Bài 1 reg wage educ exper nonwhite female married south Source | SS df MS Number of obs = 526 + F( 6, 519) = 41 18 Model | 2309 32588 6 384 887647 Prob > F = 0 0000 Residual | 4851 08841 519 9 3469911[.]
Bài reg wage educ exper nonwhite female married south Source | SS df MS -+ -Model | 2309.32588 384.887647 Residual | 4851.08841 519 9.34699115 -+ -Total | 7160.41429 525 13.6388844 Number of obs F( 6, 519) Prob > F R-squared Adj R-squared Root MSE = = = = = = 526 41.18 0.0000 0.3225 0.3147 3.0573 -wage | Coef Std Err t P>|t| [95% Conf Interval] -+ -educ | 5694223 0519968 10.95 0.000 4672723 6715723 exper | 0552699 011024 5.01 0.000 0336127 076927 nonwhite | 0729731 4437979 0.16 0.869 -.7988879 9448342 female | -2.092037 2717096 -7.70 0.000 -2.625823 -1.558251 married | 7150305 2975101 2.40 0.017 1305585 1.299503 south | -.6416218 2830282 -2.27 0.024 -1.197644 -.0856001 _cons | -1.410051 7743316 -1.82 0.069 -2.93126 1111587 SRF: wage = -1.41 + 0.569educ + 0.055exper+0.072nonwhite 2.092female+0.715married-0.641south +u^ Wage^ = -1.41 + 0.569educ + 0.055exper+0.072nonwhite 2.092female+0.715married-0.641south Bài reg wage educ exper nonwhite female married south, robust Linear regression Number of obs F( 6, 519) Prob > F R-squared Root MSE = = = = = 526 26.91 0.0000 0.3225 3.0573 -| Robust wage | Coef Std Err t P>|t| [95% Conf Interval] -+ -educ | 5694223 0657564 8.66 0.000 4402409 6986037 exper | 0552699 0103954 5.32 0.000 0348477 075692 nonwhite | 0729731 3861896 0.19 0.850 -.6857137 83166 female | -2.092037 2538627 -8.24 0.000 -2.590762 -1.593312 married | 7150305 2672913 2.68 0.008 1899247 1.240136 south | -.6416218 2715594 -2.36 0.019 -1.175113 -.108131 _cons | -1.410051 8691299 -1.62 0.105 -3.117496 2973942 Bài Source | SS df MS -+ -Model | 28988.0176 5797.60351 Residual | 545486.724 1381 394.994007 -+ -Total | 574474.741 1386 414.48394 Number of obs F( 5, 1381) Prob > F R-squared Adj R-squared Root MSE = = = = = = 1387 0.0000 0.0470 19.874 -bwght | Coef Std Err t P>|t| [95% Conf Interval] -+ -cigs | -.5014951 0915656 -5.48 0.000 -.6811179 -.3218723 parity | 1.79824 603 2.98 0.003 6153453 2.981135 male | 3.078625 1.068652 2.88 0.004 9822692 5.174982 white | 6.36384 1.310043 4.86 0.000 3.793952 8.933729 motheduc | 2697001 2325746 1.16 0.246 -.1865376 7259379 _cons | 106.7333 3.461357 30.84 0.000 99.94326 113.5234 -bwght int %8.0g birth weight, ounces cigs byte %8.0g cigs smked per day while preg parity byte %8.0g birth order of child male byte %8.0g =1 if male child white byte %8.0g =1 if white motheduc byte %8.0g mother's yrs of educ Viết hàm SRF Tính giải Kiểm định Kiểm định phương pháp: thích ý nghĩa hệ số xác định R2 phù hợp mơ hình giải thích ý nghĩa biến cigs; male; motheduc với kiểm định t, khoảng tin cậy Bài Source | SS df MS Number of obs = 1260 -+ -F( 8, 1251) = 29.51 Model | 4341.73975 542.717468 Prob > F = 0.0000 Residual | 23005.6994 1251 18.3898477 R-squared = 0.1588 -+ -Adj R-squared = 0.1534 Total | 27347.4392 1259 21.7215561 Root MSE = 4.2883 -wage | Coef Std Err t P>|t| [95% Conf Interval] -+ -looks | 6252181 1790031 3.49 0.000 2740387 9763975 union | 5564871 2739391 2.03 0.042 0190562 1.093918 goodhlth | 1983287 490779 0.40 0.686 -.764512 1.161169 black | -.3063812 4700496 -0.65 0.515 -1.228554 6157913 female | -2.398656 2751384 -8.72 0.000 -2.93844 -1.858873 married | 3874926 2869665 1.35 0.177 -.175496 9504813 exper | 2726329 0404318 6.74 0.000 1933113 3519546 expersq | -.0047333 0008924 -5.30 0.000 -.006484 -.002982 _cons | 1.845306 8706818 2.12 0.034 1371483 3.553463 -wage float %9.0g hourly wage exper byte %8.0g years of workforce experience looks byte %8.0g from