Grace/C3/Data-Fitting/English-timed

Time	Narration
00:01	Welcome to the tutorial on Data fitting in Grace.
00:06	In this tutorial, we will Fit a given set of data points to a linear equation
00:14	Add two datasets to a graph panel
00:18	Add legends and format the legend properties
00:24	To record this tutorial, I am using Ubuntu Linux 16.04 OS
00:33	Grace 5.1.25 and Gedit 3.18.3
00:42	To follow this tutorial, Learner must be familiar with the Grace interface.
00:48	For pre-requisite tutorials, please visit this website.
00:54	Data fitting is a process to find a mathematical relation between two variables.
01:01	An equation is used to describe the XY dataset pair.
01:06	Using an iterative process, the coefficients are optimized.
01:11	The process returns the best possible coefficients for the equation.
01:17	Go to Desktop. Notice the file, line-fit.txt.
01:24	This file is provided to you in the Code Files link.
01:29	I have downloaded, extracted and saved the file to the Desktop directory.
01:36	Next, open the Grace program and load this file to plot an XY graph.
01:43	Choose XY data set, and autoscale data.
01:48	Next I will format the plot.
01:52	I have changed the symbols, line style, tick marks and axis labels.
01:59	The data points do not lie on a straight line.
02:04	The data points look scattered around a straight line.
02:08	We will fit this data to a straight line.
02:12	For a straight line, this is process is called linear regression.
02:18	A straight line is described by an equation in two variables,
02:24	The equation has the form, y equals mx+c.
02:29	Here, m is the slope of the line and c is the intercept.
02:36	Go to Data and select Transformations.
02:42	Notice the available options.
02:46	Choose Regression from the list.
02:48	A Regression dialog box opens.
02:52	Choose the data set of interest from Apply to set.
02:58	Currently only a single set is loaded and it is shown as S0.
03:05	The next number is the number of points that are present in the dataset.
03:12	If many datasets are loaded more sets will be seen, in the Apply to set box.
03:19	Choose linear for Type of fit.
03:23	In the Load drop-down, choose Fitted values.
03:29	For restrictions choose None.
03:33	If only part of the data set needs to be fitted, enter the required range.
03:39	We can also specify the start load at and stop load at values in the form.
03:47	I will leave it as is and will not make changes here.
03:52	Click on Accept to run the data fitting.
03:56	The data fitting algorithm runs.
03:59	A Grace console window appears with mathematical details.
04:04	We can see the equation used for the fitting.
04:08	The output values of residual, slope, intercept and other parameters are seen.
04:17	The fitted line appears on the screen.
04:21	The fitted values are loaded in a different set, S1.
04:26	Click on File, Save to save the parameters.
04:31	The Grace: Save logs window opens.
04:35	In Save to file text box, type the filename as line-regression.txt.
04:43	Click on Apply and then click on Close to close the dialog box.
04:49	Close the Grace Console by File, Close or press theEsc key.
04:56	Click on Close in the Regression window.
05:00	Double click on the line in the graph.
05:03	Grace Set appearance window opens.
05:07	In the select set form, select S1 data set. This is the fitted data.
05:15	Right click to open the context menu.
05:19	Select Edit, and in In spreadsheet option.
05:24	The Grace: Spreadsheet dataset editor appears.
05:29	Notice the x and corresponding fitted y values of the fitted data.
05:36	These values are different from the input data.
05:40	This line fits the input data best, with a linear equation.
05:46	Close the Spreadsheet dataset Editor.
05:50	The original data set and the fitted values are the 2 datasets in the graph.
05:58	Datasets are labeled as [S0] for first set and [S1] for the second set.
06:05	We can add more datasets to this graph panel.
06:10	For this load the data using the Data, import, ASCII option from the menu.
06:18	Next, we will add legends to the plot.
06:22	Open the Set Appearance window and choose the dataset S0.
06:29	Under Legend, in the String field, type Data as the legend label. Click on Apply.
06:38	Next choose S1 dataset from the Select set data form.
06:45	Here, I will type Fitted data for the legend and click on Apply.
06:53	Close the Set Appearance window.
06:57	For some users, the legend position may be outside the graph border.
07:03	To reposition the legends, place the cursor in the legend box.
07:09	Then press Control and L keys together.
07:13	The cursor changes to a pointer.
07:17	Left click and drag the legend box to the desired position.
07:24	Then, press Enter.
07:27	Press the Escape key to exit the movement mode.
07:32	Next, open the Graph appearance window.
07:36	In the window, click on the Legends tab, to format the legend fonts.
07:43	Under the text properties, move the character size slider to 70.
07:49	The legends look very large compared to the graph.
07:53	I will retain the font type as Times-Roman.
07:58	Click on Apply to apply the changes.
08:02	Click on the legend box tab.
08:06	We can also specify the position of the legend in the location section. I will leave it as is.
08:10	In the Frame line section, for Pattern, I will choose None.
08:22	Click on Apply. This will remove the legend frame box.
08:28	Then Close the dialog box.
08:31	Now save the project in the Desktop directory.
08:36	I will use the file name regression dot agr.
08:41	Press Control Q or Click on File, Exit to exit Grace.
08:49	Let’s summarize.
08:51	In this tutorial we, Performed data fitting for a straight line.
08:57	Added two datasets in the graph panel.
09:01	Added legends to the plot and Formatted the legend properties.
09:08	For assignment activity, please do the following.
09:13	Two assignment files are provided in the Code files link.
09:18	Plot them in the same graph panel.
09:22	Perform quadratic regression on the parabola1.txt file.
09:28	Load the fitted data in the graph.
09:32	Add legend strings and reposition them inside the graph panel.
09:38	Format the appearance of the three datasets to distinguish them.
09:43	Your completed assignment may look similar to this.
09:48	This video summarizes the Spoken Tutorial Project. Please download and watch it.
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10:24	This is Rani from IIT Bombay. Thank you for joining.

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