Part 3: Linear Regression

For this part, you will look at the minimum temperature recorded on 2/22/2022, in F, for various weather stations in OR.4 The data file also includes the elevation of the weather station in feet.

In this part, our goal is to use linear regression to analyze the relationship, if any, between elevation and minimum temperature.

The data file can be found by following the link below. It is named Elevation and Minimum Temperature. Note that you can not edit the file, so you will need to make a copy of it to your Drive in order to use it.

Station	Elevation	Minimum Temperature
Astoria	11	34
Tillamook	36	30
Newport	130	32
Portland Airport	27	32
Troutdale	35	32
Hillsboro	204	31
Aurora	196	31
McMinnville	160	33
Salem	210	31
Corvallis	246	30
Eugene	369	32
Creswell	541	30
Hood River	630	27
Brookings Arpt	459	33
Roseburg	525	35
Sexton Summit	3958	22
Medford	1330	30
Klamath Falls	4090	14
Lakeview	4730	19
Bend Airport	3454	14
La Grande	2700	17
Meacham	3726	6
Pendleton Airport	1495	13
Redmond	3077	14
The Dalles	250	27
John Day Airport	3700	15
Prineville Airport	3250	14
Sisters Airport	3152	16
Sunriver Airport	4152	12
Joseph Airport	4120	11
Baker City	3370	18
Burns	4140	20
Ontario	2190	28
Rome	4050	20

3.2 Performing Regression

a) Compute the least squares regression line. Round coefficients to four decimal places.

b) Interpret the slope. You may convert numbers in a way that creates a more under-standable statement if you choose.

c) Interpret the y-intercept. Round the intercept to one decimal place in your interpre- tation.

d) Compute R and R2. Round these to four decimal places. What do EACH of these tell you?

e) Use your model from a) to determine the minimum temperature expected at an elevation of 1000ft? Round final answer to one decimal place.

f) Use your model from a) to determine at what elevation you would expect the min- imum temperature to be 28F. Round final answer to the nearest whole number.

g) Create a scatterplot of the data that includes the least squares line.