1 00:00:00,480 --> 00:00:06,160 ready to roll. Happy Wednesday, everybody. I know that we're all 2 00:00:06,160 --> 00:00:13,560 excited by Excel, and where Laurie and I are gonna just give 3 00:00:13,560 --> 00:00:17,760 you about a 20 to 25 minute overview of some things that we 4 00:00:17,760 --> 00:00:23,400 like to do with Excel, with no further delay, I'm going to go 5 00:00:23,400 --> 00:00:28,880 ahead and give it over to Laurie, with the appropriate 6 00:00:28,960 --> 00:00:36,400 introduction to cover a very exciting topic called Excel 7 00:00:36,400 --> 00:00:37,120 charts. 8 00:00:38,920 --> 00:00:45,640 All right. Get us all excited about Excel charts. So today I'm 9 00:00:45,640 --> 00:00:49,000 gonna, I'm going to talk about Excel charts, and then I'll hand 10 00:00:49,000 --> 00:00:52,480 it off to Teri to expand on charts and talk about pivot 11 00:00:52,480 --> 00:00:58,240 tables. But when I was thinking about this topic. And you know 12 00:00:58,240 --> 00:00:59,840 what, what we should cover. 13 00:01:01,080 --> 00:01:01,880 I thought, 14 00:01:01,920 --> 00:01:06,720 there are a million places where you can go to find out how to 15 00:01:06,720 --> 00:01:09,480 create an Excel chart you know it's really not that hard to 16 00:01:09,480 --> 00:01:14,200 create a simple Excel chart. But what is a little bit more 17 00:01:14,200 --> 00:01:18,240 difficult is to figure out which charts are right for which kind 18 00:01:18,240 --> 00:01:22,320 of data. So instead of going into nitty gritty about how to 19 00:01:22,320 --> 00:01:25,600 actually create those charts. I'm going to start by focusing 20 00:01:25,600 --> 00:01:26,880 on trying to figure 21 00:01:26,880 --> 00:01:29,160 out what kind of chart is right for your data. 22 00:01:33,880 --> 00:01:37,960 So, knowing what your end goal is will help you decide on the 23 00:01:37,960 --> 00:01:41,560 best way to visualize your data in a chart. These three 24 00:01:41,560 --> 00:01:44,560 questions should help you get started. What do you hope to 25 00:01:44,560 --> 00:01:48,400 convey. Are you trying to show trends are you pointing out an 26 00:01:48,400 --> 00:01:52,240 anomaly, are you communicating a distribution of money. Among the 27 00:01:52,560 --> 00:01:57,360 service areas, how much data and what kind of data are you 28 00:01:57,360 --> 00:02:00,800 working with. Can you cut it down and still convey your 29 00:02:00,800 --> 00:02:04,120 meaning confusing charts with lots of data can be difficult to 30 00:02:04,120 --> 00:02:08,440 understand and can actually work against you or against, you 31 00:02:08,440 --> 00:02:11,080 know, people trying being able to understand what it is you're 32 00:02:11,080 --> 00:02:17,600 trying to convey the type of data is important, because, as 33 00:02:17,600 --> 00:02:20,720 an example, you might it might not be advisable to try to 34 00:02:20,720 --> 00:02:24,760 compare both of dollar Vale values and numeric values, 35 00:02:25,440 --> 00:02:28,960 because it can be hard to visualize that that kind of data 36 00:02:28,960 --> 00:02:33,280 on one chart. Are you trying to compare data with wildly varying 37 00:02:33,280 --> 00:02:38,560 quantities. This can also be hard to visualize and then 38 00:02:38,560 --> 00:02:41,880 define your data elements, you might have a table with a ton of 39 00:02:41,880 --> 00:02:45,320 data in it, but not all of it is relevant for what you decided 40 00:02:45,320 --> 00:02:49,480 your goal was for this chart. So just spend a few times a few 41 00:02:49,480 --> 00:02:52,960 minutes thinking about these questions and then deciding on 42 00:02:52,960 --> 00:02:57,160 which chart would be best to help you meet your goals. 43 00:03:03,880 --> 00:03:05,720 There we go. Maybe. 44 00:03:07,160 --> 00:03:08,560 Nope, skipped one. 45 00:03:13,040 --> 00:03:13,600 There we go. 46 00:03:14,640 --> 00:03:18,200 So these are the most common types of charts and the reasons 47 00:03:18,200 --> 00:03:21,960 you might want to might want to choose one over another. These 48 00:03:21,960 --> 00:03:26,120 are not hard and fast rules, it's gonna vary depending on the 49 00:03:26,120 --> 00:03:31,720 data, but this is a, you know, just a basic guide. So, the four 50 00:03:31,720 --> 00:03:35,600 most common chart types that you'll see are pie charts line 51 00:03:35,600 --> 00:03:39,480 charts bar charts and column charts bar and column charts are 52 00:03:39,480 --> 00:03:43,720 often used interchangeably, but a bar chart, would be preferable 53 00:03:43,720 --> 00:03:46,840 if you're trying to show categories of data over a 54 00:03:46,840 --> 00:03:48,080 certain time period. 55 00:03:53,200 --> 00:03:59,040 So let's talk about line chart. First, these are often used to 56 00:03:59,040 --> 00:04:04,400 show changes over time. In this example that I have here. We 57 00:04:04,400 --> 00:04:09,200 have three columns of information and we have a date. 58 00:04:09,640 --> 00:04:13,680 Students savings and student enrollment. So if I wanted to 59 00:04:13,680 --> 00:04:17,520 show all three a line chart, just wouldn't work. But 60 00:04:17,520 --> 00:04:21,120 displaying just the date and one data point is a very effective 61 00:04:21,120 --> 00:04:24,440 use of a line chart. So looking at this chart can bring all 62 00:04:24,440 --> 00:04:28,280 kinds of questions to mind. What happened in the summer fall of 63 00:04:28,280 --> 00:04:35,920 2017, that made the that the savings spike so much. What do 64 00:04:35,920 --> 00:04:39,480 did we have a goal, you know, for, for students savings. 