~ Model [ Select ] ["III", "II", "I"] is the most appropriate for predicting pull strength. For the next two statements, the first one should have the lower model number and the second one should have the higher model number. If you do it the opposite way round it will be marked incorrect. ~ Model [ Select ] ["III", "II", "I"] was eliminated because the adj R2 is very low. . ~ Model [ Select ] ["I", "III", "II"] was eliminated because [ Select ] ["the R2 is very high.", "the adj R2 is almost the same as the adj R2 of the model that was selected as the most appropriate model, but it has fewer predictors.", "the adj R2 is very high.", "the R2 is very low.", "the adj R2 is very low.", "the adj R2 is almost the same as the adj R2 of the model that was selected as the most appropriate model, but it has more predictors."] .
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.
~ Model [ Select ] ["III", "II", "I"] is the most appropriate for predicting pull strength.
For the next two statements, the first one should have the lower model number and the second one should have the higher model number. If you do it the opposite way round it will be marked incorrect.
~ Model [ Select ] ["III", "II", "I"] was eliminated because the adj R2 is very low. .
~ Model [ Select ] ["I", "III", "II"] was eliminated because [ Select ] ["the R2 is very high.", "the adj R2 is almost the same as the adj R2 of the model that was selected as the most appropriate model, but it has fewer predictors.", "the adj R2 is very high.", "the R2 is very low.", "the adj R2 is very low.", "the adj R2 is almost the same as the adj R2 of the model that was selected as the most appropriate model, but it has more predictors."] .
Trending now
This is a popular solution!
Step by step
Solved in 2 steps
