南京大学模板PPT学习教案

1、会计学1南京大学模板南京大学模板2022-4-282第1页/共16页2022-4-283第2页/共16页2022-4-284第3页/共16页2022-4-285 _cons . .2 26 64 43 37 72 25 5 . .2 28 89 90 09 96 65 5 0 0. .9 91 1 0 0. .3 37 70 0 - -. .3 33 33 36 66 69 92 2 . .8 86 62 24 41 14 41 1 x3 . .0 02 29 99 96 60 08 8 . .0 04 41 14 46 65 56 6 0 0. .7 72 2 0 0. .4 47 77 7

2、 - -. .0 05 55 58 81 17 73 3 . .1 11 15 57 73 38 89 9 x2 - -. .1 10 02 28 81 19 95 5 . .0 06 67 72 27 71 19 9 - -1 1. .5 53 3 0 0. .1 14 40 0 - -. .2 24 41 19 98 82 21 1 . .0 03 36 63 34 43 31 1 x1 . .0 09 99 93 31 12 22 2 . .0 00 03 32 23 32 2 3 30 0. .7 73 3 0 0. .0 00 00 0 . .0 09 92 26 62 26 63

3、3 . .1 10 05 59 99 98 81 1 y Coef. Std. Err. t P|t| 95% Conf. Interval Total 9 97 7. .8 86 63 34 47 71 16 6 2 26 6 3 3. .7 76 63 39 97 79 96 68 8 Root MSE = . .2 29 99 93 3 Adj R-squared = 0 0. .9 97 76 62 2 Residual 2 2. .0 06 60 02 28 83 33 31 1 2 23 3 . .0 08 89 95 57 77 75 53 35 5 R-squared = 0

4、0. .9 97 78 89 9 Model 9 95 5. .8 80 03 31 18 88 83 3 3 3 3 31 1. .9 93 34 43 39 96 61 1 Prob F = 0 0. .0 00 00 00 0 F( 3, 23) = 3 35 56 6. .5 50 0 Source SS df MS Number of obs = 2 27 7. regress y x1 x2 x3第4页/共16页2022-4-286第5页/共16页2022-4-287第6页/共16页2022-4-288第7页/共16页2022-4-289第8页/共16页2022-4-2810第9页

5、/共16页2022-4-2811第10页/共16页2022-4-2812 _cons 3 3. .5 54 42 20 08 84 4 . .5 57 73 36 62 21 17 7 6 6. .1 17 7 0 0. .0 00 00 0 2 2. .3 36 60 06 68 88 8 4 4. .7 72 23 34 48 8 x3 - -. .2 27 71 16 63 32 22 2 . .2 25 51 12 23 38 83 3 - -1 1. .0 08 8 0 0. .2 29 90 0 - -. .7 78 89 90 06 67 71 1 . .2 24 45 58 8

6、0 02 28 8 y Coef. Std. Err. t P|t| 95% Conf. Interval Total 9 97 7. .8 86 63 34 47 71 16 6 2 26 6 3 3. .7 76 63 39 97 79 96 68 8 Root MSE = 1 1. .9 93 33 38 8 Adj R-squared = 0 0. .0 00 06 65 5 Residual 9 93 3. .4 49 92 20 02 24 46 6 2 25 5 3 3. .7 73 39 96 68 80 09 98 8 R-squared = 0 0. .0 04 44 47

7、 7 Model 4 4. .3 37 71 14 44 47 70 02 2 1 1 4 4. .3 37 71 14 44 47 70 02 2 Prob F = 0 0. .2 28 89 99 9 F( 1, 25) = 1 1. .1 17 7 Source SS df MS Number of obs = 2 27 7. regress y x3 _cons 4 4. .5 50 04 49 94 47 7 1 1. .3 34 41 10 09 95 5 3 3. .3 36 6 0 0. .0 00 03 3 1 1. .7 74 42 29 90 09 9 7 7. .2 2

8、6 66 69 98 84 4 x2 - -. .4 46 61 12 29 95 51 1 . .4 41 14 42 28 80 08 8 - -1 1. .1 11 1 0 0. .2 27 76 6 - -1 1. .3 31 14 45 52 22 2 . .3 39 91 19 93 32 23 3 y Coef. Std. Err. t P|t| 95% Conf. Interval Total 9 97 7. .8 86 63 34 47 71 16 6 2 26 6 3 3. .7 76 63 39 97 79 96 68 8 Root MSE = 1 1. .9 93 31

9、 12 2 Adj R-squared = 0 0. .0 00 09 91 1 Residual 9 93 3. .2 23 39 93 37 70 05 5 2 25 5 3 3. .7 72 29 95 57 74 48 82 2 R-squared = 0 0. .0 04 47 73 3 Model 4 4. .6 62 24 41 10 01 10 06 6 1 1 4 4. .6 62 24 41 10 01 10 06 6 Prob F = 0 0. .2 27 76 61 1 F( 1, 25) = 1 1. .2 24 4 Source SS df MS Number of

10、 obs = 2 27 7. regress y x2 _cons - -. .0 01 10 05 55 50 05 5 . .1 11 14 41 12 27 72 2 - -0 0. .0 09 9 0 0. .9 92 27 7 - -. .2 24 45 55 59 99 98 8 . .2 22 24 44 49 98 88 8 x1 . .0 09 99 95 54 43 39 9 . .0 00 03 31 14 47 76 6 3 31 1. .6 63 3 0 0. .0 00 00 0 . .0 09 93 30 06 61 12 2 . .1 10 06 60 02 2

11、6 65 5 y Coef. Std. Err. t P|t| 95% Conf. Interval Total 9 97 7. .8 86 63 34 47 71 16 6 2 26 6 3 3. .7 76 63 39 97 79 96 68 8 Root MSE = . .3 30 08 89 97 7 Adj R-squared = 0 0. .9 97 74 46 6 Residual 2 2. .3 38 86 65 57 79 92 22 2 2 25 5 . .0 09 95 54 46 63 31 16 69 9 R-squared = 0 0. .9 97 75 56 6 Model 9 95 5. .4 47 76 68 89 92 24 4 1 1 9 95 5. .4 47 76 68 89 92 24 4 Prob F = 0 0. .0 00 00 00 0 F( 1, 25) = 1 10 00 00 0. .1 14