65 00:04:40,040 --> 00:04:43,080 Something like this will probably show up in a report for 66 00:04:43,080 --> 00:04:46,800 Affordable Learning, and would have that context around the 67 00:04:46,800 --> 00:04:56,440 content in this chart. This is a poor use of a line chart. It 68 00:04:56,440 --> 00:05:00,040 makes the number of students look like it's zero and enter 69 00:05:00,040 --> 00:05:04,200 remained flat over the entire course of the AR program. And 70 00:05:04,200 --> 00:05:06,120 that's just because the proportion of students to 71 00:05:06,120 --> 00:05:11,240 savings is so far apart. So the number of students enrolled at 72 00:05:11,280 --> 00:05:15,520 one point was as high as, like 5000. So between one and 5000 73 00:05:15,520 --> 00:05:18,600 but that's not going to show up when you have data that is 74 00:05:18,840 --> 00:05:22,080 starts at, you know, the hundred thousand dollar range. You just 75 00:05:22,080 --> 00:05:29,240 can't show those two different data points on a line chart. Bar 76 00:05:29,240 --> 00:05:32,640 charts. These are often used to compare different categories of 77 00:05:32,640 --> 00:05:36,840 data. It's good for visualizing the proportion of items, when 78 00:05:36,840 --> 00:05:42,000 there are multiple categories. So in this example, we're 79 00:05:42,000 --> 00:05:45,000 comparing the number of students enrolled to the number of 80 00:05:45,000 --> 00:05:48,600 courses, using affordable educational resources for each 81 00:05:48,640 --> 00:05:52,760 of these time periods. So looking at this chart you can 82 00:05:52,760 --> 00:05:57,560 easily see that the summer, summer, fall 2018, and the 83 00:05:57,560 --> 00:06:03,000 summer, fall, 2017 had about the same number of courses, but many 84 00:06:03,000 --> 00:06:07,040 more students were enrolled in 2017. So having that 85 00:06:07,360 --> 00:06:11,160 visualization might drive you to look more closely at the data to 86 00:06:11,160 --> 00:06:14,400 identify those courses with higher enrollment numbers that 87 00:06:14,400 --> 00:06:17,360 are especially effective in reaching lots of students with 88 00:06:17,360 --> 00:06:25,400 these resources. And then pie charts. These are often used to 89 00:06:25,400 --> 00:06:28,640 illustrate the distribution of data over our total value. So 90 00:06:28,640 --> 00:06:34,280 the pie is the total. This chart tells us that there were a total 91 00:06:34,320 --> 00:06:38,280 of, there was a total of $9 million in students savings 92 00:06:38,680 --> 00:06:43,560 realized, and it shows how much impact each program had. So this 93 00:06:43,560 --> 00:06:47,440 is a very effective way of showing that you know total 94 00:06:47,480 --> 00:06:52,760 savings. So these are just a few examples of common charts that 95 00:06:52,760 --> 00:06:57,080 you're going to see, and why you might want to pick them, or 96 00:06:57,080 --> 00:07:01,560 maybe not pick them and some of the bad examples. Now we're 97 00:07:01,560 --> 00:07:04,240 going to delve into a couple of more complex 98 00:07:04,800 --> 00:07:06,240 types of charts. 99 00:07:08,160 --> 00:07:12,880 So, this is a, what we're calling a bell curve, but I'll 100 00:07:12,880 --> 00:07:16,080 start by saying this is not a true bell curve because the true 101 00:07:16,080 --> 00:07:19,880 bell curve is symmetrical. If you folded it in half, it would 102 00:07:19,880 --> 00:07:24,600 be equal on both sides. However, this is an effective way of 103 00:07:24,600 --> 00:07:29,680 showing the distribution of data. I made these charts to 104 00:07:29,680 --> 00:07:37,680 show survey results for the Zendesk task portal that we did 105 00:07:38,120 --> 00:07:42,080 recently in the past six months on this chart you can easily see 106 00:07:42,080 --> 00:07:47,160 that most of the scores lie between about 75 and 100. We've 107 00:07:47,160 --> 00:07:50,280 added a few helpful helpful pieces of information to show 108 00:07:50,320 --> 00:07:56,040 our goal, which is 80 to show the average score, which is 87. 109 00:07:56,680 --> 00:08:00,960 And then I also added the probability range of these 110 00:08:00,960 --> 00:08:07,160 scores. And by probability range. I mean, it's the range 111 00:08:07,160 --> 00:08:10,280 where the scores are most likely to fall. So I'll, I'll talk 112 00:08:10,280 --> 00:08:13,800 about the formulas in just a minute but the average score was 113 00:08:13,800 --> 00:08:20,840 87. And if we add 18, or the standard deviation was 18. So if 114 00:08:20,840 --> 00:08:25,760 we add and subtract 18 from 87, that gives us a range of 69 to 115 00:08:25,760 --> 00:08:31,920 105. So this visual tells us which scores shows us first you 116 00:08:31,920 --> 00:08:35,440 know which scores are are falling within that range, and 117 00:08:35,440 --> 00:08:38,240 then also which ones are falling outside of the 118 00:08:38,320 --> 00:08:39,440 normal range. 119 00:08:41,080 --> 00:08:44,440 And then I'll show you all those, those formulas in just a 120 00:08:44,440 --> 00:08:50,000 second. But as I was preparing this presentation, I started to 121 00:08:50,000 --> 00:08:50,560 ask. 122 00:08:50,960 --> 00:08:52,000 Like why, why does, 123 00:08:52,000 --> 00:08:52,520 Like why, why does, why do these formulas work. And what does all this data really 124 00:08:52,520 --> 00:08:55,840 why do these formulas work. And what does all this data really 125 00:08:55,840 --> 00:09:03,000 mean what and what are we trying to show here. So, as I 126 00:09:03,000 --> 00:09:06,120 mentioned, it does show the concentration of the scores, and 127 00:09:06,120 --> 00:09:08,840 that they fall within a certain range and that is really good 128 00:09:08,840 --> 00:09:12,640 information that you know we want to convey. But I thought 129 00:09:12,680 --> 00:09:17,080 wouldn't it be nice if we could actually show how many people 130 00:09:17,120 --> 00:09:22,360 scored. You know each of, if this could show how many people 131 00:09:22,560 --> 00:09:28,120 scored it at 100 as opposed to 40. It would be really nice if 132 00:09:28,120 --> 00:09:31,880 we can make those little data points. The size of those data 133 00:09:31,880 --> 00:09:35,560 points vary depending on the frequency of those scores. But 134 00:09:35,560 --> 00:09:39,000 that's, you know, not it's in this case it's basically giving 135 00:09:39,000 --> 00:09:42,880 equal weight to each of those scores. So I'll talk about 136 00:09:42,880 --> 00:09:48,200 another way of doing that in just a second. But if you do 137 00:09:48,200 --> 00:09:53,160 want to make a chart like this. These are all the details for 138 00:09:53,160 --> 00:09:58,320 it. The essential functions are average normal distribution and 139 00:09:58,320 --> 00:10:02,400 standard deviation. If you look at the excerpt there from the 140 00:10:02,400 --> 00:10:05,560 table you can see that we are plotting one column, which is 141 00:10:05,560 --> 00:10:09,280 column A and those are all the scores for this particular 142 00:10:09,280 --> 00:10:14,200 survey. So step one is to determine the average, and 143 00:10:14,200 --> 00:10:18,040 everybody knows how to do that. Step two is to determine the 144 00:10:18,040 --> 00:10:22,720 standard deviation. And that is, it's just a formula that you you 145 00:10:22,720 --> 00:10:26,200 know put into Excel. And when I first did this I wanted to 146 00:10:26,200 --> 00:10:29,720 really actually understand what it was doing so I did all of the 147 00:10:29,720 --> 00:10:33,800 algebra behind it. I did that six months ago so I don't recall 148 00:10:34,240 --> 00:10:37,960 really much of it and it's so much easier to just stick it in 149 00:10:37,960 --> 00:10:42,640 Excel and have it do it for you. And then step three is to figure 150 00:10:42,640 --> 00:10:47,320 out the normal distribution, which is, again, just a formula 151 00:10:47,320 --> 00:10:52,960 that you put in there. Note that the formula for normal 152 00:10:52,960 --> 00:10:59,160 distribution has two cells with dollar signs in them if you ar 153 00:10:59,160 --> 00:11:02,000 not familiar with it, thos dollar signs are basicall 154 00:11:02,000 --> 00:11:07,560 telling it that those ar absolute references, so it won' 155 00:11:07,560 --> 00:11:10,960 change like when you, when yo click in the corner of a cel 156 00:11:10,960 --> 00:11:14,680 and drag to pull that formul down through the whole column 157 00:11:14,960 --> 00:11:21,320 it won't change those, thos cells so it'll stay c two and 158 00:11:21,320 --> 00:11:25,120 two rather than making it go three c four or, you know, as i 159 00:11:25,120 --> 00:11:29,200 normally would. That's just useful bit of information there 160 00:11:30,280 --> 00:11:32,960 And then another thing t remember and I think I had thi 161 00:11:32,960 --> 00:11:38,280 on the previous slide was befor you actually start any of this 162 00:11:38,680 --> 00:11:43,600 make sure that you sorted you column of data from lowest t 163 00:11:43,600 --> 00:11:47,920 highest otherwise you'll end u with an actual scatter char 164 00:11:47,920 --> 00:11:51,240 where the all of the points ar all over the place, rather tha 165 00:11:51,240 --> 00:11:52,520 being in that nic 166 00:11:53,200 --> 00:11:54,160 curve shape. 167 00:11:58,800 --> 00:12:03,440 So, the histogram is another way of showing this data. What a 168 00:12:03,440 --> 00:12:07,560 histogram does is creates bins, or ranges of data to show 169 00:12:07,560 --> 00:12:11,920 frequency. Um, there are two ways to create these. One is to 170 00:12:11,920 --> 00:12:15,800 let Excel do it for you, which is fine, but you might want more 171 00:12:15,800 --> 00:12:20,400 control over the bins. And the second way is to activate the 172 00:12:20,400 --> 00:12:25,040 data analysis toolpak in the Data tab. This will allow you to 173 00:12:25,040 --> 00:12:30,040 define your own bins for the data or your own ranges for the 174 00:12:30,040 --> 00:12:34,760 data, which will make your chart more meaningful. This chart is 175 00:12:34,760 --> 00:12:39,720 with the bins that Excel came up with. For me, it's so good 176 00:12:39,720 --> 00:12:42,280 though because it shows that the majority of the responses were 177 00:12:42,280 --> 00:12:46,680 higher than 78. So it's still you know it's a meaningful 178 00:12:47,000 --> 00:12:48,160 meaningful chart, 179 00:12:48,440 --> 00:12:50,920 but if I 180 00:12:52,320 --> 00:12:55,640 do this on my own where I come up, come up with my own bins and 181 00:12:55,640 --> 00:12:59,600 you can see which ones I decided I wanted I wanted it to be 182 00:12:59,600 --> 00:13:09,600 between 40 and 59 60 and 79 80 and well now that's not quite 183 00:13:09,600 --> 00:13:21,360 what it was. 40 50 50 to 60 60 to 80 and then 80 to 100. This 184 00:13:21,360 --> 00:13:27,120 will show us much more easily. you know which ones scored from 185 00:13:27,120 --> 00:13:32,880 that average or from our goal which was 80 and higher so it's 186 00:13:32,880 --> 00:13:38,000 very easy to see that, let's say, 10 people scored at about 187 00:13:38,040 --> 00:13:45,720 80 and about 20 people scored at 100. So you can very quickly see 188 00:13:45,720 --> 00:13:46,360 when a score. 189 00:13:53,840 --> 00:13:59,240 And it puts the low scoring responses into perspective. All 190 00:13:59,240 --> 00:14:03,520 right. These are just a few resources that I used for 191 00:14:03,520 --> 00:14:08,160 creating those histograms for figuring out how to activate 192 00:14:08,160 --> 00:14:13,000 that tool pack that you need to create your own histograms, and 193 00:14:13,000 --> 00:14:17,840 then how to create bell curves. That's about it. Are there any, 194 00:14:17,880 --> 00:14:20,560 Any questions before I turn it over to Teri. 195 00:14:30,800 --> 00:14:35,440 All right, well, if you do have questions we will have time at 196 00:14:35,440 --> 00:14:46,480 the end for those. And. Give me one second. Okay, stop sharing. 197 00:14:48,600 --> 00:14:50,160 Maybe. All right, 198 00:14:52,240 --> 00:15:02,880 and see if I can get this up for you. Okay. So now, Teri's going 199 00:15:02,880 --> 00:15:07,520 to talk to us about. I think she's going to give us a couple 200 00:15:07,520 --> 00:15:12,760 of examples of how she's used bell curves, and also 201 00:15:14,480 --> 00:15:15,800 talk about pivot tables. 202 00:15:19,720 --> 00:15:22,400 I don't know, not really waiting very well. 203 00:15:23,120 --> 00:15:23,720 There we go. 204 00:15:25,680 --> 00:15:32,560 Okay, thank you. Okay, so I'm going to talk, give you just a 205 00:15:32,560 --> 00:15:37,160 couple more resources on learning more about the normal 206 00:15:37,160 --> 00:15:42,760 distribution. One of the reasons that I wanted to see us bring 207 00:15:42,760 --> 00:15:48,120 more of the bell curve into things that we report was after 208 00:15:48,120 --> 00:15:54,520 taking classes using SPSS, which is really easy to do, 209 00:15:54,560 --> 00:16:00,760 histograms, and scatter plots. After you leave a program you 210 00:16:00,760 --> 00:16:05,560 don't always have access to that software so I was interested in 211 00:16:05,560 --> 00:16:10,320 being being able to do it in Excel once I knew that my SPSS 212 00:16:10,320 --> 00:16:15,720 access to software would vanish. If you want to learn more about 213 00:16:15,720 --> 00:16:19,080 the normal distribution or refresh if you had a statistics 214 00:16:19,080 --> 00:16:24,280 class. 