> 00:05:37.680 Once I calculate the mean for all three of my genotypes, 00:05:37.680 --> 00:05:41.450 I can condense that down to an even smaller table 00:05:41.450 --> 00:05:45.440 and then I can round my mean to the 10th decimal place. 00:05:45.440 --> 00:05:47.660 And then once I do that, 00:05:47.660 --> 00:05:51.083 I can then begin to create my line graphs. 00:05:52.020 --> 00:05:57.020 Once you create line graphs for both 2014 and 2015, 00:05:58.440 --> 00:06:01.970 make sure that you add a caption for them. 00:06:01.970 --> 00:06:05.130 So graphs -- bar graphs, line graphs -- 00:06:05.130 --> 00:06:07.650 we're always gonna refer to them as figures. 00:06:07.650 --> 00:06:10.830 So figure one here is the percent greenness 00:06:10.830 --> 00:06:14.910 for three buffalograss genotypes in 2014. 00:06:14.910 --> 00:06:18.510 The scores are the averages of three repetitions. 00:06:18.510 --> 00:06:20.240 And so if I have two graphs, 00:06:20.240 --> 00:06:23.470 I'm going to have figure one and figure two. 00:06:23.470 --> 00:06:25.370 And so once you do that, 00:06:25.370 --> 00:06:28.260 then you are going to rank the genotypes 00:06:28.260 --> 00:06:30.390 based on their greenness. 00:06:30.390 --> 00:06:35.390 So which variety did best in 2014 by looking at this graph? 00:06:35.450 --> 00:06:37.110 Which one do you think? 00:06:37.110 --> 00:06:42.110 And the same for 2015, which did best in 2015? 00:06:42.250 --> 00:06:44.460 And which variety did best overall? 00:06:44.460 --> 00:06:46.860 Did best in 2014? 00:06:46.860 --> 00:06:48.573 In 2015? 00:06:49.860 --> 00:06:52.920 And so when you create these graphs, 00:06:52.920 --> 00:06:56.220 always make sure to omit any bad data 00:06:56.220 --> 00:06:58.880 and the bad data that we talked about... 00:06:58.880 --> 00:07:01.223 Let me find that slide. 00:07:03.700 --> 00:07:05.730 And so the bad data we talked about 00:07:05.730 --> 00:07:08.210 would be due to the quality of the photo, 00:07:08.210 --> 00:07:12.000 which the only date you will come across with bad quality 00:07:12.000 --> 00:07:16.010 is for June 8th in 2015. 00:07:16.010 --> 00:07:19.600 And then also make sure you take out any bad data 00:07:19.600 --> 00:07:23.100 due to weed and bluegrass infestations, 00:07:23.100 --> 00:07:26.943 which will be seen in the spring and in the fall. 00:07:27.830 --> 00:07:30.750 And so to help you make your line graphs, 00:07:30.750 --> 00:07:33.970 I do have links to two presentations 00:07:33.970 --> 00:07:38.530 on how to calculate the mean and how to create a line graph 00:07:38.530 --> 00:07:39.403 in Excel. 00:07:41.420 --> 00:07:46.660 So which three genotypes are you going to graph and rank 00:07:46.660 --> 00:07:47.880 in Excel? 00:07:47.880 --> 00:07:49.890 Well, there are two options for you. 00:07:49.890 --> 00:07:52.960 So there's option one and there's no difference 00:07:52.960 --> 00:07:55.070 between option one or option two, 00:07:55.070 --> 00:07:57.730 just simply different genotypes. 00:07:57.730 --> 00:08:00.840 And so, say, if you choose option two, 00:08:00.840 --> 00:08:04.040 you will look here and these are all in Excel files 00:08:04.040 --> 00:08:05.440 for you already. 00:08:05.440 --> 00:08:09.310 So you have your three genotypes, your three repetitions, 00:08:09.310 --> 00:08:12.400 and the percent greenness for all of the dates. 00:08:12.400 --> 00:08:17.050 And then for you to kind of visualize where they are 00:08:17.050 --> 00:08:22.050 on the experimental area, I have them plotted here for you.