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find the local extrema and saddle point (if its exist)
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- TABLE BELOW SHOWS THE REGRESSION ESTIMATION RESULT FOR THE PRICE AND DEMAND VARIABLES INCLUDED IN THE PREVIOUS QUESTION. PLEASE ESTIMATE THE DEMAND OF THE PRODUCT WHEN THE PRICE IS 15. PLEASE EXPLAIN YOUR PEASON) Intersection (c) Price A32.13 OC685 0.451.5 Coefficients Standard t Stat Error 2,142429 14,04948 0,000781 0,206155 -7,5186 0,004876 30,1 -1,55 P-Valuecompute the least-squares regression line for predicting diatolic pressure (y)from systolic pressure(x) find the P-value Interpret the P-value state a conclusionDetail all the steps involved in testing the hypothesis below for the linear regression model y = XB + e, where X = (50 x 6) for two cases. Ho: X3B3 + x4B4 = 0 На: x3B3 + x4B4 # 0
- d and eProve this statement "Regression models used for forecasting need not have a causal interpretation".?If the slope of a regression line (b1) is 1.5, then a) for every unit of change in x, there is a change of 1.5 units in y b) for every unit of change in y, there is a change of 1.5 in x. c) the score of each case is 1.5 times higher on yt han on x d)y causes X
- Anarticle in Technometrics by S. C. Narula and J. F. Wallington Prediction, Linsar Regression, anda Minimum Sum of Relative Erro Vol. 19, 1977) presents data on the selling price (y) and annual taxes (x) for 24 houses. The taxes include local, school and county ta The data are shown in the following table. Sale Price/1000 Taxas/1000 25.9 4.9176 29.5 5.0208 27.9 4.5429 25.9 4.5573 29.9 5.0597 29.9 3.8910 30.9 5.8980 28.9 5.6039 35.9 5.8282 315 5.3003 31.0 6.2712 30.9 5.9592 30.0 5.0500 36.9 8.2464 419 6.6969 40.5 7.7841 43.9 9.0384 37.5 5.9894 37.9 7.5422 44.5 8.7951 37.9 6.0831 38.9 8.3607 36.9 8.1400 45.8 9.1416spss/minitabHi i have attached my question in the image thanks
- Square Feet Sum of Bedrooms and Bathrooms Age of the Home Sales Price Square Feet Residual Plot Square Feet Line Fit Plot 1,610 5 70 227,900 800,000 2,146 6 59 284,900 700,000 816 4 70 149,900 FO000 600,000 2,183 6.5 48 309,900 40000 1,046 5.5 64 134,900 500.000 20000 5,183 10.5 21 440,000 400,000 • 2 000 1,150 4 62 150,000 1,000 4,000 5.000 6,000 2000 0 300,000 1,068 70 154,900 4000 0 5,570 7 50 700,000 200,000 6000 0 2,449 6. 53 257,000 100,000 BO00 0 1,950 59 239,900 1000 00 2,630 7.5 73 349,900 Square Feet 1,000 2,000 3,000 4,000 5,000 6,000 Square Feet 2,732 7.5 20 339,900 1,908 5 46 289,000 3,666 6.5 17 399,900 Sum of Bedrooms and Bathrooms Residual Plot Sum of Bedrooms and Bathrooms Line Fit Plot 80000 1,878 7 19 290,000 800,000 2,172 62 278,000 60000 700,000 40000 600,000 SUMMARY OUTPUT 20000 500,000 400,000 Regression Statistics 12 2000 0 Multiple R 0.949366054 300,000 R Square 0.901295904 4000 0 200,000 Adjusted R Square 0.878518035 6000 0 100,000 Standard Error 47571.46177…The parameter that directly controls the amount of smoothing of a local regression is: Group of answer choices a)Span b)Both lambda and degrees of freedom c)Lambda d)None of the other options e)Degrees of freedom