d. Test the statistical significance of each estimated regressions coefficient using a = 5% (two – tail) e. Test at a = 5% that all partial slope coefficients are equal to zero.
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I need solutions for d) and e). Please.
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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.Find the simple regression line y=α+βx for the pairs of points belonging to the independent and dependent variables (xi,yi) , respectively. Also, interpret the result by calculating the Pearson correlation coefficient. Please dont use excelA random sample of 102 observations is selected to estimate the relationship between the price of a used car (y) and its odometer readings (x). The estimated simple regression line is: ý = 6533 – 0.03x (0.004) where the value 0.004 in bracket is the standard error of the point estimator for the slope in the regression model. Question 15. What percent of the variability of the used car values can be explained using this model? O 60% O 36% O 80% O 64% O 95% O o o o
- please do all parts! The estimated regression equation for this data set is y=4.4878+1.9549x. Part A: (in image) Part B: is the linear function is the appropriate regression function for this data set? Part C: do the residuals have a constant variance? Part D: are the residuals independent? Part E: are the error terms are normally distributed? y x22 821 818 846 2241 2254 2276 3258 3268 32find the (a) explained variation, (b) unexplained variation, and (c) indicated prediction interval. In each case, there is sujficient evidence to support a claim of a linear correlation, so it is reasonable to use the regression equation when making predictions. Altitude and Temperature Listed below are altitudes (thousands of feet) and outside air temperatures (°F) recorded by the author during Delta Flight 1053 from New Orleans to Atlanta. For the prediction interval, use a 95% confidence level with the altitude of 6327 ft (or 6.327 thousand feet).In a partially destroyed laboratory, record of an analysis of correlation, data, the following results only are legible:Variance of X=9. Regression equation: 8X –10Y + 66=0, 40X–18Y = 214.(1) the mean value X and Y,(2) the correlation coefficient between X and Y, and (3) the standard deviation of Y ?
- Suppose that a regional express delivery service company wants to estimate the cost of shipping a package (Y) as a function of cargo type, where cargo type includes the following possibilities: fragile, semi-fragile, and durable. Costs for 15 randomly chosen packages of approximately the same weight and same distance shipped, but of different cargo types, are provided in the file P14_16.xlsx. a. Estimate a regression equation using the given sample data, and interpret the estimated regression coefficients. b. According to the estimated regression equation, which cargo type is the most costly to ship? Which cargo type is the least costly to ship? c. How well does the estimated equation fit the given sample data? How might the fit be improved? d. Given the estimated regression equation, predict the cost of shipping a package with semi-fragile cargo.Which of the following is not a plot of residuals typically used in multiple regression analysis? Select one: None of these Residuals versus correlation coefficients Residuals versus X1 Residuals versus time Residuals versus X2.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)
- The following sample contains the scores of 6 students selected at random in Mathematics and English. Use the scores in English as the dependent variable Y. Mathematics score (X) 70 92 80 74 65 83 English score (Y) 74 84 63 87 78 90 ∑x=464, ∑y=476,∑x^2=36354,∑y^2=38254, ∑xy=36926. Estimate the regression parameters and also write the prediction equation.An educational consultant collected data from 10 school districts. Measures were taken of the number of hours per week of instructional time that were allocated to reading instruction at the district level (X) and the district mean achievement in reading (Y). Summary values from the raw data are given as follows: ∑X= 58, ∑Y= 60, ∑X²= 410 ∑Y²= 398, ∑XY= 299 Set up the regression equation for the prediction of Y from X. Determine the standard error of estimate for predicting Y. If X = 9, what will be the predicted value of Y? Determine 95% confidence interval for the predicted value of Y for X = 9.Which of the non-parametric test for ordinal data is the best to use in the given scenario? An experiment was conducted to compare the strengths of two types of elastic bandages: one a standard bandage of a specified weight and the other the same standard but treated with a chemical substance. Ten pieces of each were randomly selected from production. Does the treated bandage tend to be stronger than the standard? Table 1. Strength measurements (and their ranks) for 2 types of bandages. STANDARD TREATED 1.21(2) 1.49(15) 1.43(12) 1.37(7.5) 1.35(6) 1.67(20) 1.51(17) 1.50(16) 1.39(9) 1.31(5) 1.17(1) 1.29(3.5) 1.48(14) 1.52(18) 1.42(11) 1.37(7.5) 1.29(3.5) 1.44(13) 1.4(10) 1.53(19) a. Mood median test b. sign test c. Wilcoxon rank-sum test d. Wilcoxon matched-pairs signed-ranks test e. Spearman and Kendall correlation coefficients f. Kruskal-Wallis test