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Owner: cpj17
Created: Nov 10, 2017
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The Unofficial 2014 NFL Player Census Part 2

The Unofficial 2014 NFL Player Census Data

Data set 1. dataset deleted empty   [Info]

Scatter Plot

Result 1: Scatter Plot   [Info]

The two quantitative variables I used were height and age. The scatter plot shows no correlation. The scatter plot does not show an apparent linear relationship between the two variables.

There are no outliers in this data so nothing will be removed.

An appropriate significance level for my data is .05 because it between .1 and .01. That allows for some margin of error without having to be precise such as .01 and having too much error like in .1

My line of best fit will be 0.0627x + 21.561645. The m or slope in the equation is 0.0627 and the b or y intercept will be 21.561645

Simple Linear Regression

Result 2: Simple Linear Regression   [Info]
Simple linear regression results:
Dependent Variable: AGE
Independent Variable: HT
AGE = 21.561645 + 0.062728519 HT
Sample size: 1689
R (correlation coefficient) = 0.051425411
R-sq = 0.0026445729
Estimate of error standard deviation: 3.2166245

Parameter estimates:
ParameterEstimateStd. Err.AlternativeDFT-StatP-value
Intercept21.5616452.1967662 ≠ 016879.8151748<0.0001
Slope0.0627285190.029658877 ≠ 016872.11499980.0346

Analysis of variance table for regression model:
SourceDFSSMSF-statP-value
Model146.28298846.2829884.47322430.0346
Error168717454.83710.346673
Total168817501.12

My correlation coefficient is .05 indicating a very weak correlation between the age of players and their heights.

My terms are significant because the p value is lower than my significance level which is at .05

The y intercept is about 21.56 years and the slope is about .06 years. The line of best fit shows that for every one inch increase the age of the player will increase by about .06 years.

R squared is about .003 which means that about .3% of the variation in the in the ages of NFL players is accounted for by the best-fit line relating to age and height.

Line of Best Fit

Result 3: Simple Linear Regression line of best fit   [Info]

The line of best fit is not a good representation because it hits some points but it is not representative of the other values. There are values not being represented even though they are not outliers.

The data has no correlation.

Extra Credit #1

Result 4: Simple Linear Regression QQ plot   [Info]

I do expect my values to follow a normal distribution.

Extra Credit #2

Result 5: Simple Linear Regression predicted vs residuals   [Info]

My residual plot implies that the linear model is a good fit because the residual plots are evenly dispersed around the horizontal axis.

Extra Credit #5

Multiple Linear Regression

Result 6: Multiple Linear Regression   [Info]
Multiple linear regression results:
Dependent Variable: AGE
Independent Variable(s): HT, WT
AGE = 21.319806 + 0.067231098 HT + -0.00036939137 WT

Parameter estimates:
ParameterEstimateStd. Err.AlternativeDFT-StatP-value
Intercept21.3198062.696344 ≠ 016867.9069309<0.0001
HT0.0672310980.041551571 ≠ 016861.61801580.1058
WT-0.000369391370.0023867494 ≠ 01686-0.154767550.877

Analysis of variance table for multiple regression model:
SourceDFSSMSF-statP-value
Model246.53096623.2654832.24729460.106
Error168617454.58910.352663
Total168817501.12

Summary of fit:
Root MSE: 3.2175554
R-squared: 0.0027