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Owner: vdf17
Created: Nov 8, 2017
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NFL scores project part 2

Data set 1. NFL Scores from 2013   [Info]

Result 1: Scatter Plot   [Info]

The data is a weak positive correlation.

There are no outliers.

The significant level is .05 because it is weak and positive.

The line of best fit is y=5.8564767 + 0.4103968x

Result 2: Simple Linear Regression   [Info]
Simple linear regression results:
Dependent Variable: PtsL
Independent Variable: PtsW
PtsL = 5.8564767 + 0.4103968 PtsW
Sample size: 267
R (correlation coefficient) = 0.43377087
R-sq = 0.18815717
Estimate of error standard deviation: 7.4965782

Parameter estimates:
ParameterEstimateStd. Err.AlternativeDFT-StatP-value
Intercept5.85647671.5903691 ≠ 02653.68246390.0003
Slope0.41039680.05236687 ≠ 02657.8369548<0.0001

Analysis of variance table for regression model:
SourceDFSSMSF-statP-value
Model13451.60313451.603161.417861<0.0001
Error26514892.65256.198685
Total26618344.255

The y intercept is 5.8564767

The slope is 0.4103968

R^2 is 0.18815717

Result 3: Scatter Plot line of best fit   [Info]

The line of best fit accuratly fits my data.

No, my data is not correlated because the score of one team does not dictate the other teams score.

Result 4: QQ Plot   [Info]

EC #1: Yes, my data follows a normal distribution.

Result 5: Multiple Linear Regression   [Info]
Multiple linear regression results:
Dependent Variable: PtsW
Independent Variable(s): PtsL
PtsW = 20.92224 + 0.45847622 PtsL

Parameter estimates:
ParameterEstimateStd. Err.AlternativeDFT-StatP-value
Intercept20.922241.1481847 ≠ 026518.222015<0.0001
PtsL0.458476220.058501832 ≠ 02657.8369548<0.0001

Analysis of variance table for multiple regression model:
SourceDFSSMSF-statP-value
Model13855.97043855.970461.417861<0.0001
Error26516637.37862.782558
Total26620493.348

Summary of fit:
Root MSE: 7.9235445
R-squared: 0.1882