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
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 &
In simple terms, scatter plots are using horizontal and vertical axis points to explain how a certain variable affects another one. For example, the majority of scatter plot questions will have at least two major variables. The task is to find and explain various data patterns that explain the relationships between them. See some scatter plots examples as it will make more sense when you see graphics and formulas explained. Alternatively, start with scatter plots equations that are presented below and compare things to your original task.