Multiple Linear Regression

Model 1: Price = A1*AGST + A2*HarvestRain + B

Coefficients:	Estimate	Std. Error	t value	Pr(>|t|)
B (Intercept)	-2.20265	1.85443	-1.188	0.247585
A1 (AGST)	0.60262	0.11128	5.415	1.94e-05 ***
A2 (HarvestRain)	-0.00457	0.00101	-4.525	0.000167 ***

Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.3674 on 22 degrees of freedom

Multiple R-squared: 0.7074, Adjusted R-squared: 0.6808

F-statistic: 26.59 on 2 and 22 DF, p-value: 1.347e-06

Explanation:

• Estimate column: coefficients for the intercept (B) and for each of the independent variables in our model.

• A coefficient of 0 means that the value of the independent variable does not change our prediction for the dependent variable.

• Std. Error (standard error) measures of how much the coefficient is likely to vary from the estimate value.

• t value = Estimate/Std.Error
  It will be negative if the estimate is negative, and positive if the estimate is positive.
  The larger the absolute value of the t value. the more likely the coefficient is to be significant.
  We want variables with a large absolute value in this column.

• The last column gives the probability that a coefficient is actually 0. It will be large if the absolute value of the t value is small, and it will be small if the absolute value of the t value is large.
  We want variables with small values in this column.

• Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
  *** - most significant and ' ' - not significant

• Adjusted R^2 = 1-(1-R^2)*((n-1)/(n-m-1)), n - number of data points, m - number of independent variables in the model

• DF - Degrees of Freedom = n-m-1, n - number of data points, m - number of independent variables in the model