WEBVTT 00:01.210 --> 00:08.890 High in this session, we will wrap up with inferential statistics, so let us discuss about coordination, 00:08.920 --> 00:15.460 cooperation, which is the last topic which we have in invention diagnostics now. 00:16.720 --> 00:22.330 We have already discussed mode for deletion, so we already know what correlation is, right? 00:22.690 --> 00:31.060 Correlation is something which provides us the detection of the relationship between two evils, and 00:31.210 --> 00:36.070 it also provides us the strength of the relationship between the variables. 00:36.400 --> 00:36.690 Right. 00:37.000 --> 00:39.850 So correlation is what it provides. 00:39.880 --> 00:41.460 Now, what is the index? 00:41.470 --> 00:48.580 What is the barometer which actually provides this relationship the strength of the relationship between 00:48.580 --> 00:51.970 the variables that is called correlation coefficient. 00:53.540 --> 01:01.470 So the value of correlation coefficient will range from minus one to one, and it is calculated. 01:01.850 --> 01:07.280 Now, if the value is calculated, which is greater than one or less than one, that means that we have 01:07.280 --> 01:10.520 made some mistake in calculating the correlation for fishing. 01:11.120 --> 01:19.850 And the correlation of minus one shows that there is a negative correlation, while a correlation coefficient 01:19.850 --> 01:24.200 of one which shows that it is a perfect positive correlation. 01:25.290 --> 01:33.420 And a correlation of zero point zero shows that there is no relationship between the movement of political. 01:34.740 --> 01:35.910 Here you can see. 01:37.230 --> 01:43.950 That made us say we have to wait with one variable being income and another variable being the age of 01:43.950 --> 01:46.030 the person, but experience of the person. 01:46.200 --> 01:52.230 So as the experience of the person increases, the income of the person is also increasing. 01:52.240 --> 01:54.990 And there is a very strong relationship between these. 01:55.740 --> 02:03.660 Now, here we have a relationship when let's say we have the age of the person, I'm the exercise of 02:03.660 --> 02:04.190 the person. 02:04.380 --> 02:10.800 So let's say we have the exercise of the person and the health issues. 02:12.080 --> 02:19.100 So as the person may increase the exercise, which he is doing, then the number of health issues will 02:19.310 --> 02:19.940 reduce. 02:20.150 --> 02:23.420 So here we have a strong negative correlation. 02:23.420 --> 02:29.640 That is, it is a zero point nine nine that it is a very strong negative relationship. 02:30.260 --> 02:36.980 Now, in case we have a straight line or line, which is more on line, actually, but these are called 02:36.980 --> 02:45.950 nonlinear relationships and then we have not a straight line, nor does or not convict data points. 02:46.280 --> 02:51.840 Then it is called medium or weak correlation. 02:52.010 --> 02:57.860 So here we have a weak to medium good relationship and weak to medium negative relationship. 02:59.740 --> 03:06.040 So this means that it is not a sure thing that if we increase the salary, let's say we increase the 03:06.040 --> 03:09.950 salary of the person so it increases the number of expenses. 03:09.970 --> 03:12.100 So that is not a sure thing. 03:12.220 --> 03:17.770 So these are different kind of relationships, which we can build from correlation coefficient. 03:18.970 --> 03:28.640 Next, what we have is the dog, which you can maybe take a screenshot of and keep as a standard go 03:28.690 --> 03:32.770 to point where you can decide which particular test you want to conduct. 03:34.110 --> 03:40.260 So if you want to conduct a test and you have categorical variables. 03:41.590 --> 03:47.530 Then you will follow this particular line, and in this you have continuous variables, then you can 03:47.530 --> 03:48.610 follow this particular. 03:50.450 --> 03:59.090 So what is it in case you have categorical variables, then you can have this, you can have two samples 03:59.090 --> 04:08.750 at best, or you can have squared this now in case you have continuous values then and if you have a 04:08.750 --> 04:10.630 specific number of samples. 04:10.880 --> 04:16.940 So based on the number of samples, you can decide if let us say we have one. 04:18.860 --> 04:24.770 Sample, if we have two samples and if we have more than two samples, then we then begin to say. 04:26.450 --> 04:32.630 Here you can see if you have more than two samples, then you can apply the unadvertised. 04:33.980 --> 04:40.820 They if you have one sample and you have a standard for population standard deviation, then you can 04:40.820 --> 04:47.270 apply when somebody is if you know the population standard deviation and the sample size is less than 04:47.270 --> 04:52.940 30, you will apply once I repeat this, if the sample size is greater than equal, that you will apply 04:52.940 --> 04:59.090 one sample so you can follow this particular dog to find out which particular test you want to conduct 04:59.570 --> 04:59.920 now. 04:59.960 --> 05:08.840 Next, we will have a look at how you will perform these tests by using a machine instead of doing it 05:08.840 --> 05:09.530 by hand. 05:10.900 --> 05:16.780 So let me show you that so this is one website which we have. 05:18.210 --> 05:27.190 This website is foie gras, five dot com, so you can go to golf bag dot com and you can go to the quick 05:27.210 --> 05:28.260 calculator's.