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.

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

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

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.

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:ParameterEstimateStd. Err.AlternativeDFT-StatP-valueAnalysis of variance table for regression model:SourceDFSSMSF-statP-valueI am seeing a slight pattern here which means I will need to try and straighten the data,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: 29R (correlation coefficient) = 0.86591979R-sq = 0.74981708Estimate of error standard deviation: 0.058865711Parameter estimates:Analysis of variance table for regression model:This proves that my mini proposal hypothesis is correct by the variation being strong and linear.Here are pictures: