garyman said:Wasn't sure which section this should be in, so please move if inappropriate. If a plot between two variables theoretically should pass through the origin, should you force the linear regression through the origin. I seem to get a much better value for my gradient when forced through the origin, and was just wondering if this is acceptable.
The type of graph you choose depends on the type of data you are working with. Bar graphs are used for comparing discrete categories, line graphs are used for showing trends over time, and scatter plots are used for showing relationships between two continuous variables. Consider the purpose of your data and choose the graph that best represents it.
Labeling the axes is important because it provides context and meaning to the data being presented. The horizontal axis (x-axis) represents the independent variable and the vertical axis (y-axis) represents the dependent variable. Labels should be clear and concise, and include units of measurement if applicable.
The scale of a graph refers to the range of values shown on the axes. It is important to choose a scale that allows the data to be easily interpreted and clearly demonstrates any patterns or trends. Generally, the scale should start at zero and increase in equal intervals.
The title of a graph should provide a brief overview of the data being presented. This helps the reader to understand the purpose of the graph and what information is being displayed. A legend is used to explain any symbols or colors used in the graph and their corresponding meaning. This makes it easier for the reader to interpret the data.
Interpreting the data on a graph involves looking at the patterns, trends, and relationships shown. This can help to identify any significant findings or conclusions. It is important to consider the context of the data and any potential limitations or factors that may have influenced the results.