model jan = lat
The results of this command are
See M-Lab 3 for explanation of this ANOVA Table
*************************************************************************
*Sequential Sums of Squares ANOVA Table *
* *
*Source df SS MS F p-val *
* *
*Intercept 1 45777.3282 45777.3282 638.4939 0.00000000 *
*lat 1 4797.7835 4797.7835 66.9186 1.1773e-10 *
*Error 48 3441.3983 71.6958 *
*************************************************************************
R-square 0.58231 <--- For models with one term like this, this is just
the square of the correlation beteen x and y,r2.
For models with more than term, R2 is the proportion
of the variation in y explained by the association
with x.
Standard Error 8.4673 <--- The estimate of the standard deviation of the
errors ei in the model yi=a+bxi+ei.
Parameter Estimates
Source Parameter Estimate Std. Error t p-val
Intercept 99.9955 8.60870 11.6157 1.5543e-15
lat -1.8898 0.23102 -8.1804 1.1773e-10
^ ^ ^ ^ ^ p-values smaller
| | | | | than .05 suggest
| | | | that the true
List of terms Least sq. estimates. | | coeff. is not 0.
in the model. 99.9955 is estimated | |
intercept. -1.8898 is | Est./Std. Error
estimated slope for lat.| For example,
| 99.9955/8.60870=11.6157
|
Estimates of the standard deviation
of the least squares estimates of the
coefficients.
Thus the least squares line is
jan = 99.9955 + -1.8898*lat