StatCrunch logo (home)

Report Properties
Thumbnail:

from Flickr
Owner: vdf17
Created: Nov 8, 2017
Share: yes
Views: 271
Tags:
 
Results in this report
 
Data sets in this report
 
Need help?
To copy selected text, right click to Copy or choose the Copy option under your browser's Edit menu. Text copied in this manner can be pasted directly into most documents with formatting maintained.
To copy selected graphs, right click on the graph to Copy. When pasting into a document, make sure to paste the graph content rather than a link to the graph. For example, to paste in MS Word choose Edit > Paste Special, and select the Device Independent Bitmap option.
You can now also Mail results and reports. The email may contain a simple link to the StatCrunch site or the complete output with data and graphics attached. In addition to being a great way to deliver output to someone else, this is also a great way to save your own hard copy. To try it out, simply click on the Mail link.
NFL scores project part 2
Mail   Print   Twitter   Facebook

 

Data set 1. NFL Scores from 2013   [Info]
To analyze this data, please sign in.

 

Result 1: Scatter Plot   [Info]
Right click to copy

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]
Right click to copy

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]
Right click to copy

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
R-squared (adjusted): 0.1851

 

HTML link:
<A href="https://www.statcrunch.com/5.0/viewreport.php?reportid=73499">NFL scores project part 2</A>

Comments
Want to comment? Subscribe
Already a member? Sign in.
By xg15
Nov 13, 2017

When determine whether a slope is significant or not, we compare the P-value with the significance level;
R^2 is the percentage of variance explained by linear model;
This two variable is correlated;
The Multiple linear model is just another simple linear model.

Always Learning