College scatterplots questions and answers

The accompanying data on y = normalized energy \(\displaystyle{\left(\frac{{J}}{{{m}}^{2}}\right)}\) and x = intraocular pressure (mmHg) appeared in a scatterplot in the article “Evaluating the Risk of Eye Injuries: Intraocular Pressure During High Speed Projectile Impacts” (Current Eye Research, 2012: 43–49). an estimated regression function was superimposed on the plot.
\(\begin{array}{} x&2761&19764&25713&3980&12782&19008\\ y&1553&14999&32813&1667&8741&16526 \\ x&20782&19028&14397&9606&3905&25731\\ y&26770&16526&9868&6640&1220&30730 \\ \end{array}\)
The standardized residuals resulting from fitting the simple linear regression model (in the same order as the observations) are .98, -1.57, 1.47, .50, -.76, -.84, 1.47, -.85, -1.03, -.20, .40, and .81. Construct a plot of e* versus x and comment. [Note: The model fit in the cited article was not linear.]

Using the daily high and low temperature readings at Chicago's O'Hare International Airport for an entire year, a meteorologist made a scatterplot relating y = high temperature to x = low temperature, both in degrees Fahrenheit.

After verifying that the conditions for the regression model were met, the meteorologist calculated the equation of the population regression line to be  \(\left[\mu_y=16.6+1.02\right] with \left[\sigma = 6.6+^\circ F\right]\)

If the meteorologist used a random sample of 10 days to calculate the regression line instead of using all the days in the year, would the slope of the sample regression line be exactly 1.02? Explain your answer.

The accompanying data on y = normalized energy \(\displaystyle{\left(\frac{{J}}{{m}}{2}\frac{{J}}{{m}^{{2}}}\right)}\) and x = intraocular pressure (mmHg) appeared in a scatterplot in the article “Evaluating the Risk of Eye Injuries: Intraocular Pressure During High Speed Projectile Impacts” (Current Eye Research, 2012: 43–49); an estimated regression function was superimposed on the plot.
x2761197642571339801278219008 y155314999328131667874116526 x2078219028143979606390525731 y267701652698686640122030730
The standardized residuals resulting from fitting the simple linear regression model (in the same order as the observations) are .98, -1.57, 1.47, .50, -.76, -.84, 1.47, -.85, -1.03, -.20, .40, and .81. Construct a plot of e* versus x and comment. [Note: The model fit in the cited article was not linear.]

Using the health records of ever student at a high school, the school nurse created a scatterplot relating y = height (in centimeters) to x = age (in years). After verifying that the conditions for the regression model were met, the nurse calculated the equation of the population regression line to be &\(\mu;0=105+4.2x\) with &\(\sigma;=7\) cm. According to the population regression line, what is the average height of 15-year-old students at this high school?