20 years ago or even you know sometimes it's even like 215 00:16:24,280 --> 00:16:29,520 Laurie said six months ago and you've forgotten that I can say 216 00:16:29,520 --> 00:16:34,520 that library school doesn't prioritize some of these data 217 00:16:34,520 --> 00:16:38,520 skills that as we all get into professions, we realized that 218 00:16:38,520 --> 00:16:42,400 they're very very valuable skills to be able to talk about 219 00:16:42,400 --> 00:16:48,680 data. So here's a quick YouTube video we'll put it in the chat, 220 00:16:48,720 --> 00:16:51,840 or else we'll, we'll make sure it's in the notes for you but 221 00:16:51,840 --> 00:16:56,600 this is a great quick overview on what the normal distribution 222 00:16:56,600 --> 00:17:04,520 is. And just some reminders about the standard deviation. 223 00:17:05,640 --> 00:17:09,560 And, you know, the percentages of people that will fall within 224 00:17:09,560 --> 00:17:14,120 a range so if you've ever had to take a IQ test for example or 225 00:17:14,120 --> 00:17:20,400 had a child that's taken an IQ test. You know that if somebody 226 00:17:20,400 --> 00:17:25,840 is falling in the first segment within the one standard 227 00:17:25,840 --> 00:17:30,040 deviation which is the bulk of the results that 68% of the 228 00:17:30,040 --> 00:17:35,840 people, 95% of people score in, you know, within two standard 229 00:17:35,840 --> 00:17:40,560 deviations, and then our super super smart people on one end 230 00:17:40,560 --> 00:17:44,880 and those with a lower IQ. On the other end are past that 231 00:17:44,880 --> 00:17:50,760 third point, on, on the normal distribution. So that's just a 232 00:17:50,760 --> 00:17:56,040 little bit more there. Here's another example of how we've 233 00:17:56,040 --> 00:17:59,880 been using them. This was a report, I did for the Board of 234 00:17:59,880 --> 00:18:05,760 Regents on LMS activity. And you can see it's really helpful here 235 00:18:05,760 --> 00:18:10,920 to be able to show those averages, these individual 236 00:18:11,040 --> 00:18:15,880 points represent each institution and where they fall. 237 00:18:16,720 --> 00:18:21,880 But it's a really quickly understandable way to convey a 238 00:18:21,880 --> 00:18:27,080 lot of data about where people fall, and it's even helpful in 239 00:18:27,080 --> 00:18:31,840 this case, I left an outlier here so somebody's data was way 240 00:18:31,840 --> 00:18:37,000 different than everybody else's. And it's just a way to identify 241 00:18:37,200 --> 00:18:41,040 differences and how people are reporting data sometimes the 242 00:18:41,040 --> 00:18:44,760 outliers are they're really interesting pieces of data to 243 00:18:44,760 --> 00:18:50,120 look at. So that's a single series example, and Laurie 244 00:18:50,120 --> 00:18:53,360 showed you kind of the setup here's what the setup behind the 245 00:18:53,360 --> 00:18:58,000 scenes looks like for that. This is literally the data set for 246 00:18:58,000 --> 00:19:04,000 that table, I've anonymized it but you can see each school has 247 00:19:04,080 --> 00:19:11,440 a row and tree. They have the average standard deviation and 248 00:19:11,440 --> 00:19:14,680 the norm of my columns got a little squished over there but 249 00:19:15,400 --> 00:19:22,320 the average here 8.38, and the standard deviation. Another 250 00:19:22,320 --> 00:19:28,560 example that I recently had to do involved the series so this 251 00:19:28,560 --> 00:19:35,360 was a research project for ICOLC. I'm working on a project 252 00:19:35,400 --> 00:19:39,360 that's essentially strategic planning and defining a mission. 253 00:19:40,160 --> 00:19:44,640 We had done some open ended engagement focus group type 254 00:19:44,640 --> 00:19:51,120 things to find out what people valued most about this 255 00:19:51,160 --> 00:19:56,040 organization, and then turned around and use that open ended 256 00:19:56,080 --> 00:20:00,480 information in the survey to confirm. So it was a small group 257 00:20:00,480 --> 00:20:03,840 of people that kind of came up with these core ideas and then 258 00:20:03,840 --> 00:20:09,960 we tested those ideas on the full population. So this is what 259 00:20:09,960 --> 00:20:13,720 it looks like when it comes out of Qualtrics, or if you use 260 00:20:13,720 --> 00:20:17,160 something like Survey Monkey, these are the type of results 261 00:20:17,160 --> 00:20:21,360 that you often get the minimum score the maximum score the mean 262 00:20:21,360 --> 00:20:22,880 the standard deviation. 263 00:20:24,200 --> 00:20:27,920 And you can sit and look at these and if you know a little 264 00:20:27,920 --> 00:20:32,440 bit about means and standard deviation. Maybe you'll be able 265 00:20:32,440 --> 00:20:36,440 to determine which ones are the most important or the strongest 266 00:20:36,440 --> 00:20:41,600 held agreements, but I went ahead and converted this to a 267 00:20:41,640 --> 00:20:47,720 series of bell curves so that you can see how these compare to 268 00:20:47,720 --> 00:20:51,560 one another. And so that looks more like this, and you can 269 00:20:51,560 --> 00:21:00,560 start to see how, in this case, certain values or beliefs, hold 270 00:21:00,560 --> 00:21:05,960 strong more strongly for the community so if we set 50, a 271 00:21:05,960 --> 00:21:11,120 scale of zero to 100 and that's kind of an important piece. I 272 00:21:11,120 --> 00:21:16,400 tend not to use Likert in a, in an assessment, just because it's 273 00:21:16,400 --> 00:21:20,720 not a continuous variable. When you set in a lot of things that 274 00:21:20,720 --> 00:21:24,720 you'll respond to from Louis you'll notice we use zero to 100 275 00:21:25,280 --> 00:21:29,760 there. The reason is this, because we can look more closely 276 00:21:29,760 --> 00:21:35,280 into the data. When we use a continuous measurement like 277 00:21:35,280 --> 00:21:41,240 this. But you can see the top, upper right quadrant I had to 278 00:21:41,240 --> 00:21:44,160 look, if I was talking left to right here I have to look at my 279 00:21:44,160 --> 00:21:49,760 hands and upper right quadrant is where we have the most 280 00:21:49,760 --> 00:21:53,640 agreement. And so there are three statements here, and I'll 281 00:21:53,640 --> 00:21:57,480 go back and bold them again. There are three statements here 282 00:21:57,480 --> 00:22:02,360 that are widely held in the, in the community, the standard 283 00:22:02,360 --> 00:22:07,000 deviation is low, meaning that there's a greater consensus and 284 00:22:07,000 --> 00:22:10,640 the mean is higher, so more people were rating that 285 00:22:10,640 --> 00:22:14,600 statement higher. So, essentially this is the same 286 00:22:14,600 --> 00:22:23,520 thing as this one. It's just, this is one series or one 287 00:22:23,520 --> 00:22:33,440 variable. And this is six variables. So they're fun to 288 00:22:33,440 --> 00:22:37,520 work with. I think I have an example. Let me see if I can 289 00:22:37,520 --> 00:22:44,440 pull it up. Of what, no I want I'm going to stick on my 290 00:22:44,440 --> 00:22:48,200 PowerPoint screen because I don't think that zoom is gonna 291 00:22:48,200 --> 00:22:56,000 like me jumping around. So there are plenty of videos out there. 