##### Code for Basic linear Regression in R


### Importing data


#Don't call your data 'data' - that word already has a meaning in R

datum=read.csv(file.choose()) ### creates a data frame named datum from csv file


### check data was imported properly


head(datum) ### column headers of data file plus first 6 rows of data 



### Create a scatterplot of data
#plot(Y~X,data=datum) ### creates a scatter plot between 'Y' and 'X' variables
#Replace 'Y' and 'X' with whatever those columns are actually called

plot(Mass~Protein,data=datum) ### scatterplot of data used in example


### Run regression using linear model
#results=lm(Y~X,data=datum) ### runs a regression between 'Y' and 'X'
#Creates and object called 'results' that hold results of analysis
#Replace 'Y' and 'X' with whatever those columns are actually called


results2=lm(Mass~Protein,data=datum) ### linear regression of example data 
summary(results) ### prints a summary of 'results'



anova(results2) ### generate an anova table of regression 



### Generate confidence intervals for parameters

confint(results2) #calculates confidence limits on slope and intercept



### Plot regression line over data
# requires either a lm object or values for a (intercept) and b (slope)

abline(results2) ### plots results on the already created plot

# Note must have run regression first
# Note won't do anything if you haven't plotted data.