1. The regression equation is
Y = B0 + B1 X1 + B2 X2 where B0, B1 and B2 are constants and coefficients of Xi's
2. The regression equation is
Y = - 2.3 + 1.88 X1 + 5.15 X2
3.
Given X1= 11 and X2 = 15
The regression equation is
Y = - 2.3 + 1.88 (11) + 5.15 (15) = 95.63
Residual = 75 - 95.63 = -20.63
4.
SSE = 17043
R-square = 0.808
5. 80.8% of variation in the y variable is explained by the independent variable X1 and X2
6. The 95% confdience interval for Y-intercept is (-42.05706898, 37.374361)
The 95% confidence interval for X1 is (1.521200409, 2.233389189)
The 95% confidence interval for x2 is (1.761117505, 8.548867588)
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Question 15 2 pts 5 1 0 Details 10 kW) 9 8 7 6 5 4 3 2 1 10 -9 -8 -7 -6 -5 - -3 -2 4 5 6 7 8 9 را به -5 -6 -7 -9 Given the function above, find the average rate of change for k from-5 to 0.
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1. Evaluate the following determinants: 8 7 3 4 0 2 (a) 4 0 1 (c) 6 0 3 6 03 8 2 3 ab (e) b c с a b 1 1 1 2 3 (b) 4 7 5 3 6 9 4 -2 (d) 8 11 4 х (f) 3 9 5 0 y 2 -18 7
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Python Question: In [76]: arr3d = np.array([[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]]) In [77]: arr3d Out[77]: array([[[ 1, 2, 3], [ 4, 5, 6]], [[ 7, 8, 9], [10, 11, 12]]]) In [78]: arr3d[0] Out[78]: array([[1, 2, 3], [4, 5, 6]]) Can someone tell me why arr3d[0] is a 2 × 3 array?
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