This function takes in a dataframe, the dependent variable, and optionally a character vector of independent variables you want the function to ignore, and produces a regression plot of every variable compared to the dependent variable passed into the function. It will ignore columns which contain characters and (also optional) factors.

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`data` |
data.frame object that contains both the dependent variable and predictor variables you want to plot. |

`dv.var` |
single dependent variable you want to plot your predictors against. |

`ignore` |
accepts a character vector of one or more variables you want the function to skip. If nothing is passed through this option, the function will attempt to create a graph plotting the dependent variable and every other column of data. |

`save` |
accepts a character. If the function recieves a character, it will create a pdf file with that name and leave the plots in there. |

`include.factors` |
if TRUE, will also plot factor variables against your dv. Otherwise it will skip these as regression plots of categorical variables are of imited use. |

`include.se` |
if left TRUE, will shade the area around the regression line with the 95% confidence interval range. Setting to FALSE will plot only the regression line to a scatter plot for each paring of variables. |

Doesn't return a value, per se, but will generate side effects like plotting all the graphs created by the function. If the save option is used, it will save all generated graphs to a pdf file whose name is specified by the user.

1 2 3 | ```
exam.df <- iris
massregplot(exam.df, "Sepal.Length", ignore = "Species")
massregplot(exam.df, "Sepal.Length", ignore = c("Species", "Petal.Width"), include.se = FALSE)
``` |

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