# data[].attributes.body
Good Graph: This graph is good because it has both a title and a source for the data. The scales on both the x and y-axis are equal increments apart, and there is a title for the scale.

# data[].attributes.body
Good Graph: This is an example of a good graph because it is color coded which shows the variance in activities very well. This graph is labeled and clear. If you add up the percentages shown in this graph, you will find that it equal 100 percent.

# data[].attributes.body
Good Graph: This graph is good because it has a clear source for the information it presents. The graph also begins at zero and has reasonable, forthright intervals. In addition, all aspects of this graph are clearly labeled.

# data[].attributes.body
Good Graph: This graph is forthright because the numbers are lined up with tick marks on the vertical axis with a c;early indicated scale. There is a source for the information. Finally the bar sizes are scaled proportionately.

# data[].attributes.body
Bad Graph: This graph is misleading because there is no title or variable on the x-axis of the graph, so consumers don't know what the percentages mean. There is no title for the graph as a whole, which can lead to confusion as well. On the graph, the percentages go up, but the bars decrease in size, which doesn't make any logical sense.

# data[].attributes.body
Bad Graph: This graph is misleading because the scale on the vertical axis is not evenly spaced out. This causes the data to be distorted. This graph makes the differences look much larger, while they are barely a full percentage apart.

# data[].attributes.body
Bad Graph: This graph is misleading because the intervals for the average score (y-axis) are very small and distort the data. In addition, the graph's y-axis does not begin at zero. This graph also does not include a source for the data presented.

# data[].attributes.body
Bad Graph: This graph is misleading for a few reasons. First there is nothing that indicates the scale. Second, the bar sizes are not scaled proportionately. The bar for 13% is much larger than the bar for 34%. And third, the data should be in numerical order.