R Assignment 2

Ashley’s R Assignment 2

scatterplot( parenthood$dan.sleep,
      parenthood$dan.grump,
      main = "Dan's grunpiness per hours slept",
      xlab = "Dan sleep (hours)",
      ylab = "Dan grumpiness (0-100)",
      xlim = c(0,12),   # scale the x-axis
      ylim = c(0,100),    # scale the y-axis
      pch = 20,         # change the plot type
      frame.plot = FALSE, # don’t draw a box
      col= "pink"
)

The association is negative and very strong. The correlation coefficient is to be expected given how close the points lie to the line and how negatively it is sloped. Out of the three correlations this one is the strongest. This means that Dan’s grumpiness decreases significantly and quite strongly with the increasing of his hours of sleep.

cor(parenthood$dan.sleep,parenthood$dan.grump)

[1] -0.903384

lm.grump.sleep <- lm(parenthood$dan.grump ~ parenthood$dan.sleep)

The residual plot does satisfy the assumption for linear regression.

The QQ plot does satisfy the assumptions for linear regression.

scatterplot( parenthood$baby.sleep,
             parenthood$dan.grump,
             main = "Dan's grunpiness per hours slept",
             xlab = "Baby sleep (hours)",
             ylab = "Dan grumpiness (0-100)",
             xlim = c(0,12),   # scale the x-axis
             ylim = c(0,100),    # scale the y-axis
             pch = 20,         # change the plot type
             frame.plot = FALSE, # don’t draw a box
             col= "yellow"
)

The association is negative and moderately stong. The correlation coefficient for this graph is not surprising. The graph looks moderately strong, with points that are not clustered tightly around the line of best fit.

r scatterplot( parenthood$baby.sleep, parenthood$dan.grump, main = "Dan's hours slept per hours baby slept", xlab = "Baby sleep (hours)", ylab = "Dan's sleep (0-100)", xlim = c(0,12), # scale the x-axis ylim = c(0,100), # scale the y-axis pch = 20, # change the plot type frame.plot = FALSE, # don’t draw a box col= "orange" ){r}

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Error: attempt to use zero-length variable name ```

lm.grump.sleep <- lm(parenthood$baby.sleep ~ parenthood$dan.grump)

Once again, the association is negative and quite strong. The correlation given to this graph is to be expected since the graph negative and strong, but the points still have space from the line of best fit.

plot(lm.baby.sleep, which = 1)

plot(lm.baby.sleep, which = 1)

```

cor(parenthood$dan.sleep,parenthood$baby.sleep)

lm.baby.sleep <- lm(parenthood$baby.sleep ~ parenthood$dan.sleep)

plot(lm.baby.sleep, which = 1) 

plot(lm.baby.sleep, which = 2)