Use SAS output for a random sample of size 20 to answer the following questions.Regression of yon X and X₂Regression of y on X₁ X₁₂, and X₂3F1.17, 0.99 = 8.40F3,16, 0.99 = 5.29F1.17, 0.95 = 4.45F3,16, 0.95 = 3.24d) is the overall regression of the model that includes the three predictors significant? Use a=0.01O Reject the null hypothesisO Do not reject the null hypothesisSourceModelErrorParameterInterceptx1x2x3Corrected Total 19 1855.20200Source DF111x1x2x3Number of Observations Read 20Number of Observations Used 20R-Square Coeff Var Root MSE0.818318 22.31297Analysis of Variancex1x2x3Sum of MeanSourceModelErrorDF Squares2 1317.79841 658.8992117 537.40359 31.61198Square F Value Pr > F20.84 <.0001Type I SS8.352481StandardEstimateError-36.76492528 7.010925770.00076294 0.000636301.19217421 0.561653914.71982137 1.530475471309.445931200.346530y Mean4.589778 20.57000Source DFType III SS130.2855361194.91291621 200.3465296DF Sum of Squares Mean Square F Value Pr > F31518.144941 506 048314 24.02 0001337.067069 21.0660661855.20200016Corrected Total 19F2,17, 0.99 = 6.11F4.16, 0.99 = 4.77F2,17, 0.95 = 3.59F4.16, 0.95 = 3.01

As per the given information, there are three predictor variables in the model. X1,X2 and X3. The…

Answered: d) is the overall regression of the…

Calculus For The Life Sciences

2nd Edition

ISBN:9780321964038

Author:GREENWELL, Raymond N., RITCHEY, Nathan P., Lial, Margaret L.

Publisher:GREENWELL, Raymond N., RITCHEY, Nathan P., Lial, Margaret L.

Chapter1: Functions

Section1.2: The Least Square Line

Problem 8E

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Find the equation of the regression line for the following data set. x 1 2 3 y 0 3 4
Olympic Pole Vault The graph in Figure 7 indicates that in recent years the winning Olympic men’s pole vault height has fallen below the value predicted by the regression line in Example 2. This might have occurred because when the pole vault was a new event there was much room for improvement in vaulters’ performances, whereas now even the best training can produce only incremental advances. Let’s see whether concentrating on more recent results gives a better predictor of future records. (a) Use the data in Table 2 (page 176) to complete the table of winning pole vault heights shown in the margin. (Note that we are using x=0 to correspond to the year 1972, where this restricted data set begins.) (b) Find the regression line for the data in part ‚(a). (c) Plot the data and the regression line on the same axes. Does the regression line seem to provide a good model for the data? (d) What does the regression line predict as the winning pole vault height for the 2012 Olympics? Compare this predicted value to the actual 2012 winning height of 5.97 m, as described on page 177. Has this new regression line provided a better prediction than the line in Example 2?
For the following table of data. x 1 2 3 4 5 6 7 8 9 10 y 0 0.5 1 2 2.5 3 3 4 4.5 5 a. draw a scatterplot. b. calculate the correlation coefficient. c. calculate the least squares line and graph it on the scatterplot. d. predict the y value when x is 11.
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.

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d) is the overall regression of the model that includes the three predictors significant? Use a 0.01- O Reject the null hypothesis O Do not reject the null hypothesis