to union byte %8.0g =1 if union member goodhlth byte %8.0g =1 if good health black byte %8.0g =1 if black female byte %8.0g =1 if female married byte %8.0g =1 if married Write SRF Calculate R-square and explain the meaning of R-squared Test for the overall significance of the model Test for the significance of each independent variable with methods: critical value, p-value and confidence interval Bài Source | SS df MS -+ -Model | 30215.0262 5035.83771 Residual | 544259.715 1380 394.391098 -+ -Total | 574474.741 1386 414.48394 Number of obs F( 6, 1380) Prob > F R-squared Adj R-squared Root MSE = = = = = = 1387 12.77 0.0000 0.0526 0.0485 19.859 -bwght | Coef Std Err t P>|t| [95% Conf Interval] -+ -faminc | 0591903 0335575 1.76 0.078 -.006639 1250196 cigs | -.4864302 0918935 -5.29 0.000 -.6666963 -.3061642 male | 3.176597 1.069279 2.97 0.003 1.079008 5.274186 parity | 1.805121 6025522 3.00 0.003 6231034 2.987138 white | 5.678583 1.365476 4.16 0.000 2.999949 8.357217 motheduc | 0793044 2562425 0.31 0.757 -.4233625 5819713 _cons | 107.921 3.523649 30.63 0.000 101.0087 114.8333 Write SRF Calculate R-square and explain the meaning of R-squared Test for the overall significance of the model Test for the significance of independent variables (faminc, cigs, male) with methods: critical value, p-value and confidence interval bwght faminc cigs parity male white motheduc int %8.0g byte byte byte byte byte %8.0g %8.0g %8.0g %8.0g %8.0g birth weight, ounces family income, $ cigs smked per day while preg birth order of child =1 if male child =1 if white mother's yrs of educ male + Gender has statistically significant effect on birth weight of babies Male has higher birth weight than female on average + In particular, with the sample we have, the estimated result shows that male has higher average birth weight compared with female by 3.176 ounce, holding other factors fixed cigs + Number of cigarretes smoked per day by mother while pregnancy has statistically significant effect on birth weight of babies + In particular, with the sample we have, the estimated result shows that another cigarretes smoked per day by mother while pregnancy will reduce birth weight of baby by 0.486 ounce, holding other factors fixed Faminc Family income has no statistically significant effect on birth weight of babies Bài Source | SS df MS -+ -Model | 309231.224 103077.075 Residual | 608623.281 84 7245.51526 -+ -Total | 917854.506 87 10550.0518 Number of obs F( 3, 84) Prob > F R-squared Adj R-squared Root MSE = = = = = = 88 0.0000 0.3132 85.121 -price | Coef Std Err t P>|t| [95% Conf Interval] -+ -bdrms | 57.68736 11.49826 lotsize | 0028554 0009058 colonial | -2.202985 20.66589 _cons | 63.47836 39.90354 storage display value variable name type format label variable label price float %9.0g house price, $1000s bdrms byte %9.0g number of bdrms lotsize float %9.0g size of lot in square feet colonial byte %9.0g =1 if home is colonial style Bài Source | SS df MS -+ -Model | 2.15692562 431385123 Residual | 17.2491738 135 127771658 -+ -Total | 19.4060994 140 138614996 Number of obs F( 5, 135) Prob > F R-squared Adj R-squared Root MSE = = = = = = 141 3.38 0.0066 0.1111 0.0782 35745 -colGPA | Coef Std Err t P>|t| [95% Conf Interval] -+ -soph | 3056211 2102452 1.45 0.148 -.1101791 7214214 male | -.0030009 0624554 -0.05 0.962 -.1265185 1205166 campus | -.1272217 0828543 -1.54 0.127 -.291082 0366386 clubs | 1072495 0625 1.72 0.088 -.0163563 2308552 skipped | -.0922866 0290847 -3.17 0.002 -.1498072 -.034766 _cons | 3.108133 0662338 46.93 0.000 2.977143 3.239123 -1 Write SRF Calculate R-square and explain the meaning of R-squared Test for