Could somebody please explain what these words, numbers and initials mean. I hav
ID: 2931398 • Letter: C
Question
Could somebody please explain what these words, numbers and initials mean. I have been practicing Regression Analysis...however have been getting the information from the data myself. I haven't had to extract the information from numbers already worked out. I am trying to figure out what these numbers relate to. Please put explanations below (eg this is the y-intercept, this is the slope, etc)
Coef =
SE Coef=
T =
P =
S =
R-Sq =
R-Sq (adj) =
Thank you
The regression equation is Passengers200513000 0.836 Passengers2004 Predictor Constant Passengers2004 0.836 S 20000 Coef 13000 SE Coef 7464 0.2652 0.108 0.008 3.15 R-Sq= 45.3% R-Sq(adj) = 40.7% 70000-1 . Southwest Delta N 60000 50000 2 40000 30000 20000 10000 0 ATA 10000 20000 30000 40000 50000 60000 70000 Total Passengers: June 2004 0Explanation / Answer
For intercept:
Coef = 13000, In regression with multiple independent variables, the coefficient tells you how much the dependent variable is expected to increase when that independent variable increases by one, holding all the other independent variables constant.
SE Coef = 7464,
Use the standard error of the coefficient to measure the precision of the estimate of the coefficient. The smaller the standard error, the more precise the estimate. Dividing the coefficient by its standard error calculates a t-value. If the p-value associated with this t-statistic is less than your alpha level, you conclude that the coefficient is significantly different from zero
.T = 1.74, this is the 't' statistic
P = 0.108, tells us whether the value is significant or not
S = 20000, you can find S in the Summary of Model section, right next to R-squared. Both statistics provide an overall measure of how well the model fits the data. S is known both as the standard error of theregression and as the standard error of the estimate.
R-Sq = 45.3%, R-squared is a statistical measure of how close the data are to the fitted regression line. It is also known as the coefficient of determination, or the coefficient of multiple determination for multiple regression.
R-Sq (adj) = 40.7%, The adjusted R-squared is a modified version of R-squared that has been adjusted for the number of predictors in the model. The adjusted R-squaredincreases only if the new term improves the model more than would be expected by chance.
For Slope:
Coef = 0.836
SE Coef= 0.2652
T = 3.15
P = 0.008