¿Cuáles son los mejores tipos de gráficos para mostrar la relación entre dos variables?

Scatter Plots are ideal for displaying the correlation or distribution between two continuous variables. Line Charts are effective for showing trends or changes over time between two related variables.

Bubble Chart: Bubble charts extend the concept of a scatter plot by adding a third variable as the size of the bubbles. This allows you to visualize relationships between three variables. The choice of chart type should be driven by the specific context of your data and the relationship you want to highlight. Consider the data types, the number of variables, and the insights you want to convey to select the most appropriate chart type.

Scatter plots are a type of graph that shows the relationship between two variables. They are used to visualize how the values of one variable change with respect to the values of another variable. Scatter plots are created by plotting the values of one variable on the horizontal axis and the values of the other variable on the vertical axis. Each point on the scatter plot represents a single data point. The pattern of the points on the scatter plot can reveal the relationship between the two variables. For example, a scatter plot of the height and weight of a group of people might show a positive correlation. This means that taller people tend to be heavier.

The answer really lies in the eye's of the beholder, if you will. Every person has their own learning style and method of viewing analytics. I prefer line graphs as it is like following the yellow brick road. Have a great weekend!!

In my experience, the beauty of scatter plots is their adaptability to different needs. They can illustrate linear correlations where points align along a straight line, or non-linear relationships where the points may form curves or clusters.

I have found line charts to be a valuable tool for tracking the relationship between two variables over time. For example, I use line charts to compare the number of sign-ups for a specific service with advertising spend. This allows me to identify the correlation between spend and customer acquisition, which can help me to optimize my advertising campaigns.

One thing I've found helpful when you want to study the impact of one variable on another over time is a line chart. For example, you can use a line chart to show how marketing expenditures influence sales figures month by month, providing a clear picture of cause-and-effect relationships.

Line charts are used to show the trends and patterns of data. It is used when we wanted to see the trend of a particular variable-the behaviour between specific time period. Line charts are used for tracking purposes. It is continuous.

Line chart is best for comparing relationship between two variables. Further, to study correlation one can go for scattered chart for measuring corel.

When I teach chart types, the chart is about helping answer the question. For example, is the question about something measured over time? Then that's a great indication that you need to create a line chart. One point to be aware of though. A general best practice is not to truncate the axis. In some cases, it might be appropriate to do so. If that's the case with your data, then review the chart after truncating it to see if it distorts what you're trying to convey.

Bar charts can reveal the distribution of data, such as frequency distributions and histograms. In my experience, they are particularly useful when dealing with discrete variables, as they can showcase the frequency of each category or range, helping you identify patterns and outliers.

Bar charts are used to depict the categorical variables represented by rectangular bars. Length of the bar represents the value of a variable. Example: Sales by state or Sales by Zone. Basically it is used to compare the values between groups/categories.

By showcasing the distribution or comparison of values through distinct bars, this chart type excels in providing a clear, side-by-side representation. Whether using horizontal or vertical bars, stacked or grouped, bar charts offer a versatile solution for conveying the interplay between variables, making them valuable in scenarios like comparing sales across different regions or understanding customer satisfaction by product categories.

Bar charts are ideal for performance dashboards. It provides the message clearly for both expert and novice users equally. For novice users the pattern itself gives the information whereas the expert ones can benefit from the pattern and the values.

If you are looking at a chart for simplicity and definition, bars are immersive blocks of the whole picture. Comparing and stacking have never been easier.

Pie charts are ideal to be used when percentage wise distribution of data has to be shown lets say in market segmentation, in my experience they are highly effective especially in retail reports for instance to show a product wise category split distribution .

Pie chart is also used to compare the categorical values but it is represented in a circular form and the percentages. It is mainly used when we want to see the contributions of different groups/segments. Example: Revenue contribution by product category, Sales contribution by country/state.

Pie charts can be used to show the relationship between two variables, but they are not the best choice for this task. Pie charts are best suited for showing the parts-to-whole relationship of a single variable. If you are trying to show the relationship between two variables, a scatter plot or a bar chart would be a better choice. Scatter plots show the relationship between two continuous variables, while bar charts show the relationship between two categorical variables. However, there are some cases: For example, you could use a pie chart to show the distribution of a variable within different categories. For example, you could use a pie chart to show the percentage of students in different grade levels who passed a exam.

Pie charts have certain limitations and lack the versatility of other visualization tools like bar charts, line charts, or scatter plots. In contemporary data visualization practices, experts advise avoiding the use of pie charts whenever possible. If you find yourself needing to depict relationships between parts and the whole, consider a donut chart instead. Donut charts offer a cleaner and more intuitive representation compared to traditional pie charts.

I have never used pie charts to show relationships between two variables. My typical use case would be to represent a dataset with limited categories which is not required a detailed comparison. That said (I have done a bit of research to answer this question) pie chart could be used to show relationships but the use cases are really complex and sometimes require a lot of work to implement.

Heat maps are a great way to visualise categorical variables especially when these variables have more discrete values. For example, visualising population working in different industry sector in every state of the country.

Heatmaps are a great way to visualize the relationship between two variables. They are especially useful for exploring relationships between large numbers of variables. To create a heatmap, you first need to calculate a correlation matrix. The correlation matrix is a square matrix that shows the correlation between each pair of variables. The correlation coefficient is a measure of the strength and direction of the linear relationship between two variables. Once you have calculated the correlation matrix, you can use it to create a heatmap. The heatmap will show the correlation between each pair of variables, with the color of each cell indicating the strength of the correlation.

I love heat maps from the perspective that they allow an understanding of trending. Two great applications for all business users are the following: 1. Highlighting a column of results in a spreadsheet and creating a heat map from it, to understand trending of the data. 2. Using a visualization tool like Python or Tableau to visualize data in more interactive manners (e.g. per customer, zip code, state)

Emphasis should be made on the color scheme. 1 color gradient vs 2 color gradient. You use one color when you have all positive values (grey to darker red/blue/etc.) You use 2 color when you have negative values (blue[negative] to red[positive], or reverse the colors -- context is key)

Heatmaps are useful for showing the relationship between two variables in a grid format. They use color to represent the strength of the relationship, making it easy to spot patterns and correlations.

Some other charts which I found helpful were - Box plots - It helps to discover interquartile range, median and outliers in the data before performing any find of regression - Correlation charts - It could be helpful to deal with knowing the positive or negative relationship between the variables - Word cloud - It will be useful for visual representation of textual data in the form of most recurring texts.

Other than chart one should consider correlation: it’s a table that shows the correlation coefficients between multiple pairs of variables, indicating the strength and direction of relationships. For further enhancements, such matrix can be clubbed with heat map kinda colour coding to have better visualisation.

In my experience, I prefer to look at the data set, context of establishing / comparing relationship, objective(s) of preparing charts, visualization need(s), audience, etc. before selecting a chart type. In summary, storyline in totality is more important in conveying the message. A chart should only be considered as an enabler. Each types has its merits and can be selectively used in story-telling.

In my opinion, it completely depends on what kind of variables you are working with. 1. Both categorical variables - Stacked bar charts will be good 2. Both numerical variables - scatter plot are your friend 3. 1 Categorical and 1 Numerical Variable - Bar Charts, Histogram can be helpful

.. In addition to the great chart types mentioned, it's essential to consider your audience. Tailor your choice to what your audience is most comfortable with and what will convey your message clearly. Also, be mindful of data integrity and ensure your variables are correctly measured and collected to avoid misleading interpretations. Lastly, experiment with different chart types to see which one best highlights the relationship between your specific variables.