Independent Variable Minutes
Dependent Variable Age
Scatter Plot
The Scatter Plot has many values
There are no outliers that need to be removed
The LINE OF BEST FIT: Slope= 0.000171 YIntercept= 26.152
Line of Best Fit= Y=0.000171x+26.152
Simple linear regression results:
Dependent Variable: Age
Independent Variable: Minutes
Age = 26.152762 + 0.0001719426 Minutes
Sample size: 342
R (correlation coefficient) = 0.030550269
Rsq = 0.00093331893
Estimate of error standard deviation: 4.1183583
Parameter estimates:
Parameter 
Estimate 
Std. Err. 
Alternative 
DF 
TStat 
Pvalue 
Intercept 
26.152762 
0.54916679 
≠ 0 
340 
47.62262 
<0.0001 
Slope 
0.0001719426 
0.00030508878 
≠ 0 
340 
0.56358219 
0.5734 
Analysis of variance table for regression model:
Source 
DF 
SS 
MS 
Fstat 
Pvalue 
Model 
1 
5.387196 
5.387196 
0.31762488 
0.5734 
Error 
340 
5766.6976 
16.960875 

Total 
341 
5772.0848 

The correlation coefficient in this is low. The values that are represented are not correlated in a strong way.

The slope in this equation is small.

The R squared value is significantly small, meaning the two variable in this equation are very poorly correlated.

The scatter plots represents a lot of information and inserting the line of best fit represents how poorly correlated these values are.

The terms have no significance due to such a low correlation
Line of Best Fit
QQ Plot of Residuals
These values do not seem to follow a normal distribution. These values vary and do have some values that are nowhere near the trend.
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Nov 22, 2017
The significance level, also denoted as alpha or α, is the probability of rejecting the null hypothesis when it is true. Usually it is 0.05 or 0.01.
When determine whether a slope is significant or not, we compare the Pvalue with the significance level
R^2 is the percentage of variance of response explained by linear model
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