292 00:22:56,640 --> 00:23:00,160 If you have a data set and you want to send it and want some 293 00:23:00,160 --> 00:23:04,680 thoughts about it. Just open a ticket. We can't do all your 294 00:23:04,680 --> 00:23:09,320 data, assessment for you but we can certainly, we'd like to play 295 00:23:09,320 --> 00:23:14,120 with data at LOUIS. And we can certainly point you in some 296 00:23:14,120 --> 00:23:19,800 directions. Okay. So, pivot tables how many people in the 297 00:23:19,800 --> 00:23:29,440 group. Use pivot tables loves pivot tables. I don't see a lot 298 00:23:29,440 --> 00:23:33,720 of hands. Okay, I love pivot tables, they're really fast and 299 00:23:33,720 --> 00:23:38,560 easy. I'm going to do a live demo with pivot tables and it 300 00:23:38,560 --> 00:23:42,800 may or may not come back to haunt me, but I'm going to start 301 00:23:42,800 --> 00:23:46,520 with just a really simple data set, I'm going to walk you 302 00:23:46,520 --> 00:23:50,480 through creating a pivot table out of it. I love them because 303 00:23:50,480 --> 00:23:54,000 as I said here they allow you to group and summarize data in 304 00:23:54,000 --> 00:23:58,800 meaningful ways. So pivot tables, use what like a 305 00:23:58,800 --> 00:24:05,360 repeating data element to group elements. So an example would be 306 00:24:05,440 --> 00:24:12,880 a report by system level so LCTC system schools UL system LSU 307 00:24:12,880 --> 00:24:18,320 system. I can roll up as another word for it I can roll up the 308 00:24:18,320 --> 00:24:24,920 data by master grouping. If you're a SirsiDynix person, and 309 00:24:24,920 --> 00:24:27,600 you're running reports there you're probably familiar with 310 00:24:27,600 --> 00:24:33,200 rolling up or grouping data by item types grouping is great 311 00:24:33,200 --> 00:24:36,960 grouping data by user types so how do you want to look at it. 312 00:24:37,440 --> 00:24:40,240 Do you want to look at it by demographics, do you want to 313 00:24:40,240 --> 00:24:43,560 roll up your data and look at it by different demographic 314 00:24:43,600 --> 00:24:44,480 graphics. 315 00:24:46,080 --> 00:24:50,520 If you're an acquisitions, or an administrative type person. Do 316 00:24:50,520 --> 00:24:54,120 you want to roll up your data and look at it by expenditure 317 00:24:54,120 --> 00:24:58,840 type. So all those things are really easy to do with pivot 318 00:24:58,840 --> 00:25:04,160 tables, if you've set up your Excel in a reasonable way. I'm 319 00:25:04,160 --> 00:25:07,920 going to start with a pretty ugly spreadsheet that is missing 320 00:25:07,920 --> 00:25:11,360 some key elements and show you how I'll fix it to make it a 321 00:25:11,360 --> 00:25:17,080 little bit easier to deal with than a pivot table. So here are 322 00:25:17,080 --> 00:25:23,720 the setup things. Number one, in your Excel spreadsheet that 323 00:25:23,720 --> 00:25:27,040 you're working with. it's very important to have header rows 324 00:25:27,040 --> 00:25:31,640 with meaningful names. Because when you get into pivot tables, 325 00:25:32,640 --> 00:25:37,080 your header rows are going to help you figure out how to group 326 00:25:37,080 --> 00:25:41,160 things if you haven't put those in. You'll be flipping back and 327 00:25:41,160 --> 00:25:44,400 forth between your source spreadsheet and the pivot table 328 00:25:44,400 --> 00:25:48,000 to try and figure out which groupings. You're trying to work 329 00:25:48,000 --> 00:25:53,880 with. It's important that you have your numbers formatted as 330 00:25:53,880 --> 00:25:57,160 numbers, especially if you do want to do things like add 331 00:25:57,440 --> 00:26:02,680 Count. Find averages. Clean up your data before you get into 332 00:26:02,680 --> 00:26:08,840 pivot tables and have everything formatted as numbers. Save your 333 00:26:08,840 --> 00:26:12,320 file, especially if you've exported it from somewhere save 334 00:26:12,320 --> 00:26:16,800 it as an Excel file and not as a CSV so that you don't 335 00:26:17,200 --> 00:26:22,440 accidentally forget to save the work that you've done so things 336 00:26:22,440 --> 00:26:28,880 like spreadsheets. I, I'm sorry worksheets and imagery that 337 00:26:28,880 --> 00:26:33,920 you've created in Excel is not saved the same way in a CSV file 338 00:26:33,920 --> 00:26:37,760 so I recommend. After you export it from wherever you're getting 339 00:26:37,760 --> 00:26:43,560 it go ahead and save it in Excel format. From there we're going 340 00:26:43,560 --> 00:26:46,800 to do a lot of click drag and drop, which I'm sure a lot of 341 00:26:46,800 --> 00:26:51,520 you can appreciate pivot tables. You don't necessarily need to 342 00:26:51,520 --> 00:26:56,200 know how to do formulas in Excel. You can click drag and 343 00:26:56,200 --> 00:27:01,320 drop to get it to work. And this is really, I had an epiphany 344 00:27:01,320 --> 00:27:05,280 recently about this about why it's called a pivot table. Well, 345 00:27:05,560 --> 00:27:09,600 what I can surmise is that you're pivoting what's in your 346 00:27:09,600 --> 00:27:14,040 column headers becomes your row headers so you're literally 347 00:27:14,080 --> 00:27:21,440 pivoting the data. Then something else to consider that 348 00:27:21,440 --> 00:27:26,520 as we put this data together the first row that you put in there 349 00:27:26,560 --> 00:27:30,400 is going to be your highest grouping element. So your most 350 00:27:30,400 --> 00:27:36,280 important