Reading and Interpreting Graphs
The usage of graphs are essential in the illustration and comprehension of statistical output. They type of graphs used depends on the author and the view they are trying to portrait. Graphs can be used to prove or disprove a theory based on its presentation.
The Following graph is used as a visually depiction of a sample of grade school childern of whom an IQ exam was performed.
Figure: The histogram figures shown are extracts of an IQ test performed on elementary school subjects and is graphed with a normal curve.
Judging from the raw data provided and the visual inspection of this graph, it is easy to conclude without prejudice that the male (represented by the number 1) subjects of this research tend to score better on the exam than that of its female counterpart (represented by the number 2). However, the male output is more skewed in its distribution where the symmetry of the female output has more equal and distinct meaning when viewing the kurtosis of the graph and hover around the mean of about 100.
Although the Male output is more diverse, its concentration of variables is more spread out. I think we should take note that the amounts of female subjects were far lesser than the male so the raw visually data seems bias and in favor of the male.
I also believe that we cannot definitively gain an accurate conclusion by visual inspection alone. Looking further into the dataset, we can see the modality for both male and female is around 100. This suggests that the two sets are relatively close rather than one being superior. Looking at the graph, we cannot possibly make this determination.
In this scenario, the dependent variables (what we are studying) are the students themselves and their individual results. The independent variables (what is being administered or modified) are the tests and the contents of the tests, which is the controlled aspect of the experiment.
The male data skewed in its presentation because the amounts of
The Following graph is used as a visually depiction of a sample of grade school childern of whom an IQ exam was performed.
Figure: The histogram figures shown are extracts of an IQ test performed on elementary school subjects and is graphed with a normal curve.
Judging from the raw data provided and the visual inspection of this graph, it is easy to conclude without prejudice that the male (represented by the number 1) subjects of this research tend to score better on the exam than that of its female counterpart (represented by the number 2). However, the male output is more skewed in its distribution where the symmetry of the female output has more equal and distinct meaning when viewing the kurtosis of the graph and hover around the mean of about 100.
Although the Male output is more diverse, its concentration of variables is more spread out. I think we should take note that the amounts of female subjects were far lesser than the male so the raw visually data seems bias and in favor of the male.
I also believe that we cannot definitively gain an accurate conclusion by visual inspection alone. Looking further into the dataset, we can see the modality for both male and female is around 100. This suggests that the two sets are relatively close rather than one being superior. Looking at the graph, we cannot possibly make this determination.
In this scenario, the dependent variables (what we are studying) are the students themselves and their individual results. The independent variables (what is being administered or modified) are the tests and the contents of the tests, which is the controlled aspect of the experiment.
The male data skewed in its presentation because the amounts of