Problem 2: In modeling a combustion process it is required to find enthalpy as afunction of temperature. Find linear splines interpolation for the following data.T6080100120140160E0.017.245.292.9178.8349.41. Find E(85), and E(105)2. Plot the data and the splines on the same figure3. What conclusion can be drawn from the figure?

Answered: Image /qna-images/answer/ff7eac41-7848-4c9c-8a8e-018996c9d93e.jpg

Answered: Problem 2: In modeling a combustion…

Glencoe Algebra 1, Student Edition, 9780079039897, 0079039898, 2018

18th Edition

ISBN:9780079039897

Author:Carter

Publisher:Carter

Chapter10: Statistics

Section10.2: Representing Data

Problem 24PFA

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Table 6 shows the population, in thousands, of harbor seals in the Wadden Sea over the years 1997 to 2012. a. Let x represent time in years starting with x=0 for the year 1997. Let y represent the number of seals in thousands. Use logistic regression to fit a model to these data. b. Use the model to predict the seal population for the year 2020. c. To the nearest whole number, what is the limiting value of this model?
What does the y -intercept on the graph of a logistic equation correspond to for a population modeled by that equation?
Find the mean hourly cost when the cell phone described above is used for 240 minutes.
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?
A tool and die maker grinds a pin to an 18.25-millimeter diameter as shown in Figure 29-13.The pin is to be pressed (an interference fit) into a hole. The minimum interference permittedis 0.03 millimeter. The maximum interference permitted is 0.07 millimeter. Determine the mean diameter of the hole. All dimensions are in millimeters.
Planetary Velocity The following table gives the mean velocity of planets in their orbits versus their mean distance from the sun. Note that 1AU astronomical unit is the mean distance from Earth to the sun, abut 93 million miles. Planet d=distance AU v=velocity km/sec Mercury 0.39 47.4 Venus 0.72 35.0 Earth 1.00 29.8 Mars 1.52 24.1 Jupiter 5.20 13.1 Saturn 9.58 9.7 Uranus 19.20 6.8 Neptune 30.05 5.4 Astronomers tell us that it is reasonable to model these data with a power function. a Use power regression to express velocity as a power function of distance from the sun. b Plot the data along with the regression equation. c An asteroid orbits at a mean distance of 3AU from the sun. According to the power model you found in part a, what is the mean orbital velocity of the asteroid?
Hrs.5
pan's high population density has resulted in a multitude of resource-usage problems. One especially serious difficulty concerns waste removal. An article reported the development of a new compression machine for processing sewage sludge. An important part of the investigation involved relating the moisture content of compressed pellets (y, in %) to the machine's filtration rate (x, in kg-DS/m/hr). The following data was read from a graph in the article. x 125.8 98.1 201.4 147.3 145.9 124.7 112.2 120.2 161.2 178.9 159.5 145.8 75.1 151.5 144.2 125.0 198.8 133.9 y 77.9 76.8 81.5 79.8 78.2 78.3 77.5 77.0 80.1 80.2 79.9 79.0 76.9 78.2 79.5 78.1 81.5 71.0 (a) Determine the slope and intercept of the estimated regression line. (Round your answers to 5 decimal places, if needed.)slope: intercept: (b) Does there appear to be a useful linear relationship? Carry out a test using the ANOVA approach and a significance level of 0.05. State the appropriate null and alternative hypotheses.…
This question had 6 parts and I was ask to resend the last 3. the question is The line of best fit for a data set is y= 1.1x+3.4. Find the residual for each of the coordinate pairs (x,y).
Step 1 (a) Compute MSR and MSE. The MSR, mean square due to regression is calculated as follows where SSR is the sum of squares due to regression and p is the number of independent variables in the estimated regression equation. SSR MSR = The estimated regression equation was given to be ý = 26.1470 + 0.5802x, + 0.499x,. This equation has two independent variables, so p = %3D The value of SSR was given to be SSR = 6,227.375. Use these to find the value of MSR, rounding the result to three decimal places. SSR MSR = 6,227.375
please show calc functions ti-84
Please help with 5.2 only.

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