column, and usually this is a text element. So in 351 00:27:36,280 --> 00:27:43,440 the example of systems, my highest grouping is system and 352 00:27:43,480 --> 00:27:48,680 the groupings are all going to be text. And then you'll see as 353 00:27:48,680 --> 00:27:53,320 we walk through here that your values are usually your columns 354 00:27:53,320 --> 00:28:00,960 that are numerical, and again, formatted as numbers. Okay, so 355 00:28:00,960 --> 00:28:05,040 here's a little bit of a finished product of what that 356 00:28:05,080 --> 00:28:11,520 looks like. And I'm only showing a small snippet of one system 357 00:28:11,520 --> 00:28:14,400 but we're going to walk through this full report that has all 358 00:28:14,400 --> 00:28:19,280 the systems you can see I'm group now. I have all my LCTCS 359 00:28:19,280 --> 00:28:24,960 schools grouped together I have individual totals, as well as 360 00:28:25,560 --> 00:28:31,120 totals for each type. So let me hop out of 361 00:28:37,760 --> 00:28:38,720 this. 362 00:28:40,280 --> 00:28:46,240 And, hopefully, over to Excel. Let's see. 363 00:28:51,840 --> 00:28:56,640 Stop share. I'm gonna go find my screen. 364 00:29:21,000 --> 00:29:23,560 Okay. Can you see my Excel screen. 365 00:29:29,440 --> 00:29:31,040 Yes. Okay, good. 366 00:29:31,040 --> 00:29:39,280 Thank you. All right. So, um, here's kind of a peek behind the 367 00:29:39,280 --> 00:29:47,440 scenes, and you can see I have headers in here. However, I have 368 00:29:47,440 --> 00:29:51,840 kind of broken my own rule here and the headers that are on top, 369 00:29:51,880 --> 00:29:56,080 are these questions because this came out of Qualtrics, and my 370 00:29:56,080 --> 00:30:02,240 headers say Q1 Q2. So again, that's not going to be really 371 00:30:02,240 --> 00:30:08,680 helpful when I'm working with the pivot tables so I do want to 372 00:30:10,720 --> 00:30:17,440 change these, so that they are something meaningful. 373 00:30:34,120 --> 00:30:47,520 Okay. I was the viewing of Excel in, zoom, Laurie. It's good. 374 00:30:47,520 --> 00:30:53,480 Okay. All right. So over here I've kind of already started. 375 00:30:54,600 --> 00:30:58,600 But I'll go straight through I'm going to click the Insert 376 00:30:58,600 --> 00:31:05,320 button. This is where your Pivot Table Tools are. If you click on 377 00:31:05,320 --> 00:31:10,880 pivot table, it will automatically, usually, select 378 00:31:10,920 --> 00:31:14,200 the range that you want if it doesn't, select the range that 379 00:31:14,200 --> 00:31:18,560 you want, you can look and see these little green marching ants 380 00:31:18,840 --> 00:31:23,400 are running around, just scroll through and it shows you which 381 00:31:23,400 --> 00:31:28,720 data it is going to grab for your pivot table so it was 382 00:31:28,720 --> 00:31:34,800 pretty smart it knew where to stop. It also did not include I 383 00:31:34,800 --> 00:31:39,960 already in this spreadsheet had some totals in there. And so 384 00:31:39,960 --> 00:31:43,880 this is good I actually didn't want my totals to come over into 385 00:31:43,880 --> 00:31:48,280 the pivot table so I'm gonna click OK, and then you get this 386 00:31:48,720 --> 00:31:53,760 scary box. If you've ever tried to use this maybe you've gotten 387 00:31:53,760 --> 00:32:00,480 to this point. And you're like, what, now what okay where's my 388 00:32:00,480 --> 00:32:08,280 table, and you see over here the pivot table fields are up here, 389 00:32:08,560 --> 00:32:12,360 and this is where we click drag and drop, and this is why I was 390 00:32:12,360 --> 00:32:16,880 saying I needed to change my header rows. Unfortunately, I 391 00:32:16,880 --> 00:32:21,840 may not have saved them, or put them in the right place so it 392 00:32:21,840 --> 00:32:29,280 did not did not grab them, let me do this again. Insert, 393 00:32:30,680 --> 00:32:32,000 Pivot Table. 394 00:32:34,720 --> 00:32:38,960 Okay, it didn't change them. That's okay. I know what they 395 00:32:38,960 --> 00:32:44,600 are. So I told you the checklists the very. The very 396 00:32:44,600 --> 00:32:49,040 first thing that you want to do is group. These, what was the 397 00:32:49,040 --> 00:32:55,000 column. So if you go back to my source data. The first column 398 00:32:55,000 --> 00:32:59,600 was system, and I'm going to turn that column into a row. 399 00:32:59,720 --> 00:33:06,640 Okay so magic just happened. And I've now got groupings for each 400 00:33:06,680 --> 00:33:10,520 of my systems, and you can see that I have one in here that I 401 00:33:10,520 --> 00:33:14,960 don't want this one that says select system and institution. 402 00:33:15,440 --> 00:33:19,760 That's okay, I'm going to uncheck that box. And that one 403 00:33:19,760 --> 00:33:24,360 is going to magically disappear. I have not done anything to my 404 00:33:24,360 --> 00:33:28,720 source data, it is all safely in another worksheet, I've done 405 00:33:28,720 --> 00:33:33,640 nothing to it. If you remember back to my example, my second 406 00:33:33,800 --> 00:33:39,600 column was names of the institutions. So, if I've 407 00:33:39,600 --> 00:33:45,760 properly labeled. All of my data. And I drop it now down 408 00:33:45,760 --> 00:33:50,360 into the rows, you can see I magically get a breakdown now. 409 00:33:51,200 --> 00:33:56,960 You can do it as I guess they call this a. I don't know this 410 00:33:56,960 --> 00:34:04,200 is a name. It is a refer to this as a two dimensional pivot 411 00:34:04,200 --> 00:34:09,200 table, when you have two data elements, you could put this in 412 00:34:09,200 --> 00:34:14,880 here, or you could leave it as a filter. If you wanted to be able 413 00:34:14,880 --> 00:34:19,880 to filter these later on you see up here I have a dropped down 414 00:34:19,880 --> 00:34:24,560 now I can filter those so if I wanted to just look at certain 415 00:34:24,560 --> 00:34:29,040 institutions in my finished pivot table I could put it as a 416 00:34:29,040 --> 00:34:37,560 filter. I'm going to put it back down here. My next 12345 417 00:34:37,960 --> 00:34:43,680 elements were numerical okay so these are my actual values to 418 00:34:43,680 --> 00:34:48,200 the questions that I asked. And again, I'm going to drop my 419 00:34:48,240 --> 00:34:57,480 numerical values over here into the values. And I think I have 420 00:34:57,520 --> 00:35:01,880 five of them again you can see why I should have from the get 421 00:35:01,880 --> 00:35:08,080 go, made sure that my numbers were correct. And right now this 422 00:35:08,080 --> 00:35:14,120 looks pretty not useful. It's really just telling me how many 423 00:35:14,160 --> 00:35:19,640 of each yes or no did this site have data reported for that data 424 00:35:19,640 --> 00:35:22,680 element it's just a count of whether or not there's data 425 00:35:22,680 --> 00:35:28,720 there or not. What I really need is a sum. So you'll see these 426 00:35:28,720 --> 00:35:32,440 elements over here have dropdowns. I'm going to switch 427 00:35:32,720 --> 00:35:41,120 from account to a sum. Okay, and I'm going to do this for each 428 00:35:41,120 --> 00:35:51,800 one of my data elements, switch it from count to sum. And that's 429 00:35:51,840 --> 00:35:53,960 value field settings. 