Problem 4. Let the input a be the total seasonal depth of applied water in inches, and let the output y be the average yield of alfalfa in tons per acre. The data given by the table below was compiled by the UC Experimental Station. I 12 18 24 30 36 y 5.27 5.68 6.25. 7.21 8.20 42 8.71 Compute the formula for the empirical regression line corresponding to this data. This is a problem in Chapter 14 of the text.

Problem 4. Let the input a be the total seasonal depth of applied water in inches, and let the output y be the average yield of alfalfa in tons per acre. The data given by the table below was compiled by the UC Experimental Station. I 12 18 24 30 36 y 5.27 5.68 6.25. 7.21 8.20 42 8.71 Compute the formula for the empirical regression line corresponding to this data. This is a problem in Chapter 14 of the text.

Functions and Change: A Modeling Approach to College Algebra (MindTap Course List)

6th Edition

ISBN:9781337111348

Author:Bruce Crauder, Benny Evans, Alan Noell

Publisher:Bruce Crauder, Benny Evans, Alan Noell

Chapter5: A Survey Of Other Common Functions

Section5.6: Higher-degree Polynomials And Rational Functions

Problem 1TU: The following fictitious table shows kryptonite price, in dollar per gram, t years after 2006. t=...

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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?
The following fictitious table shows kryptonite price, in dollar per gram, t years after 2006. t= Years since 2006 0 1 2 3 4 5 6 7 8 9 10 K= Price 56 51 50 55 58 52 45 43 44 48 51 Make a quartic model of these data. Round the regression parameters to two decimal places.
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.
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?
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STER. 1. Wine Consumption. The table below gives the U.S. adult wine consumption, in gallons per person per year, for selected years from 1980 to 2005. a) Create a scatterplot for the data. Graph the scatterplot Year Wine below. Consumption 2.6 b) Determine what type of model is appropriate for the 1980 data. 1985 2.3 c) Use the appropriate regression on your calculator to find a Graph the regression equation in the same coordinate plane below. d) According to your model, in what year was wine consumption at a minimum? A e) Use your model to predict the wine consumption in 2008. 1990 2.0 1995 2.1 2000 2.5 2005 2.8
The amounts of a chemical compound y that dissolved in 100 grams of water at various temperatures x were recorded. Use the given data to complete parts (a) through (c) below. Click the icon to view the recorded data. (a) Find the equation of the regression line 9-x (Round the constant to two decimal places as Chemical compound data x (degrees C) 0 10 20 30 40 50 748854 12 26 30 49 y (grams) 6002385 33 152844 7 22 - X
A population data set produced the following information. n= 250 Ex = 9290 Ey = 1418 xy = 85010 and E = 480730 (a) Determine the population regression line. Round the coefficients to four decimal places. i (b) Predict the value of y if x is 30. Round the answer to four decimal places. y = i
For a sample of over 160 college students, the following variables were measured: Y = height X1 = mother’s height X2 = father’s height X3 = 1 if male, 0 if female The goal is to predict student’s height using the mother’s and father’s heights, and sex, where sex is categorized using the variable “male” = 1 if male, 0 if female. Question 1 The regression model is __________________________________________ Regress command results (remember that we now have n = 165 cases; we removed one outlier): Source | SS df MS Number of obs = 165 -------------+------------------------------ F( 3, 161) = 104.38 Model | 1679.17741 3 559.725803 Prob > F = 0.0000 Residual | 863.31653 161 5.36221447 R-squared = 0.6604 -------------+------------------------------ Adj R-squared = 0.6541 Total | 2542.49394 164 15.5030118 Root MSE = 2.3156…
Table 1: ANOVA Model Sum of Squares df Mean Square F Sig. 1 Regression 35.428 4 8.857 38.005 .000 Residual 57.097 245 .233 Total 92.525 249 a. Dependent Variable: Teacher's Motivation b. Predictors: (Constant), Rewards & Incentives, Individual Characteristics, Employee Training, Work Environment Table 2: Model Summary Model R R Adjusted R Std. Error of Square Square the Estimate 1 .619a .383 .373 .48275 Table 3: Coefficient Analysis Model Unstandardized Standardized Sig. Collinearity Coefficients Coefficients Statistics B Std. Beta Tolerance VIF Error 1 (Constant) .255 .310 .825 .410 Rewards & .434 .073 .346 5.971 .000 .749 1.335 Incentives Individual .305 .083 .215 3.673 .000 .732 1.366 Characteristics Employee .170 .050 .188 3.387 .001 .821 1.218 Training Work .090 .055 .089 1.648 .101 .869 1.151 Environment A multiple regression analysis output above are based on Determinants of Teacher's Motivation in Higher Education Institution in Sri Lanka. (Farouk, 2019) a. Analyze Table 1 and…
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