Compute Var(y3) for the following model, where e ~ wn(0,0.01), i.e., a white noise process with mean zero and variance 0.01. Yt =1+0.5y-1+Et, Y0 = 1. Please give the exact answer.
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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.Y, = XB + ɛ, Show that the model variance in model Yi unbiased estimator ofInterpret the least squares regression line of this data set. Meteorologists in a seaside town wanted to understand how their annual rainfall is affected by the temperature of coastal waters. For the past few years, they monitored the average temperature of coastal waters (in Celsius), x, as well as the annual rainfall (in millimetres), y. Rainfall statistics • The mean of the x-values is 11.503. • The mean of the y-values is 366.637. • The sample standard deviation of the x-values is 4.900. • The sample standard deviation of the y-values is 44.387. • The correlation coefficient of the data set is 0.896. The correct least squares regression line for the data set is: y = 8.116x + 273.273 Use it to complete the following sentence: The least squares regression line predicts an additional annual rainfall if the average temperature of coastal waters increases by one degree millimetres of Celsius.
- Find the minimum mean square error forecast Y(1), forecast error e, (1) and Varfe, (1)1 for the following modes. Y, = 0.8Y, +e,. Y, = 3+21+e,.Use the least squares regression line of this data set to predict a value. Meteorologists in a seaside town wanted to understand how their annual rainfall is affected by the temperature of coastal waters. For the past few years, they monitored the average temperature of coastal waters (in Celsius), x, as well as the annual rainfall (in millimetres), y. Rainfall statistics • The mean of the x-values is 11.503. • The mean of the y-values is 366.637. • The sample standard deviation of the x-values is 4.900. • The sample standard deviation of the y-values is 44.387. • The correlation coefficient of the data set is 0.896. The least squares regression line of this data set is: y = 8.116x + 273.273 How much rainfall does this line predict in a year if the average temperature of coastal waters is 15 degrees Celsius? Round your answer to the nearest integer. millimetresDetermine the mean waiting time W for an M/M/2 system when lambda = 2 and μ = 1.2. Compare this with the mean waiting time in an M/M/1 system whose arrival rate is lambda = 1 and service rate is μ = 1.2. Since the arrival rate per server is the same in both cases and service times don’t vary, should the wait times be the same in both cases?
- 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.Explain why Y is considered the least squares estimator of the mean of Y, µy.Compute the least-squares regression line for predicting y from x given the following summary statistics. Round the slope and y-intercept to at least four decimal places. x = 12.5 sx = 2.2 y = 1400 sy = 1.8 r = 0.50 Regression line equation: y^ = ___. image attached bellow for better view.
- Curing times in days (x) and compressive strengths in MPa (V) were recorded for several concrete specimens. The means and standard deviations of the x and y values were * = 5, s, = 2, 5 = 1350, s, = 100. The correlation between curing time and compressive strength was computed to be r = 0.7. Find the equation of the least-squares line to predict compressive strength from curing time.Show the best fitted line on scatter diagram and Find the predicted value for each y using the exposure time and the equation obtained in part b (b. Find the equation of regression line between radiation doses on exposure time .usingleast square method)I ONLY NEED PART C,D, and E answered please thanks A regression was run to determine if there is a relationship between the happiness index (y) and life expectancy in years of a given country (x).The results of the regression were: ˆyy^=a+bxa=-1.102b=0.074 (a) Write the equation of the Least Squares Regression line of the formˆyy^= + x(b) Which is a possible value for the correlation coefficient, rr? -0.858 -1.07 1.07 0.858 (c) If a country increases its life expectancy, the happiness index will decrease increase (d) If the life expectancy is increased by 2.5 years in a certain country, how much will the happiness index change? Round to two decimal places._____(e) Use the regression line to predict the happiness index of a country with a life expectancy of 63 years. Round to two decimal places._______Use the space below to type your answer AND/OR to upload a picture of your work for all the questions in this problem.