430 00:36:08,000 --> 00:36:08,840 One more. 431 00:36:10,320 --> 00:36:16,480 Great. So now I know, and if I had done this before I think it 432 00:36:16,480 --> 00:36:24,440 was. I can still change these. I had WiFi, I think I had laptop, 433 00:36:26,040 --> 00:36:27,160 books, 434 00:36:29,800 --> 00:36:34,560 iPads, this might not be the right order, and headsets. Okay. 435 00:36:36,520 --> 00:36:42,640 Um, so right now I have sums but what if I wanted to know. Oh and 436 00:36:42,640 --> 00:36:46,120 I have a grand total What if I wanted to know the percentage, 437 00:36:46,160 --> 00:36:55,480 what percentage of 12,000 Wi Fi hotspots were requested by each 438 00:36:55,480 --> 00:36:58,920 system or each campus if I wanted to get an assessment of 439 00:36:58,920 --> 00:37:03,720 whether or not the requests were fair. Now, if you remember when 440 00:37:03,720 --> 00:37:06,880 I clicked over here. There wasn't anything that said 441 00:37:06,880 --> 00:37:17,680 percentage. I need to go up here and show values as I can switch 442 00:37:17,680 --> 00:37:22,440 it to now the percentage of the grand total so I know overall 443 00:37:22,920 --> 00:37:33,040 100% of the Wi Fi accounted for LCTCS requested 48% of them, and 444 00:37:33,040 --> 00:37:37,160 then I get the individual breakdown so that is right click 445 00:37:37,680 --> 00:37:47,040 Show values as. percent of grand total, and I'll go back to. I'll 446 00:37:47,040 --> 00:37:53,120 go back to no calculation. The other thing people want to do 447 00:37:53,120 --> 00:38:00,000 often is sort these top to bottom. So just need to click in 448 00:38:00,000 --> 00:38:08,160 a cell that is not one of these blue header bars and sort. You 449 00:38:08,160 --> 00:38:15,600 can do sort smallest to largest. And you can see that it's sorted 450 00:38:15,600 --> 00:38:20,360 within my groupings smallest to largest. I believe if I click 451 00:38:20,360 --> 00:38:25,280 here and sort smallest to largest. I'm now going to get 452 00:38:26,040 --> 00:38:33,120 over all the systems. So it now regrouped and moved the LSU 453 00:38:33,120 --> 00:38:39,480 system to the top, And then resorted inside of each system 454 00:38:39,520 --> 00:38:46,840 level as well. So those are the fun things to do with pivot 455 00:38:46,840 --> 00:38:52,840 tables. You can also create charts and graphs from here 456 00:38:52,840 --> 00:38:59,120 pivot tables. So if you go back to insert. All of these 457 00:38:59,120 --> 00:39:05,040 functions that Laurie already mentioned, you can use those 458 00:39:05,040 --> 00:39:10,000 with pivot charts, as well so now I can click on this really 459 00:39:10,000 --> 00:39:16,040 interesting clustered column and show this really nice visual 460 00:39:16,040 --> 00:39:18,840 representation I'll make it bigger so you can kind of see 461 00:39:18,840 --> 00:39:27,600 it. What each institution has requested. And there's a little 462 00:39:27,600 --> 00:39:34,800 key close out my pivot table. And there's a key over here to 463 00:39:34,800 --> 00:39:44,400 show you which devices were requested where going to stop 464 00:39:44,400 --> 00:39:51,720 share. And I'm going to come back over here. 465 00:39:58,080 --> 00:40:03,400 So again, here are the setup reminders. Your first row is 466 00:40:03,400 --> 00:40:07,360 your most important or your highest grouping element, and 467 00:40:07,360 --> 00:40:13,880 your values are usually going to be numbers. They're really easy 468 00:40:13,880 --> 00:40:20,440 to print out to PDF they look good, right away. And then as I 469 00:40:20,440 --> 00:40:24,040 showed you, you can experiment with pivot tables you are not 470 00:40:24,040 --> 00:40:28,800 going to break your original data set. Pivot Tables are being 471 00:40:28,800 --> 00:40:36,160 built in a completely separate sheet. So if you change the row 472 00:40:36,160 --> 00:40:40,920 labels in the pivot table, it's not going to go back and edit 473 00:40:40,920 --> 00:40:42,200 your worksheet. 474 00:40:48,680 --> 00:40:49,440 Okay. 475 00:40:53,800 --> 00:40:57,520 What questions do you have, you can ask us to try and do 476 00:40:57,520 --> 00:41:02,080 something. I can't guarantee that we'll be able to do it on 477 00:41:02,080 --> 00:41:02,840 the fly. 478 00:41:05,240 --> 00:41:06,240 But we'll try. 479 00:41:17,040 --> 00:41:19,360 Is there anything you want to see more in depth. 480 00:41:27,240 --> 00:41:32,040 Elizabeth says that's a great intro. Pivot Tables are not 481 00:41:32,040 --> 00:41:38,240 scary. I think people don't use them as much as they could. They 482 00:41:38,240 --> 00:41:43,520 look great in PowerPoints, and they're super easy to export 483 00:41:43,520 --> 00:41:53,080 into Word. Anybody else here have any fancy things that they 484 00:41:53,080 --> 00:41:57,840 like to do in Excel with charts and visual representations that 485 00:41:58,080 --> 00:42:01,080 you don't we won't put you on the spot and make you show us. 486 00:42:03,680 --> 00:42:06,520 Do you Emily asked do you feel like you could do a pivot tables 487 00:42:06,520 --> 00:42:11,600 even with a small data set or is it more for larger data sets. 488 00:42:12,720 --> 00:42:17,120 Emily that data set that I had was not very big at all it was, 489 00:42:17,680 --> 00:42:21,200 you know, 35 institutions with five data points so that's 490 00:42:21,200 --> 00:42:27,800 pretty, pretty small. I think it could have been