My proposal is that I will measure in inches the height and the widths of the car and/or truck tires. I think there will be a strong and linear association between the two.
I will go to 30 different cars or trucks with a tape measure and measure the tire height and widths.

Link to spread sheet:
__https://docs.google.com/spreadsheets/d/1YpsELbrMAe75vEXqBDlmLdCiz2_wSpgQXFJ04pSVMfw/edit#gid=0__

Simple linear regression results:
Dependent Variable: Height(Inches)
Independent Variable: Width(Inches)
Height(Inches) = 5.2049273 + 2.394505 Width(Inches)
Sample size: 29
R (correlation coefficient) = 0.89879974
R-sq = 0.80784098
Estimate of error standard deviation: 3.2506966

Parameter estimates:
Parameter
Estimate
Std. Err.
Alternative
DF
T-Stat
P-value
Intercept
5.2049273
2.4764302
≠ 0
27
2.1017864
0.045
Slope
2.394505
0.22475084
≠ 0
27
10.654043
<0.0001
Analysis of variance table for regression model:
Source
DF
SS
MS
F-stat
P-value
Model
1
1199.4489
1199.4489
113.50862
<0.0001
Error
27
285.30977
10.567028


Total
28
1484.7586
Regression1.PNG

external image YluFrb1db7JcZlSe-n_5-gbCzfrEfatIiY8d_U2bzddkGAoiAse-4TEDsIxIqpYbaw9z19YtEnoVc7YsJwssvi4bhBt8qz9HrEoH-Una1dOzXnrFAoZY2bIHkUG3ThHFIPYLD509

I am seeing a slight pattern here which means I will need to try and straighten the data,


external image CzNMzMyu3xL7oMcR-2wwzRMFwjN5rDNgvdyld7awmlldHEVpSP6qfqYd0v2iatIJiCkQTFH8ebedPAoZ-9cMuOnyQGi-0ROi6JiWW5loBET3HS0pWnlVbEuR5HsUYytxdIfixEIG
I compared the square root of the width versus height and then the log of width versus the height. Both of the graphs straightened the data however the log(width) vs height graph had smaller residual numbers on the y-axis.


Simple linear regression results:
Dependent Variable: Log(Width)
Independent Variable: Height(Inches)
Log(Width) = 0.59126497 + 0.013742482 Height(Inches)
Sample size: 29
R (correlation coefficient) = 0.86591979
R-sq = 0.74981708
Estimate of error standard deviation: 0.058865711


Parameter estimates:
Parameter
Estimate
Std. Err.
Alternative
DF
T-Stat
P-value
Intercept
0.59126497
0.048295552
≠ 0
27
12.242638
<0.0001
Slope
0.013742482
0.0015276873
≠ 0
27
8.9956121
<0.0001


Analysis of variance table for regression model:
Source
DF
SS
MS
F-stat
P-value
Model
1
0.2804053
0.2804053
80.921037
<0.0001
Error
27
0.093559641
0.0034651719


Total
28
0.37396494





This proves that my mini proposal hypothesis is correct by the variation being strong and linear.

Here are pictures: