options ls=85 nodate; Data Weather; Title 'Lows and Highs from N&O Jan 28,29,30 1992'; Title2 'using actual numbers (yesterday values)'; input city $ hi2 lo2 yhi ylo thi tlo; * Mon Tues Wed ; cards; seattle 51 44 52 44 59 47 boston 29 12 32 29 44 28 richmond 47 23 55 40 51 28 louisville 53 30 37 29 46 24 lubbock 46 40 48 42 54 42 omaha 31 26 36 22 47 31 sanfran 56 47 65 49 65 47 philly 36 18 46 27 46 26 cincinnati 50 25 36 29 41 30 phoenix 74 49 75 48 75 48 miami 72 68 77 71 79 72 milwauke 31 23 33 26 35 26 dallas 50 47 53 47 56 44 burlingvt 20 -2 28 03 39 24 buffalo 34 18 34 28 32 26 charlotte 49 38 60 41 59 41 bismark 27 -5 43 15 47 17 elpaso 61 34 64 33 64 32 rapidcity 46 20 62 25 57 41 ; proc reg; model thi = yhi hi2 tlo ylo lo2/ss1 ss2; test tlo=0, ylo=0, lo2=0; /*----------------------------------------------- | Showing sequential and partial sums of squares| | Note t**2 = F relationship for partial F. By | | hand, construct F to leave out lows. Compare | | to test statement. | -----------------------------------------------*/ proc reg; model thi = yhi hi2; /*-------------------------------------------------- | Use this to construct full vs. reduced model F | | Compare to previous results. | | Note decrease in model SS = increase in error SS. | --------------------------------------------------*/ proc reg; model thi = hi2 tlo ylo lo2 yhi/ss1 ss2; /*--------------------------------------------------- | How do sequential and partial for yhi here compare | | to first regression? Note that partial SS for any | | variable is sequential SS I WOULD have gotten if | | that variable were fitted last. | ----------------------------------------------------*/ run;