the overall significance of the model Test for the significance of each independent variable with methods: critical value, p-value and confidence interval Soph =1 if sophomore Male = if male Campus = if living in campus Clubs = if joining a club Skipped: numbers of classes skipped Bài Source | SS df MS -+ -Model | 11170.8751 5585.43753 Residual | 37286.3735 170 219.331609 -+ -Total | 48457.2486 172 281.728189 Number of obs F( 2, 170) Prob > F R-squared Adj R-squared Root MSE = = = = = = 173 25.47 0.0000 0.2305 0.2215 14.81 -voteA | Coef Std Err t P>|t| [95% Conf Interval] -+ -democA | 9.192198 2.267415 4.05 0.000 4.716283 13.66811 expendA | 0242475 004022 6.03 0.000 016308 0321869 _cons | 37.87049 2.129706 17.78 0.000 33.66642 42.07457 democA voteA expendA byte byte float %3.2f %5.2f %8.2f =1 if A is democrat percent vote for A camp expends by A, $1000s Write SRF Calculate R-square and explain the meaning of R-squared Test for the overall significance of the model Test for the significance of each independent variable with methods: critical value, p-value and confidence interval; and explain the meaning of varibables Democrat Reject Ho - Being democrat has statistically significant effect on voteA - The estimated results show that being democrate will increase voteA by 9.19% compared with not being democrat, holding other factors fixed ExpendA Reject Ho - Expenditure on compaign of A has statistically significant effect on voteA - The estimated results show that when expenditure on compaign of A increase unit, voteA will increase by 0.024 unit Bài Source | SS df MS -+ -(SSE)Model | 309231.224 103077.075 (SSR)Residual | 608623.281 84 7245.51526 -+ -(SST)Total | 917854.506 87 10550.0518 Number of obs F( 3, 84) Prob > F R-squared Adj R-squared Root MSE = = = 88 0.0000 = = = 0.3132 85.121 -price | Coef Std Err t P>|t| [95% Conf Interval] -+ -bdrms | 57.68736 11.49826 lotsize | 0028554 0009058 colonial | -2.202985 20.66589 _cons | 63.47836 39.90354 storage display value variable name type format label variable label price float %9.0g house price, $1000s bdrms byte %9.0g number of bdrms lotsize float %9.0g size of lot in square feet colonial byte %9.0g =1 if home is colonial style Write SRF Calculate R-square and explain the meaning of R-squared Test for the overall significance of the model Test for the significance of each independent variable with methods: critical value and confidence interval Một số ví dụ câu hỏi trắc nghiệm: Your data produce the regression result: 𝑌̂𝑖 = + 5𝑋𝑖 If the x values were scaled by multiplying them by 0.5, the new intercept and slope estimates will be: a and 2.5 b and 2.5 c and 10 d 16 and 10 Consider the following estimated model: 𝑌̂𝑖 = -24.5 + 12𝑋2𝑖 − 8𝑋3𝑖 We observe that 𝑌𝑖 = 125, 𝑋2𝑖 = 13 and 𝑋3𝑖 = What is the value of the residual of this observation? a 33.5 b -15.5 c -46.5 d 69.5 a b c d Studying inflation in the United States from 1970 to 2006 is an example of using randomized controlled experiments time series data panel data cross-sectional data 4 a b c d Which of the following models allows the marginal effect of X on Y to change? 𝑌𝑖 = 𝛽1 + 𝛽2 𝑋𝑖 + 𝑢𝑖 Ln(𝑌𝑖 ) = 𝛽1 + 𝛽2 𝑋𝑖 + 𝛽3 𝑍𝑖 + 𝑢𝑖 𝑌𝑖 = 𝛽1 + 𝛽2 𝑋𝑖 + 𝛽3 𝑍𝑖 + 𝛽4 𝑍𝑖2 + 𝑢𝑖 None of the above .. .Bài reg wage educ exper nonwhite female married south, robust Linear regression Number of obs F(... _cons | -1.410051 8691299 -1.62 0.105 -3.117496 2973942 Bài Source | SS df MS -+ -Model | 28988.0176 5797.60351 Residual | 545486.724... R2 phù hợp mơ hình giải thích ý nghĩa biến cigs; male; motheduc với kiểm định t, khoảng tin cậy Bài Source | SS df MS Number of obs = 1260 -+ -F( 8, 1251) = 29.51 Model | 4341.73975