|
MLR is a program for simple and multiple linear regression analysis models. Given a set of observed values for a dependent variable, Y, and one or more independent variables X(i), MLR computes the linear least-squares regression equation and accompanying statistics.
Input data organization. Except for the title, which must be on a separate line, your data for MLR may be entered freeform by separating values by one or more spaces or by starting a new line. The following data is must be provided:
1. k, the number of independent (X) and dependent (Y) variables (maximum: 15)
2. n, the number of observations of the k variables (maximum: 300).
3. A list of k names for the variables, each up to 8 characters long,
separated by blanks with the dependent variable is listed LAST.
4. n sets of observations for the k variables separated by blanks.
The form is:
X(1,1) X(1,2) ...X(1,k-1) Y(1)
X(2,1) X(2,2) ...X(2,k-1) Y(2)
...
X(n,1) X(n,2) ... X(n,k-1) Y(n)
5. Any additional observations to be forecast with the regression
equation computed from the preceding data.
Example data set. This may be cut and pasted into the input form.
3 10
INCOME$ FAMSIZE SPENT$
8 6 22
10 7 23
7 5 18
2 2 9
4 3 14
6 4 20
7 4 21
6 3 18
4 3 16
6 3 19
Example data set 2: Longley certification dataset
7 16
x1 x2 x3 x4 x5 x6 y
83 234289 2356 1590 107608 1947 60323
88.5 259426 2325 1456 108632 1948 61122
88.2 258054 3682 1616 109773 1949 60171
89.5 284599 3351 1650 110929 1950 61187
116.9 554894 4007 2827 130081 1962 70551
96.2 328975 2099 3099 112075 1951 63221
98.1 346999 1932 3594 113270 1952 63639
99 365385 1870 3547 115094 1953 64989
100 363112 3578 3350 116219 1954 63761
101.2 397469 2904 3048 117388 1955 66019
104.6 419180 2822 2857 118734 1956 67857
108.4 442769 2936 2798 120445 1957 68169
110.8 444546 4681 2637 121950 1958 66513
112.6 482704 3813 2552 123366 1959 68655
114.2 502601 3931 2514 125368 1960 69564
115.7 518173 4806 2572 127852 1961 69331
Other data sets:
|
