To create a scatterplot with regression fit lines and groups, complete the steps for the option that best describes your data.

Complete the following steps if your groups are defined by values in a grouping variable, or unique combinations of values in multiple grouping variables.

- Specify the pairs of variables to appear on your graph.
- In Y variables, enter the column that you want to explain or predict.
- In X variables, enter a corresponding column that might explain or predict changes in the Y variable.

- In Categorical variables for grouping (0-3), enter up to three columns that define the groups.

In this worksheet, Weight is the Y variable and Height is the X variable. Gender is the categorical variable for grouping. The graph shows the relationship between height and weight for each gender, which is represented by the fitted regression lines.

C1 | C2 | C3 |
---|---|---|

Height | Weight | Gender |

66.0 | 140 | M |

61.0 | 140 | F |

72.0 | 145 | M |

... | ... | ... |

Complete the following steps if you have multiple pairs of numeric or date/time columns and each pair is a group.

- Specify the pairs of variables to appear on your graph.
- In Y variables, enter multiple columns that you want to explain or predict.
- In X variables, for each Y variable, enter the corresponding numeric column that might explain or predict changes in the Y variable.

- Select X-Y pairs form groups.

In this worksheet, M_Weight is the first Y variable and M_Height is the corresponding X variable. F_Weight is the second Y variable and F_Height is the corresponding X variable. Each set of Y and X variables forms a group. The graph shows the relationship between height and weight for each gender, which is represented by the fitted regression lines.

C1 | C2 | C3 | C4 |
---|---|---|---|

M_Height | M_Weight | F_Height | F_Weight |

66.00 | 140 | 61.00 | 140 |

72.00 | 145 | 66.00 | 120 |

73.50 | 160 | 68.00 | 130 |

... | ... | ... | ... |