Dalam artikel ini, kita akan membahas hubungan antara persentase lemak tubuh remaja perempuan dan indeks massa tubuh (BMI). Kita akan menggunakan grafik scatterplot untuk mengevaluasi hubungan antara dua variabel tersebut.

Grafik scatterplot biasanya terdiri atas beberapa elemen:

• Garis X yang mewakili nilai dari variabel kontinu. Dalam hal ini, garis X mewakili indeks massa tubuh (BMI).
• Garis Y yang mewakili nilai dari variabel kontinu. Dalam hal ini, garis Y mewakili persentase lemak tubuh remaja perempuan.
• Simbol-simbol yang digambar di titik koordinat (X, Y) data. Opsional, grafik dapat menggunakan simbol berwarna atau bentuk yang berbeda untuk mewakili grup-grup yang sama.

Grafik scatterplot menunjukkan hubungan positif yang kuat dan moderat antara BMI dan persentase lemak tubuh remaja perempuan. Semakin tinggi nilai BMI, semakin tinggi pula persentase lemak tubuh remaja perempuan. Hubungan ini tampaknya mengikuti kurva karena ia menjadi lebih lurus untuk nilai BMI yang lebih tinggi. Untuk menggambarkan ketercuraman ini, analis menggunakan suku kuadrat dalam model.

Interpreting Scatterplots and Assessing Relationships between Variables

Grafik scatterplot menampilkan arah, kekuatan, dan linearitas hubungan antara dua variabel. Hubungan positif terjadi ketika nilai-nilai variabel meningkat bersama-sama. Contohnya, hubungan antara tinggi badan dan berat badan memiliki hubungan positif.

Namun, jika salah satu variabel meningkat seiring lainnya menurun, maka itu adalah hubungan negatif. Contoh di bawah ini menunjukkan hubungan negatif antara BMI dan persentase lemak tubuh remaja perempuan:

Kekuatan Hubungan

Hubungan yang lebih kuat menghasilkan clustering data yang lebih ketat. Perlu diingat bahwa perubahan skala dapat berpengaruh pada kekuatan hubungan yang tampak. Koeffisien korelasi memberikan penilaian objektif kekuatan hubungan yang independen dari skalanya.

Grafik-grafik di atas menunjukkan data-point yang lebih ketat dalam grafik pertama daripada grafik kedua. Oleh karena itu, dataset pertama menunjukkan hubungan yang lebih kuat.

Hubungan Linear dan Curved

Determine whether your data have a linear or curved relationship. When a relationship between two variables is curved, it affects the type of correlation you can use to assess its strength and how you can model it using regression analysis.

Adding a fit line highlights how well the model fits your data. When a relationship exists, you might want to model it using regression analysis.

Determine Whether the Relationship Changes between Groups

When your data have groups, you can determine whether the relationship between two variables differs between the groups. To make these comparisons, you'll need a categorical variable that defines the groups. All groups must use the same X and Y measurements.

In this scatterplot, the slope of the relationship is the same for the two groups, but the output values of group B are consistently higher for any given input value.

In this scatterplot, the slope for group B is steeper than for group A. As the input value increases, the output for group B increase more quickly than group A.

Use indicator variables and interaction terms in a regression model to test the statistical significance of these differences. Click the link below for details.

Find Outliers and Unusual Observations with Scatterplots

Scatterplots can help you find multiple types of outliers.

Some outliers have extreme values. These outliers are distanced from other data points, as shown below.

Unusual observations have values that are not necessarily extreme, but they do not fit the observed relationship. In the scatterplot below, the circled point has X and Y values that are not unusual. However, the combination of the two values clearly does not fit the overall relationship.

Trends Over Time

Typically, analysts use time series plots to display data over time. However, you can also use scatterplots for this purpose. Scatterplots are a perfect choice for time-related data when your observations occur at irregular intervals. When creating a scatterplot for time data, be sure to add a connect line between the data points!

Use Scatterplots with the Appropriate Statistical Analysis

In order to draw meaningfuls from a scatterplot, it is important to use the appropriate statistical analysis. This may include calculating the correlation coefficient, testing for significance, and using regression analysis to model the relationship.

By carefully considering these factors and using scatterplots as part of a comprehensive statistical analysis, you can gain valuable insights into the relationships between your data and make informed decisions about your research or business endeavors.