The video tutorial explains the Spearman rank correlation, a non-parametric counterpart to the Pearson correlation. It highlights the key difference: Spearman uses data ranks instead of raw data. An example is provided, demonstrating how to rank data and calculate the Spearman correlation. The video compares results with Pearson correlation, showing they can be equivalent when using ranks. It also covers hypothesis testing, explaining how to determine if a correlation is significant using a t-test and p-value. The tutorial concludes with a practical example, rejecting the null hypothesis based on a calculated p-value.

What is the primary difference between Spearman and Pearson correlation?

• Both use ranks.

• Both use raw data.

• Spearman uses ranks while Pearson uses raw data.

• Spearman uses raw data while Pearson uses ranks.

In the example provided, what is the first step in calculating Spearman correlation?

• Calculate the Pearson correlation.

• Assign ranks to the data.

• Plot the data on a scatter plot.

• Calculate the mean of the data.

How is the Spearman correlation coefficient calculated?

• By using the raw data values.

• By using the differences in ranks.

• By using the product of the data values.

• By using the sum of the data values.

What does a Spearman correlation coefficient of 0.9 indicate?

• A weak negative correlation.

• A strong positive correlation.

• No correlation.

• A moderate negative correlation.

What does a Spearman correlation coefficient of zero signify?

• A strong positive correlation.

• A strong negative correlation.

• No correlation.

• A moderate positive correlation.

What is the null hypothesis in hypothesis testing with Spearman correlation?

• The correlation coefficient is not equal to zero.

• The correlation coefficient is greater than zero.

• The correlation coefficient is less than zero.

• The correlation coefficient is equal to zero.

What statistical test is used to check if the correlation coefficient is significantly different from zero?

• Chi-square test

• ANOVA

• t-test

• Z-test

What is the significance level commonly used in hypothesis testing?

• 10%

• 1%

• 5%

• 0.5%

If the p-value is less than the significance level, what decision is made regarding the null hypothesis?

• Accept the alternative hypothesis.

• Reject the null hypothesis.

• Reject the alternative hypothesis.

• Accept the null hypothesis.

What does a p-value of 0.002 indicate in the context of hypothesis testing?

• The alternative hypothesis is rejected.

• The null hypothesis is rejected.

• The null hypothesis is accepted.

• The test is inconclusive.