High and Low Temperature, The National Oceanic and Atmospheric Administration publishes temperature information of cities around the world in Climates of the world. A random sample of 50 cities gave the data on average average high and low temperature in january shown on the Weis Stats site.

a. Obtain the scatterplot for the data

b. Decide whether finding a regression line for the data is reasonable. if so, then also do parts (c)-(f)

c. Determine and interpret the regression equation for the data

d.Identify the potential outliers and influential observation

e. In case a potential outlier is present, remove it and discuss the effect.

f. In case potential influential observations is present,remove it and discuss the effect.

Given in the question that the National Oceanic and Atmospheric Administration publishes temperature information of cities around the world in Climates of the world. A random sample of 50 cities gave the data on average average high and low temperature in january shown on the Weis Stats site.

we have to obtain the scatterplot for the data.

Given in the question that the National Oceanic and Atmospheric Administration publishes temperature information of cities around the world in Climates of the world. A random sample of 50 cities gave the data on average average high and low temperature in january shown on the Weis Stats site. we have to decide whether finding a regression line for the data is reasonable

If the scatterplot has no strong curvature, it is reasonable to find the regression line for the data.

Because the scatterplot has no strong curvature, it is reasonable to identify the regression line to the data.

Given in the question that the National Oceanic and Atmospheric Administration publishes temperature information of cities around the world in Climates of the world. A random sample of 50 cities gave the data on average average high and low temperature in january shown on the Weis Stats site. we have to determine and interpret the regression equation for the data.

Formula used to find the regression equation is

$\hat{y}=b_0+b_{1}x$

where

$b_0=-7.5692$

$b_1=0.9168$

therefore,

$\hat{y}=-7.5692+0.9168x$

Given in the question that the National Oceanic and Atmospheric Administration publishes temperature information of cities around the world in Climates of the world. A random sample of 50 cities gave the data on average average high and low temperature in january shown on the Weis Stats site. we have to identify the potential outliers and influential observation.

An outlier is a data point that is far off the regression line.

An influential observation is one where the removal of a point causes a significant change in the regression equation. That instance, removing a point causes a significant shift in the regression line's direction.

There are no potential outliers in the dataset because all of the points are closed to the regression line in the plotted graph. There is no considerable change in the direction of the regression line when a point is removed, hence there are no potentially influencing observations.

Given in the question that the National Oceanic and Atmospheric Administration publishes temperature information of cities around the world in Climates of the world. A random sample of 50 cities gave the data on average average high and low temperature in january shown on the Weis Stats site.

we have to obtain in case a potential outlier is present, remove it and discuss the effect.

There are no outliers in part d so this is not applicable.

Given in the question that the National Oceanic and Atmospheric Administration publishes temperature information of cities around the world in Climates of the world. A random sample of 50 cities gave the data on average average high and low temperature in january shown on the Weis Stats site.

we have to obtain in case potential influential observations is present,remove it and discuss the effect.

There is no potential influential observation in part d. so this is not applicable.