Final answer:
To compare the shapes of expected and actual distributions in bar charts, one should describe the shape of each chart by identifying features like peaks and uniformity. Changing the number of bars affects the appearance and detail of the distribution. Bar charts are effective for comparisons due to their visual clarity, and slopes in graphs indicate different types of changes or trends.
Step-by-step explanation:
To compare the shape of the expected and actual distributions by glancing at bar charts, one should first describe the shape of each graph by identifying features such as uniformity, skewness, or the presence of peaks (modes). For instance, if one graph has a single peak in the middle and slopes down on both sides, this might be described as a bell curve or normal distribution. If another has most of its bars of similar height, it could be described as a uniform distribution.
Changing the number of bars, which represents different class intervals or categories, could change the shape by making the distribution appear more detailed with smaller intervals or more generalized with larger ones. The overall outline might appear smoother or more jagged based on how many bars are used. If the number of bars is too small, it may obscure important features of the data, while too many bars may result in excessive noise and overcomplication.
A bar chart is best to illustrate comparisons because it clearly shows differences in the heights (or lengths) of the bars representing different categories or numerical intervals side-by-side, which makes it easier to visually assess differences in frequency or count between categories.
The appearance of positive slope in a graph indicates an increase as one moves from left to right, negative slope indicates a decrease, and zero slope suggests that there is no change; the line is flat and horizontal.