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Created: Nov 8, 2017
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Project Part 2
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 Scatter Plot

 

My scatter plot has no correlation and my two variables are walmart and target incidents. There are no outliers. There is no linear relationship either.

 My signifigance level is .05 because the variation is moderate. Weak correlation

My line of best fit is y=171.28464x + .0044184021

 

 Simple linear regression results:

Dependent Variable: Target Incidents
Independent Variable: Walmart Incidents 
Target Incidents = 171.28464 + 0.0044184021 Walmart Incidents
Sample size: 32
R (correlation coefficient) = 0.0069340322
R-sq = 0.000048080803
Estimate of error standard deviation: 100.76015

Parameter estimates:

ParameterEstimateStd. Err.AlternativeDFT-StatP-value
Intercept 171.28464 91.3491 ≠ 0 30 1.8750556 0.0705
Slope 0.0044184021 0.11633444 ≠ 0 30 0.037980172 0.97


Analysis of variance table for regression model:

SourceDFSSMSF-statP-value
Model 1 14.64507 14.64507 0.0014424934 0.97
Error 30 304578.23 10152.608    
Total 31 304592.88      

  Y intercept is 171.28464

Correlation Coefficient is .0069340322

Slope is .0044184021

R squared is .0000448080803

My line of best fit doesn't seem to touch any of the point because there is no correlation. My terms are significant.

 

Graph with line of best fit

The line of best fit doesn't seemt to work with this data becaue there is no correlation. My points are not correlated and there is no relation.

 

EC 1 Yes on the residuals graph they seem to follow a normal distribution.

EC 3 Yes I got different results. 

EC 2 The graph is still similar with no correlation.

EC 5 Multiple linear regression graph

Result 1: Scatter Plot   [Info]
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Result 2: Simple Linear Regression   [Info]
Simple linear regression results:
Dependent Variable: Target Incidents
Independent Variable: Walmart Incidents
Target Incidents = 171.28464 + 0.0044184021 Walmart Incidents
Sample size: 32
R (correlation coefficient) = 0.0069340322
R-sq = 0.000048080803
Estimate of error standard deviation: 100.76015

Parameter estimates:
ParameterEstimateStd. Err.AlternativeDFT-StatP-value
Intercept171.2846491.3491 ≠ 0301.87505560.0705
Slope0.00441840210.11633444 ≠ 0300.0379801720.97

Analysis of variance table for regression model:
SourceDFSSMSF-statP-value
Model114.6450714.645070.00144249340.97
Error30304578.2310152.608
Total31304592.88

Result 3: Simple Linear Regression with line of best fit   [Info]
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Result 4: qq plot of residuals   [Info]
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Result 5: predicted vs residuals   [Info]
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Result 6: Multiple Linear Regression   [Info]
Multiple linear regression results:
Dependent Variable: Walmart Incidents
Independent Variable(s): Target Incidents, Distance, Difference
Walmart Incidents = 1.8189894e-12 + 1 Target Incidents + 1.1368684e-13 Distance + 1 Difference

Parameter estimates:
ParameterEstimateStd. Err.AlternativeDFT-StatP-value
Intercept1.8189894e-123.655941e-12 ≠ 0280.497543420.6227
Target Incidents18.514176e-15 ≠ 0281.1745118e14<0.0001
Distance1.1368684e-133.6816642e-13 ≠ 0280.308791980.7598
Difference14.4244729e-15 ≠ 0282.2601562e14<0.0001

Analysis of variance table for multiple regression model:
SourceDFSSMSF-statP-value
Model3750172.22250057.411.7210126e28<0.0001
Error284.0683069e-221.4529667e-23
Total31750172.22

Summary of fit:
Root MSE: 3.8117801e-12
R-squared: 1
R-squared (adjusted): 1

HTML link:
<A href="https://www.statcrunch.com/5.0/viewreport.php?reportid=73444">Project Part 2</A>

Comments
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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;

The multiple linear regression model is useless since we know that Walmart Incidents=Target Difference

Always Learning