Fit a second-degree parabola to the following data by least squares method. Also calculate the least error. 1935 1936 1937 1931 357 1929 1930 1932 1933 1934 у 1 352 356 358 360 361 361 360 359
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A: Given that y = -0.5x + 108 Here y is IQ score and x is shoe size To compute x when y=102
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- The position of a particle x(t) on an axis has been monitored. The results are shown in the following table. By the least squares method, fit the data to a quadratic model: Report the position predicted by the model for t = 10.Use the least-squares equation for the table below to predict the depth of an earthquake that measures 3.8 on the Richter scale? x = earthquake magnitude 2.9 4.2 3.3 4.5 2.6 3.2 3.4 y = depth of earthquake (in km) 5 10 11.2 10 7.9 3.9 5.5If the least squares line has an equation of y = -0.5x + 108, find the estimated shoe size for a man with an IQ score of 102. Group of answer choices A.) size 9 B.) size 10 C.) size 11 D.) size 12
- The following Minitab display gives information regarding the relationship between the body weight of a child (in kilograms) and the metabolic rate of the child (in 100 kcal/ 24 hr). Predictor Coef SE Coef T P Constant 0.8435 0.4148 2.06 0.84 Weight 0.40133 0.02978 13.52 0.000 S = 0.517508 R-Sq = 95.6% (a) Write out the least-squares equation. = + x (b) For each 1 kilogram increase in weight, how much does the metabolic rate of a child increase? (Use 5 decimal places.)(c) What is the value of the correlation coefficient r? (Use 3 decimal places.)The position of a particle x(t) on an axis has been monitored. The results are shown in the following table. t x(t)0 41 114 129 12 By the least squares method, fit the data to a quadratic model: Report x(10)Use the least-squares equation for the table below to predict the depth of an earthquake that measures 3.8 on the Richter scale? x = earthquake magnitude 2.9 4.2 3.3 4.5 2.6 3.2 3.4 y = depth of earthquake (in km) 5 10 11.2 10 7.9 3.9 5.5 A. 9.13 B. 8.73 C. 8.62 D. 8.43
- Find the best quadratic least squares fit to the data x 0 1 2 3 y 3 2 4 4Fit a curve of the form y = abx in least square sence to the data given below:Given the following data, determine which model is the better fit to the data. Show any graphs or calculations you used to come to this conclusion. This can be done using Excel and the Non Linear Least Squares Curve Fitting program. Model 1 y=a exp(-bx) Model 2 y=a exp(-bx) + c x y 0 530 1 203.9 2 100.1 3 57.6 4 37.3 5 34.8 6 29.7 7 31.2
- The following Minitab display gives information regarding the relationship between the body weight of a child (in kilograms) and the metabolic rate of the child (in 100 kcal/ 24 hr). Predictor Coef SE Coef T PConstant 0.8570 0.4148 2.06 0.84Weight 0.38243 0.02978 13.52 0.000 S = 0.517508 R-Sq = 97.4% (a) Write out the least-squares equation. y^= ______ + _____x (b) For each 1 kilogram increase in weight, how much does the metabolic rate of a child increase? (Use 5 decimal places.)Use the diagnostic plots to provide answers to the following questions, no justification necessary.Is there serious ground to include a quadratic term in the model? YES/ NOIs there serious ground to doubt the homoscedasticity assumption? YES/ NOIs there serious ground to doubt that the errors are normal? YES/ NOAn owner of a home in the Midwest installed solar panels to reduce heating costs. After installing the solar panels, he measured the amount of natural gas used ? (in cubic feet) to heat the home and outside temperature ? (in degree‑days, where a day’s degree‑days are the number of degrees its average temperature falls below 65 ∘F ) over a 23-month period. He then computed the least‑squares regression line for predicting ? from ? and found it to be ?̂ =85+16?. By looking at the equation of the least‑squares regression line, you can see that the correlation between amount of gas used and degree‑days is