Vanessa Kehdi
November 8, 2017
Statistics 352
Lab Assignment 5
Problem 1:
The null hypothesis is that there is no association between the political party affiliation and the opinion held of President Obama’s performance. The alternative hypothesis is that there is an association between the political party affiliation and the opinion held of President Obama’s performance.
Based on the bar chart, there is an association between the political party and President Obama’s opinion because there was a higher percentage of the democrats that approved of his opinions while republicans did not. On the other hand, there was a lower amount of democrats that disapproved of his performance and a higher amount of republicans that disapproved of Obama’s …show more content…
The relationship would not be linear (there is a gap), the direction leans towards a positive direction, and the strength is weak because the dots on the plot are more concentrated in one area rather than spread out between age and reaction time.
It would not be appropriate to find the correlation coefficient because it is not linear. Therefore, you cannot describe the strength and the direction of the two variables.
The correlation coefficient would be 0.3340977.
There are outliers within the data. With these outliers, it should be checked to see if there is a data entry error. If there is not an entry error, then the outlier can be removed only if it is for a non-statistical reason.
When looking at the scatterplot, it does not appear that the regression line fits well. The data is not spread out properly enough on the line itself. They seem to be more concentrated on the left side of the graph. It seems like it is mainly between the ages of 15-25. There is a large gap between 30-45 years …show more content…
The 0.01610 would be the regression line slope and x would be any value of the explanatory variable.
As an individual ages by 1 year, then the reaction time is expected to increase by
5 percentage of the variation is explained by the reaction time give.
My reaction time was 336 milliseconds. With the regression model, my expected age would be 38.873.
It would be dangerous to use the least-square regression equation in order to predict the age of the student with a reaction time of 1500 milliseconds since it is way out of the dataset. It would be considered extrapolation for that
November 8, 2017
Statistics 352
Lab Assignment 5
Problem 1:
The null hypothesis is that there is no association between the political party affiliation and the opinion held of President Obama’s performance. The alternative hypothesis is that there is an association between the political party affiliation and the opinion held of President Obama’s performance.
Based on the bar chart, there is an association between the political party and President Obama’s opinion because there was a higher percentage of the democrats that approved of his opinions while republicans did not. On the other hand, there was a lower amount of democrats that disapproved of his performance and a higher amount of republicans that disapproved of Obama’s …show more content…
The relationship would not be linear (there is a gap), the direction leans towards a positive direction, and the strength is weak because the dots on the plot are more concentrated in one area rather than spread out between age and reaction time.
It would not be appropriate to find the correlation coefficient because it is not linear. Therefore, you cannot describe the strength and the direction of the two variables.
The correlation coefficient would be 0.3340977.
There are outliers within the data. With these outliers, it should be checked to see if there is a data entry error. If there is not an entry error, then the outlier can be removed only if it is for a non-statistical reason.
When looking at the scatterplot, it does not appear that the regression line fits well. The data is not spread out properly enough on the line itself. They seem to be more concentrated on the left side of the graph. It seems like it is mainly between the ages of 15-25. There is a large gap between 30-45 years …show more content…
The 0.01610 would be the regression line slope and x would be any value of the explanatory variable.
As an individual ages by 1 year, then the reaction time is expected to increase by
5 percentage of the variation is explained by the reaction time give.
My reaction time was 336 milliseconds. With the regression model, my expected age would be 38.873.
It would be dangerous to use the least-square regression equation in order to predict the age of the student with a reaction time of 1500 milliseconds since it is way out of the dataset. It would be considered extrapolation for that