The following bivariate data set contains an outlier. x y 37.5 99 58 43.5 30 118.7 34 60.3 35.1 81.5 12.9 116 49.1 59.8 56.4 54 2.7 57.6 24 94.8 9.3 104.4 29.2 99.5 28.9 39.5 46.3 73.4 223.9 356.3 What is the correlation coefficient with the outlier? rw = What is the correlation coefficient without the outlier? rwo = For the next questions, I want you to consider that there is more than the existence or non-existence of correlation. You can have: Strong positive correlation Moderate positive correlation No correlation Moderate negative correlation Strong negative correlation Would inclusion of the outlier change the evidence for or against a significant linear correlation at 5% significance? No. Including the outlier does not change the evidence regarding a linear correlation. Yes. Including the outlier changes the evidence regarding a linear correlation. Would you always draw the same conclusion to the above question with the addition of any outlier? Yes, any outlier would result in the same conclusion. No, a different outlier in a different problem could lead to a different conclusion.
Inverse Normal Distribution
The method used for finding the corresponding z-critical value in a normal distribution using the known probability is said to be an inverse normal distribution. The inverse normal distribution is a continuous probability distribution with a family of two parameters.
Mean, Median, Mode
It is a descriptive summary of a data set. It can be defined by using some of the measures. The central tendencies do not provide information regarding individual data from the dataset. However, they give a summary of the data set. The central tendency or measure of central tendency is a central or typical value for a probability distribution.
Z-Scores
A z-score is a unit of measurement used in statistics to describe the position of a raw score in terms of its distance from the mean, measured with reference to standard deviation from the mean. Z-scores are useful in statistics because they allow comparison between two scores that belong to different normal distributions.
The following bivariate data set contains an outlier.
| x | y |
|---|---|
| 37.5 | 99 |
| 58 | 43.5 |
| 30 | 118.7 |
| 34 | 60.3 |
| 35.1 | 81.5 |
| 12.9 | 116 |
| 49.1 | 59.8 |
| 56.4 | 54 |
| 2.7 | 57.6 |
| 24 | 94.8 |
| 9.3 | 104.4 |
| 29.2 | 99.5 |
| 28.9 | 39.5 |
| 46.3 | 73.4 |
| 223.9 | 356.3 |
What is the
rw =
What is the correlation coefficient without the outlier?
rwo =
For the next questions, I want you to consider that there is more than the existence or non-existence of correlation. You can have:
- Strong
positive correlation - Moderate positive correlation
- No correlation
- Moderate
negative correlation - Strong negative correlation
Would inclusion of the outlier change the evidence for or against a significant
- No. Including the outlier does not change the evidence regarding a linear correlation.
- Yes. Including the outlier changes the evidence regarding a linear correlation.
Would you always draw the same conclusion to the above question with the addition of any outlier?
- Yes, any outlier would result in the same conclusion.
- No, a different outlier in a different problem could lead to a different conclusion.
Trending now
This is a popular solution!
Step by step
Solved in 2 steps with 1 images
