Answered: In an experiment the following…

In an experiment the following correspondence was found between temperature T (in °C) and electrical resistance R (in ΜΩ):

Linear Algebra: A Modern Introduction

4th Edition

ISBN:9781285463247

Author:David Poole

Publisher:David Poole

Chapter7: Distance And Approximation

Section7.3: Least Squares Approximation

Problem 34EQ

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In Galton’s height data (Figure 7.1, in Section 7.1), the least-squares line for predictingforearm length (y) from height (x) is y = −0.2967 + 0.2738x.a) Predict the forearm length of a man whose height is 70 in.b) How tall must a man be so that we would predict his forearm length to be 19 in.?c) All the men in a certain group have heights greater than the height computed in part(b). Can you conclude that all their forearms will be at least 19 in. long? Explain.
The position of a particle x(t) on an axis has been monitored. The results are shown in the following table. t x(t)0 41 114 129 12 By the least squares method, fit the data to a quadratic model: Report x(10)
Suppose the least squares regression line for predicting weight (in pounds) from height (in inches) is given by Weight= -110+3.5*(height) Which of the following statements is correct? l. A person who is 61 inches tall will weigh 103.5 pounds ll. For each additional inch of height, weight will decrease on average by 3.5 pounds. lll. There is a negative linear relationship between height and weight. a) l and lll only b) l and ll only c) ll only d) l only e) ll and lll only
Each of 25 teenage girls with one brother was asked to provide her own height (y), in inches, and the height (x), in inches, of her brother. The scatterplot below displays the results. Only 22 of the 25 pairs are distinguishable because some of the (x,y) pairs were the same. The equation of the least-squares regression line is ŷ = 35.1 + 0.427x.*Girls are on the y-axis and brothers are on the x-axis* a.) Draw the least-squares regression line on the scatterplot above. b.) One brother’s height was x = 67 inches and his sister’s height was y = 61 inches. Circle the point on the scatterplot above that represents this pair and draw the segment on the scatterplot that corresponds to the residual for it. Give a numerical for the residual. c.) Suppose the point x = 84 , y = 71 is added to the data set. Would the slope of the least squares regression line increase, decrease, or remain about the same?Explain. Would the correlation increase, decrease, or remain about the same? Explain.
Find the least-squares equation for the following pairs of data: x = earthquake magnitude 2.9 4.2 3.3 4.5 2.6 3.2 3.4 y = depth of earthquake (in km) 5 10 11.2 10 7.9 3.9 5.5 A. y = 2.16 + 0.221x B. y = 0.221 + 2.16x C. y = 2.16 + 0.312x D. y = 0.221 + 2.82x
A local tire dealer wants to predict the number of tires sold each month. He believes that the number of tires sold is a linear function of the amount of money invested in advertising. He randomly selects 6 months of data consisting of tire sales (in thousands of tires) and advertising expenditures (in thousands of dollars). Based on the data set with 6 observations, the simple linear regression equation of the least squares line is ŷ = 3 + 1x. EX= 24 EX = 124EY= 42EY = 338 EXY = 196MSE = 4 Using the sums of the squares given above, determine the 90 percent confidence interval for the mean value of monthly tire sales when the advertising expenditure is $5.000. distance value = .20238 a. (6.08, 9.92) b. (3.235, 6.765) c. (2.465, 6.853) d. (5.325, 7.675)
We use the form = a + bx for the least-squares line. In some computer printouts, the least-squares equation is not given directly. Instead, the value of the constant a is given, and the coefficient b of the explanatory or predictor variable is displayed. Sometimes a is referred to as the constant, and sometimes as the intercept. Data from Climatology Report No. 77-3 of the Department of Atmospheric Science, Colorado State University, showed the following relationship between elevation (in thousands of feet) and average number of frost-free days per year in Colorado locations. A Minitab printout provides the following information. Predictor Coef SE Coef T P Constant 318.24 28.31 11.24 0.002 Elevation -30.327 3.511 -8.79 0.003 S = 11.8603 R-Sq = 95.8% Notice that "Elevation" is listed under "Predictor." This means that elevation is the explanatory variable x. Its coefficient is the slope b. "Constant" refers to a in the equation = a + bx. (a) Use the printout to write the…
We use the form = a + bx for the least-squares line. In some computer printouts, the least-squares equation is not given directly. Instead, the value of the constant a is given, and the coefficient b of the explanatory or predictor variable is displayed. Sometimes a is referred to as the constant, and sometimes as the intercept. Data from Climatology Report No. 77-3 of the Department of Atmospheric Science, Colorado State University, showed the following relationship between elevation (in thousands of feet) and average number of frost-free days per year in Colorado locations. Minitab output is provided below. Predictor Coef SE Coef T P Constant 318.16 28.31 11.24 0.002 Elevation −30.878 3.511 −8.79 0.003 S = 11.8603 R-Sq = 96.3% Notice that "Elevation" is listed under "Predictor." This means that elevation is the explanatory variable x. Its coefficient is the slope b. "Constant" refers to a in the equation = a…
We use the form = a + bx for the least-squares line. In some computer printouts, the least-squares equation is not given directly. Instead, the value of the constant a is given, and the coefficient b of the explanatory or predictor variable is displayed. Sometimes a is referred to as the constant, and sometimes as the intercept. Data from Climatology Report No. 77-3 of the Department of Atmospheric Science, Colorado State University, showed the following relationship between elevation (in thousands of feet) and average number of frost-free days per year in Colorado locations. A Minitab printout provides the following information. Predictor Coef SE Coef T P Constant 318.24 28.31 11.24 0.002 Elevation -30.327 3.511 -8.79 0.003 S = 11.8603 R-Sq = 95.8% (a) Use the printout to write the least-squares equation. = ?+ ?x (b) For each 1000-foot increase in elevation, how many fewer frost-free days are predicted? (Use 3 decimal places.)
Each of the following pairs represents the number of licensed drivers (X) and the number of cars (Y) for seven houses in my neighborhood: Drivers X Cars Y 5 4 5 3 2 2 2 2 3 2 1 1 2 2 Construct a scatterplot to verify a lack of pronounced curvilinearity. Determine the least squares equation for these data. (Remember, you will first have to calculate r, SSy, and SSx) Determine the standard error of estimate, sy|x, given that n = 7. Predict the number of cars for each of two new families with two and five drivers.
The following Minitab display gives information regarding the relationship between the body weight of a child (in kilograms) and the metabolic rate of the child (in 100 kcal/ 24 hr). Predictor Coef SE Coef T P Constant 0.8381 0.4148 2.06 0.84 Weight 0.38108 0.02978 13.52 0.000 S = 0.517508 R-Sq = 97.6% (a) Write out the least-squares equation. = + x (b) For each 1 kilogram increase in weight, how much does the metabolic rate of a child increase? (Use 5 decimal places.) (c) What is the value of the correlation coefficient r? (Use 3 decimal places.)
Given the table of data points x −1 1 2 y 1 3 3 find the best least squares fit by a linear function f (x) = c1 + c2x.