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- Respiratory Rate Researchers have found that the 95 th percentile the value at which 95% of the data are at or below for respiratory rates in breath per minute during the first 3 years of infancy are given by y=101.82411-0.0125995x+0.00013401x2 for awake infants and y=101.72858-0.0139928x+0.00017646x2 for sleeping infants, where x is the age in months. Source: Pediatrics. a. What is the domain for each function? b. For each respiratory rate, is the rate decreasing or increasing over the first 3 years of life? Hint: Is the graph of the quadratic in the exponent opening upward or downward? Where is the vertex? c. Verify your answer to part b using a graphing calculator. d. For a 1- year-old infant in the 95 th percentile, how much higher is the walking respiratory rate then the sleeping respiratory rate? e. f.a. Show that the regression R2 in the regression of Y on X is the squaredvalue of the sample correlation between X and Y. That is, show thatR2 = r2XY.b. Show that the R2 from the regression of Y on X is the same as the R2from the regression of X on Y. c. Show that ^β1 = rXY (sY/sX), where rXY is the sample correlationbetween X and Y, and sX and sY are the sample standard deviationsof X and Y.55. Explain from the data given below whether A and B are independent. positively associated or negatively associated : N=10,000, (A) = 4500, (B) = 6000, (AB) = 3150. () (A) = 150, (a) = 250, (B) = 20o, (aB) = 125. %3D %3D %3D
- 2) Use Data Linearization technique to perform a fit in the form of y = (x^B)-¹ using the given data. x y 0.5 1.333 0.9 0.4115 0.333 1.5 0.2314. Show that the vector of observed residuals can be expressed as e = (I – H)e.Given the “data” determined by y = x^3 + (x-1)^2 with x = 0.1, 1.2, 2.3, and 2.9, calculate SSTO, SSR, and R^2. Then recalculate these using x = 0.3, 1, 2.45, and 2.8. Does where you collect your data (i.e., which values of x) appear to impact your interpretation of how good the linear model fits?.
- Q/Fit (y = ax² + (b/x)) to the following data: (x:1, 2, 3, 4), (y: -1.51, 0.99, 3.88, 7.66). *Find the equation of the regression that model the relationship between the weight of mail and number of order using HYPERBOLIC EQUATION. Compute for the correlation coefficient using PEARSON PRODUCT MOMENT CORRELATION COEFFICIENT (PPMCC).Identify the equation of the normal line to the graph of the equationy = 4/x2at point (2,1)
- Find the equation of the normal line to the curve y=(1+2x)^2 at (2,25)For the following data. i_xi_fi 00 1 13 4 267 39 9 4 12 15 i) ii) if data points at i=1,2,3 are considered, write the followings: -the first differences=? -the second differences=? -P₂(s)=? in terms of s, and the first and second differences. -S=? df(x=6)/dx=? By using only two data points.The least squares regression line for a set of data is calculated to be y = 24.8 + 3.41x. (a) One of the points in the data set is (4, 37). Calculate the predicted value. (b) For the point in part (a), calculate the residual.