even just two 491 00:42:27,800 --> 00:42:32,800 elements, two points of data for that many institutions, and it 492 00:42:32,800 --> 00:42:36,760 would still work pretty quickly, the more you use them the, the 493 00:42:37,720 --> 00:42:39,880 quicker it is to use the next time. 494 00:42:48,920 --> 00:42:56,560 Could you talk briefly about the various trend lines. User Sanlu, 495 00:42:56,600 --> 00:42:59,040 tell us a little bit more about what you want to know 496 00:43:23,560 --> 00:43:28,320 what is the logarithmic versus straight line difference. I have 497 00:43:28,320 --> 00:43:30,440 no idea. I would have to Google it. 498 00:43:36,200 --> 00:43:40,840 Do you mean how we've used smoothing, instead of straight 499 00:43:40,840 --> 00:43:53,560 lines between the points on the visualization. Adding trend 500 00:43:53,560 --> 00:44:00,360 lines to charts. Laurie, do you want to say anything about it. 501 00:44:08,480 --> 00:44:14,600 I don't know that I really have anything to add. You know that. 502 00:44:17,000 --> 00:44:21,400 I would recommend trying out the, what we did with the bell 503 00:44:21,400 --> 00:44:29,000 curves. Because it does put you know the the data into a 504 00:44:29,000 --> 00:44:32,680 perspective that you wouldn't otherwise be able to, you know, 505 00:44:32,680 --> 00:44:38,440 easily display. But, um, yeah I don't I don't really. I don't 506 00:44:38,480 --> 00:44:40,400 really have much to add there sorry. 507 00:44:41,960 --> 00:44:45,920 Yeah, I'll just add that I do two things when I'm trying to 508 00:44:45,920 --> 00:44:51,200 figure out how to do things with Excel. One, I like to watch 509 00:44:51,200 --> 00:44:57,320 YouTube videos. And a lot of times I need to do extra YouTube 510 00:44:57,320 --> 00:45:02,240 videos when they bring up a concept that I don't typically 511 00:45:02,280 --> 00:45:06,320 you know understand. The second is, I have a husband who's an 512 00:45:06,320 --> 00:45:14,240 engineer and uses Excel all day long. He's Captain formula. And 513 00:45:14,240 --> 00:45:19,040 I asked him for a little bit of assistance to ask a friend or i 514 00:45:19,320 --> 00:45:24,440 or i Google it and ZeeZee did just that. trendline is a line 515 00:45:24,440 --> 00:45:28,040 drawn over pivot highs or under pivot lows to show the 516 00:45:28,040 --> 00:45:31,040 prevailing direction of price that's probably a pretty 517 00:45:31,040 --> 00:45:36,240 specific definition. But, yeah. 518 00:45:50,760 --> 00:45:56,480 A second googling it. I mean anytime we want to, like, you 519 00:45:56,480 --> 00:45:59,360 know, the first point that I made about trying to figure out 520 00:45:59,360 --> 00:46:03,080 what it is that you're trying to convey. Starting out with that 521 00:46:03,080 --> 00:46:07,760 question, and then you know adding the keywords in Excel 522 00:46:08,240 --> 00:46:11,840 will kind of get you wherever you want to go, you know if 523 00:46:11,840 --> 00:46:16,640 you're willing to spend the time reading the various, you know, 524 00:46:16,640 --> 00:46:19,360 help articles that you'll come across or watching the various 525 00:46:19,360 --> 00:46:27,800 videos, and also having, I have a someone in my house who is 526 00:46:27,800 --> 00:46:32,360 very interested in math. And so when I was trying to figure out 527 00:46:32,360 --> 00:46:36,480 how all of the math worked. It was super helpful to just talk 528 00:46:36,480 --> 00:46:39,560 through it with someone else who had that kind of math 529 00:46:39,560 --> 00:46:44,600 perspective, versus just the, you know, visualization. Yeah. 530 00:46:45,960 --> 00:46:51,000 I find that the majority of questions that people want to 531 00:46:51,000 --> 00:46:54,960 know or the level of understanding for the general 532 00:46:54,960 --> 00:46:58,320 public when we put together reports the bell curve is 533 00:46:58,320 --> 00:47:02,240 probably the most complex thing that we do in visualizations 534 00:47:02,240 --> 00:47:05,360 generally people want to know what percentage of people felt 535 00:47:05,360 --> 00:47:13,440 this way or that way. What is the change over time. You know, 536 00:47:14,040 --> 00:47:19,720 how can something Laurie showed is what percentage of a group is 537 00:47:19,720 --> 00:47:25,280 in agreement disagreement. Where did the scores fall, and those 538 00:47:25,280 --> 00:47:30,040 are probably the things that we do the most frequently in the 539 00:47:30,040 --> 00:47:34,160 office. Mike Matthews Do you know any free resources for 540 00:47:34,160 --> 00:47:38,560 learning more about statistics and data and visualization. Khan 541 00:47:38,560 --> 00:47:43,800 Academy on YouTube is good but are there more Coursera has 542 00:47:43,800 --> 00:47:50,120 quite a few options. I think I've seen a couple. Yep. The 543 00:47:50,120 --> 00:47:58,760 Lynda courses through the Public Library. There are. I think Code 544 00:47:58,760 --> 00:48:03,560 Academy has some visualization things too, and Laurie you had 545 00:48:03,560 --> 00:48:06,840 signed up for something at one point your call what it was. 546 00:48:07,200 --> 00:48:13,160 It was EDx, and they have a, it's a, like a partnership with 547 00:48:13,200 --> 00:48:18,960 MIT and you know a bunch of different schools will provide 548 00:48:18,960 --> 00:48:22,440 content. A lot of it is free but if you want to get like the 549 00:48:22,440 --> 00:48:25,440 certification at the end you have to pay for it but you can, 550 00:48:25,440 --> 00:48:30,480 you know, view the course materials and the exercises with 551 00:48:30,480 --> 00:48:31,560 no cost. 552 00:48:42,600 --> 00:48:52,280 Great. Again, we can't do all your data for you, but you know 553 00:48:52,280 --> 00:48:55,840 we would like to offer a little bit assistance, certainly in 554 00:48:55,840 --> 00:49:01,200 pointing you towards additional training and online 555 00:49:01,240 --> 00:49:08,640 opportunities. I hope this was a good limited introduction and 556 00:49:08,640 --> 00:49:14,200 gets you interested in using some more Excel functions. We'll 557 00:49:14,200 --> 00:49:18,240 be looking forward to seeing all your charts and graphs and pivot 558 00:49:18,240 --> 00:49:26,080 tables in your LOUIS User Conference presentations. Hint 559 00:49:26,080 --> 00:49:32,560 hint. And yeah, thank you for being here today.