00:02 Pearson correlation measures the linear relationship between two variables. 01:02 Car correlation is determined by R values 02:02 Pearson correlation coefficient measures the linear relationship between two variables. 03:08 Sign of values affects correlation coefficient 04:06 Pearson correlation measures the strength and direction of the relationship between two variables. 05:03 Testing the correlation coefficient for significance 06:02 Calculating p-value and significance level for rejecting null hypothesis 07:00 Testing significance of Pearson correlation coefficient
Very Nice explanation... I would like to confirm if your name is Dr. Hannah Volk-Jessusek... because i want to put your video as a reference for my study.
Gemini: The Pearson correlation coefficient is a measure of the linear relationship between two variables. It ranges from -1 to 1, with 0 indicating no correlation, 1 indicating a perfect positive correlation, and -1 indicating a perfect negative correlation. The strength of the correlation can be interpreted using a table, with values between 0 and 0.1 indicating no correlation, and values between 0.7 and 1 indicating a very strong correlation. A positive correlation exists when large values of one variable go along with large values of the other variable, or when small values of one variable go along with small values of the other variable. A negative correlation exists when large values of one variable go along with small values of the other variable, and vice versa. The Pearson correlation coefficient is calculated using a formula that takes into account the mean values of the two variables and the individual values of each variable. The correlation coefficient is usually calculated with data taken from a sample, but we often want to test a hypothesis about the population. In this case, we want to know if there is a correlation in the population, and we check whether the correlation coefficient in the sample is statistically significantly different from zero. The null hypothesis in the Pearson correlation is that the correlation coefficient does not differ significantly from zero, and the alternative hypothesis is that the correlation coefficient differs significantly from zero. The assumptions for the Pearson correlation are that the two variables must be metric variables and that they must be normally distributed. If these assumptions are not met, the calculated test statistic t or the p-value cannot be interpreted reliably.
Thanks for the video! Though I'm slightly confued. Wouldn't the numerator always equal 0? As the sum of all values minus the number of values times the mean is 0, or is my math bad?
is the strength of the correlation affected by sample size? if I took small sample out of the larger sample, would the strength of the correlation change?
This explanation is not very clear. Could nominal, ordinal scale use pearson correlation? As I know the mention scale cannot pearson but use Spearman Rank for ordinal....
hmm, you can do it between two variables, you just want to know it there is a relation ship between the Variables. So if you want to know if there is a correlation between two dependent Variables for sure you can do it! Regards, Hannah
![Spearman Rank Correlation [Simply explained]](http://i.ytimg.com/vi/XV_W1w4Nwoc/mqdefault.jpg)
![Spearman Rank Correlation [Simply explained]](/img/tr.png)
If you like, please find our e-Book here: datatab.net/statistics-book 😎
I really want to express how grateful I am to find this video. Thank you!
Very simple and effective explanation I tried so many videos but this one finally cleared my concept!
00:02 Pearson correlation measures the linear relationship between two variables.
01:02 Car correlation is determined by R values
02:02 Pearson correlation coefficient measures the linear relationship between two variables.
03:08 Sign of values affects correlation coefficient
04:06 Pearson correlation measures the strength and direction of the relationship between two variables.
05:03 Testing the correlation coefficient for significance
06:02 Calculating p-value and significance level for rejecting null hypothesis
07:00 Testing significance of Pearson correlation coefficient
Many thanks!
Thank you for the clear explanation! It helps me understand mathematics that I use in machine learning and data analysis.
Very Nice explanation... I would like to confirm if your name is Dr. Hannah Volk-Jessusek... because i want to put your video as a reference for my study.
Simple and clearly explained 👌
Thanks : )
Yes!
this is such a great explanation. Well doen
Excellent presentation.
Thanks for sharing this knowledge!!
Glad it was helpful!
Thank you. Very clear explanation.
Im maths novice in my career at this point but this video helped me allot, Thank you :)
Glad it helped!
Loving the concise explanation..
I will certainly be your subcriber ds year. Thank you for such indepth explanation
Well explained!
Gemini: The Pearson correlation coefficient is a measure of the linear relationship between two variables. It ranges from -1 to 1, with 0 indicating no correlation, 1 indicating a perfect positive correlation, and -1 indicating a perfect negative correlation. The strength of the correlation can be interpreted using a table, with values between 0 and 0.1 indicating no correlation, and values between 0.7 and 1 indicating a very strong correlation. A positive correlation exists when large values of one variable go along with large values of the other variable, or when small values of one variable go along with small values of the other variable. A negative correlation exists when large values of one variable go along with small values of the other variable, and vice versa. The Pearson correlation coefficient is calculated using a formula that takes into account the mean values of the two variables and the individual values of each variable. The correlation coefficient is usually calculated with data taken from a sample, but we often want to test a hypothesis about the population. In this case, we want to know if there is a correlation in the population, and we check whether the correlation coefficient in the sample is statistically significantly different from zero. The null hypothesis in the Pearson correlation is that the correlation coefficient does not differ significantly from zero, and the alternative hypothesis is that the correlation coefficient differs significantly from zero. The assumptions for the Pearson correlation are that the two variables must be metric variables and that they must be normally distributed. If these assumptions are not met, the calculated test statistic t or the p-value cannot be interpreted reliably.
excellent explaination
Thankyou for the wonderful explanation. 👍
You are welcome!
Thanks for the video! Though I'm slightly confued. Wouldn't the numerator always equal 0? As the sum of all values minus the number of values times the mean is 0, or is my math bad?
Very informative video
is the strength of the correlation affected by sample size? if I took small sample out of the larger sample, would the strength of the correlation change?
thank you. But please I search the proof of the standard error of Pearson R formula but I don't find this information. Can you help me please
beautiful presentation slides
I like your videos. Not tough to understand
Thanks : )
Can we still use pearson correlation if the data is base on likert scale (1-5)?
Is it only for linearly dependent variables?
Let's put it this way, it can only detect linear relationships. Regards Hannah
can I get the presentation file?
This explanation is not very clear. Could nominal, ordinal scale use pearson correlation? As I know the mention scale cannot pearson but use Spearman Rank for ordinal....
Can this statistical analysis be used to i dentify relationships between crime and immigration?
can this be used to find relationship between two dependent variable?
hmm, you can do it between two variables, you just want to know it there is a relation ship between the Variables. So if you want to know if there is a correlation between two dependent Variables for sure you can do it! Regards, Hannah
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Boring!!!!!! I want ice cream!!!
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