summary()->output
Formula: val ~ exp(-k * time)
Parameters:
Estimate Std. Error t value Pr(>|t|)
k 4.456e-03 3.398e-05 131.2 9.77e-07 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.003644 on 3 degrees of freedom
Number of iterations to convergence: 4
Achieved convergence tolerance: 3.743e-06
Using T-Tables
We have learned the following things about a t-test:
- The t-test produces a single value, t, which grows larger as the difference between the means of two samples grows larger;
- t does not cover a fixed range such as 0 to 1 like probabilities do;
- You can convert a t-value into a probability, called a p-value;
- The p-value is always between 0 and 1 and it tells you the probability of the difference in your data being due to sampling error;
- The p-value should be lower than a chosen significance level (0.05 for example) before you can reject your null hypothesis.
