1 00:00:00,360 --> 00:00:04,620 In this video, we will discuss the results we received for the categorical variables. 2 00:00:06,360 --> 00:00:10,200 If you remember, there were two categorical variables in our dataset. 3 00:00:11,160 --> 00:00:13,470 One was airport with values. 4 00:00:13,620 --> 00:00:14,360 Yes and No. 5 00:00:15,600 --> 00:00:19,680 Which we converted into a corresponding dummy variable called airport. 6 00:00:19,710 --> 00:00:20,250 Yes. 7 00:00:20,910 --> 00:00:22,530 With values one and two. 8 00:00:23,870 --> 00:00:31,040 Second categorical variable was body, which had four categories, and we made three dummy variables 9 00:00:31,190 --> 00:00:34,480 with value zero and one to correspond to this variable. 10 00:00:36,140 --> 00:00:39,350 Let's see what that model has to say about these variables. 11 00:00:43,210 --> 00:00:45,270 Let's talk about the airport variable first. 12 00:00:47,260 --> 00:00:48,400 We look at the equation. 13 00:00:49,600 --> 00:00:52,780 Here I have kept only airport in the linear equation. 14 00:00:53,290 --> 00:00:55,090 You can keep all other variables also. 15 00:00:55,210 --> 00:01:01,450 So basically the equation is why is equal to all the other variables multiplied with the coefficients 16 00:01:01,750 --> 00:01:07,390 plus constant plus Biddulph Airport and to the airport variable. 17 00:01:10,680 --> 00:01:13,740 What this means is there are two possibilities. 18 00:01:14,100 --> 00:01:19,470 If there is a report, which means the value of x ray is one. 19 00:01:20,480 --> 00:01:27,310 Then we'll have value of all the other predicted multiplied by the collisions plus the constant plus 20 00:01:27,440 --> 00:01:28,130 beta one. 21 00:01:29,220 --> 00:01:36,400 And if it is zero, this beta one will not be present since it's a zero, so beta one into XY is zero. 22 00:01:40,580 --> 00:01:47,450 Therefore, this of airport is straightaway giving us the different and host place. 23 00:01:48,440 --> 00:01:51,680 If there is an airport and if it is not. 24 00:01:53,710 --> 00:02:00,520 So if I look at the results of my model airport, yes, variable has an estimate of one point one three. 25 00:02:00,670 --> 00:02:01,810 This is GBW. 26 00:02:03,050 --> 00:02:11,000 So this means that if there is an airport and all the other variables are seeing the value of the house, 27 00:02:11,030 --> 00:02:15,440 the price of the house will increase by one point one three unit. 28 00:02:17,990 --> 00:02:25,730 Also, look at the P value for this P value is low, which means there is a statistical evidence of 29 00:02:25,730 --> 00:02:31,250 a difference in the house price, depending on whether there is an airport or whether that is not an 30 00:02:31,250 --> 00:02:31,610 airport. 31 00:02:35,250 --> 00:02:37,640 Also note that this cording of airport. 32 00:02:37,830 --> 00:02:39,000 Yes, as one. 33 00:02:39,120 --> 00:02:41,730 An airport known as zero is arbitrary. 34 00:02:42,790 --> 00:02:44,620 We can use other venues also. 35 00:02:44,710 --> 00:02:48,180 The desert will have the same interpretation regardless. 36 00:02:48,340 --> 00:02:49,090 These values. 37 00:02:52,130 --> 00:02:54,370 And the other variable, which is autobody. 38 00:02:54,570 --> 00:02:56,010 We had three dummy variables. 39 00:02:58,190 --> 00:03:02,540 So we received three different beta values for all the three variables. 40 00:03:04,470 --> 00:03:09,520 So if you remember and this we selected the baseline of waterboarding, none. 41 00:03:10,050 --> 00:03:16,720 That is, we said that all of these values as zero will mean that there is neither a leak. 42 00:03:16,980 --> 00:03:17,450 Nada. 43 00:03:17,670 --> 00:03:18,750 They were in the area. 44 00:03:20,360 --> 00:03:28,130 So now what is the meaning of each of these variable waterboarding lake means that compared to the situation 45 00:03:28,130 --> 00:03:32,600 where there is no water body, if there is a lake in that area. 46 00:03:33,290 --> 00:03:35,870 How much will the price increase? 47 00:03:36,840 --> 00:03:43,890 So zero point two six units will be the increase in price of house if there is a lake in the area. 48 00:03:45,690 --> 00:03:53,460 If you look at the lake and river coefficient, it is minus point six eight, which means that if there 49 00:03:53,460 --> 00:04:02,010 is both lake and river, the house price will go down in comparison to if there was no water body in 50 00:04:02,010 --> 00:04:02,490 the area. 51 00:04:05,110 --> 00:04:10,450 And the third variable, which is Water Body River, it is saying that if there is a river, the host 52 00:04:10,450 --> 00:04:13,330 price will go down in comparison to it. 53 00:04:13,420 --> 00:04:15,730 There is no waterboarding in that area. 54 00:04:17,420 --> 00:04:19,780 Next thing we have to look at is the P-value. 55 00:04:20,600 --> 00:04:23,150 All of these P values are large. 56 00:04:23,840 --> 00:04:30,140 Which means that there is no statistical evidence that there will be an impact of these three variables 57 00:04:30,260 --> 00:04:31,230 on the house price. 58 00:04:32,030 --> 00:04:35,270 So all although the relationship is given by these B does. 59 00:04:36,080 --> 00:04:43,190 But statistically, we are not confident that the relationship given by these B does actually hold or 60 00:04:43,190 --> 00:04:43,490 not. 61 00:04:46,190 --> 00:04:50,700 So this is how qualitative variables that handle an interpreter didn't Linnean model. 62 00:04:51,510 --> 00:04:56,550 We first transform them into dummy variables of and minus one categories. 63 00:04:57,420 --> 00:05:02,560 Then we run the regression, then looking at the betas and the P values. 64 00:05:02,820 --> 00:05:04,020 We interpret dessert.