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So it's similar to how no matter what machine learning model in psych you learn you use they all have the fit and functions so once you learn how to plug your data into one test will be similar for the other ones too. One thing to note about these web designing course non-parametric tests is that because they make fewer assumptions they have less statistical power than if you had done something like the T-test what this means is that there would need to be a more the extreme difference in your two groups to get a statistically significant P-value.

We saw this earlier as well since the one-sided T-test makes more assumptions than the two-sided T-test the one-sided T-test has more statistical power. One of the assumptions we looked at but did not make was the fact that we could do web designing course in delhi a one-sided test. We instead chose to do a two-sided test for comparing heights. We could have used the one-sided test because we already know that men are taller than women on average.

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• The effect on the P-value is that it's easier to achieve a significant difference because we're no longer multiplying our area under the curve by two. But there are some cases where you absolutely do not want to assume you're doing a one-sided test.
• Think about it you're doing drug testing you might intuitively think that you want to measure the improvement. This drug makes in the patient's condition. But ethically we need to test web designing course for the opposite scenario as well. If the drug worsens the patient's condition that's a bad thing.
• However, if you have a drug that already works effectively and you want to test if another drug works more effectively then you can use a one-sided test for that. So to summarize the point of this the process was just to show you a simple example of how frequent statistical testing works.
• We generate a test for autistic and from that, we know it's distribution. We look to see if the tested cystic is at the extreme values of the distribution to see if what we measured is statistically significant and if it is statistically significant we reject the hypothesis. Welcome back to this class is the web designing course machine learning in Python Part in this lecture we are going to write the T-test in code. If you don't want the code alone you just want to run the code the relevant file is T-test up high.

1. These both have variants. So for this first part, we're going to roll our own T-test. So we're going to calculate the variance of. So that's eight of ours. And now by defaulting them, pi does the maximum web designing course likelihood estimate of the variance. So that's dividing by and we want the unbiased estimate which divides by and minus.
2. So we pass indeed off equals and for B we do the same thing. The next step is to calculate the pooled standard deviation. So that's the square root of the variance of the divide by two. The next step is to calculate the statistic. So this is Ada I mean minus beat.
3. I mean we divide by as times the square root of two over and next step is to calculate the degrees of freedom we have to pass web designing course that into the t distribution ZDF. So there just two times and minus two. Let's get the P-value. So that's one minus stat start Tiede CTF. We pass in the t statistic and the degrees of freedom is equal to DF and let's print out the result.
4. So we'll print out the t statistic and print out p. We are almost done if to multiply by and that is the p-value. And just to compare with the built inside the pi function that does this will get T- and P from stats that T-test and so instance for an independent pass in and B and print the same information T- and Petah we don't need to multiply that by because it's already done by SIPO.
5. So let's run this and see if we get close so we get the same answer for both cases. Everyone and welcome back to this web designing course class Bazy in machine learning in Python. In this lecture, we are going to do an exercise on applying the T-test to some data contrary to what we discussed in the previous lectures.

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Created on May 31st 2019 06:48. Viewed 410 times.

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