### Code that allows you to visually examine the assumptions of regression

### Run regression - most plots require regression analysis in order to plot

results=lm(Y~X,data=datum) #run a regression between Y and X
# replace 'Y' with y-variable name
# repace 'X' with x-variable name


### scatterplot of x-y data

plot(Y~X,data=datum) #plots data
# replace 'X' with X-variable name
# replace 'Y' with Y-variable name

abline(results) # add regression line to plot
# won't work unless you've already plotted data
# also won't work if you haven't run the regression

### plot of residuals
plot(datum$X,residuals(results))
#or
plot(residuals(results)~datum$X)
# replace 'X' with X-variable name


### plot histogram of residuals
hist(residuals(results))



### plot autocorrelation function

acf(residuals(results)[order(datum$X)]) # test of autocorrelation in order of X
### note that autocorrelation may exist in terms of the order the data was collected. If so:
acf(residuals(results))
# replace 'X' with x-variable name