Video Transcript
And the question, this is the style examining the relationship between two variables A and B. A researcher compute the Pearson correlation coefficient at 1.35. So which of the given statement is true here. Alright, so basically here the correct statement is that the researcher because incorrectly calculated the relationship between A. And B. Right? Why is it? So because a correlation coefficient value it can it lies within oneness one and Plus one, and it cannot be greater than one. Right? So here the Pearson correlation coefficient is 1.35, which is not possible. So there is some miscalculation uh done by the researcher, right? That will be the answer of the given question of this episode. Thank you for watching.
Correlation is a statistical tool that shows the association between two variables. Regression, on the other hand, evaluates the relationship between an independent and a dependent variable. Below is a list of multiple-choice questions and answers on Correlation and Regression to understand the topic better. Answer: d
- The coefficient of correlation is not dependent on the change of scale
- The coefficient of correlation is not dependent on the change of origin
- The coefficient of correlation is not dependent on both the change of scale and change of origin
- None of the above
Answer: c
- It is a bivariate analysis
- It is a multivariate analysis
- It is a univariate analysis
- Both a and c
Answer: c
- The correlation is said to be non-linear
- The correlation is said to be linear
- The correlation is said to be negative
- The correlation is said to be positive
Answer: d
- The correlation is said to be linear
- The correlation is said to be non-linear
- The correlation is said to be positive
- The correlation is said to be negative
Answer: d
- Standard error
- Correlation
- Regression
- None of the above
Answer: c
- It is less than the correlation coefficient
- It is equal to the correlation coefficient
- It is greater than or equal to the correlation coefficient
- It is greater than the correlation coefficient
Answer: d
- It is a significant estimation of the problem
- It is a rule for acceptance or rejection of the hypothesis of the research problem
- It is a method of making a significant statement
- None of the above
Answer: b
- Any wrong decision related to the null hypothesis results in two types of errors
- Any wrong decision related to the null hypothesis results in one type of an error
- Any wrong decision related to the null hypothesis results in four types of errors
- Any wrong decision related to the null hypothesis results in three types of errors
Answer: a
- Type two error means to accept an incorrect hypothesis
- Type two error means to reject an incorrect hypothesis
- Type two error means to accept a correct hypothesis
- Type two error means to reject a correct hypothesis
Answer: a
- In testing a hypothesis, we take the level of significance as 2% if it is not mentioned earlier
- In testing a hypothesis, we take the level of significance as 1% if it is not mentioned earlier
- In testing a hypothesis, we take the level of significance as 10% if it is not mentioned earlier
- In testing a hypothesis, we take the level of significance as 5% if it is not mentioned earlier
Answer: a
- Linear regression analysis
- Multiple regression analysis
- Non-linear regression analysis
- None of the above
Answer: a
- A regression line is also known as the line of the average relationship
- A regression line is also known as the estimating equation
- A regression line is also known as the prediction equation
- All of the above
Answer: d
- The correlational analysis between two sets of data is known as a simple correlation
- The correlational analysis between two sets of data is known as multiple correlation
- The correlational analysis between two sets of data is known as partial correlation
- None of the above
Answer: a
- Alternate hypothesis
- Null hypothesis
- Both a and b are incorrect
- Both a and b are correct
Answer: b
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