A chemical company wishing to study the effect of extraction time on theefficiency of an extraction operation, obtained the data shown in the followingtable.Extraction time( minutes ) x2745 4119353919491531Extractionefficiency (1%)y57648046627252775768Fit a straight line to the given data by the method of least squares and use itsto predict the extraction efficiency one can expect when the extraction timeis 35 minutes.

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Answered: Fit a straight line to the given data…

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 5E

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YOUR TURN 1 Calculate the least squares line for the following data. x 1 2 3 4 5 6 y 3 4 6 5 7 8
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.
Ordinary least squares method was used to fit a regression model to predict income (in thousands of dollars) from the following predictors: X1 = education X2 = gender (where male = 0 and female = 1) X3 = interaction between education and gender %3D The model produced the following coefficients: B, = -11.52, B, = 2.99, ß, = 1.01, ßz = -0.94. On average, how much income do men and women get if they have 20 years of education? Key A Men: $48.28 thousand; Women: $30.49 thousand B Men: $48.28 thousand; Women: $28.08 thousand C Men: $59.80 thousand; Women: $41.00 thousand D Men: $59.80 thousand; Women: $20.20 thousand
Interpret the least squares regression line of this data set. Meteorologists in a seaside town wanted to understand how their annual rainfall is affected by the temperature of coastal waters. For the past few years, they monitored the average temperature of coastal waters (in Celsius), x, as well as the annual rainfall (in millimetres), y. Rainfall statistics • The mean of the x-values is 11.503. • The mean of the y-values is 366.637. • The sample standard deviation of the x-values is 4.900. • The sample standard deviation of the y-values is 44.387. • The correlation coefficient of the data set is 0.896. The correct least squares regression line for the data set is: y = 8.116x + 273.273 Use it to complete the following sentence: The least squares regression line predicts an additional annual rainfall if the average temperature of coastal waters increases by one degree millimetres of Celsius.
A chemical company, seeking to study the effect of extraction time on the efficiency of an extraction operation, obtained the data from a table: Fit a straight line to the data given with the least squares method and use it to predict the extraction efficiency that would be expected when the extraction time is 35 minutes. x = Extraction time (minutes)y = Extraction efficiency (%)
An engineer wants to determine how the weight of a gas-powered car, x, affects gas mileage, y. The accompanying data represent the weights of various domestic cars and their miles per gallon in the city for the most recent model year. Complete parts (a) Find the least-squares regression line treating weight as the explanatory variable and miles per gallon as the response variable.
An engineer found that by including small amounts of a compound in rechargeable batteries for portable computers, she could extend their lifetimes. She experimented with different amounts of the additive and the data are Amount of additive Life (hours) 1 4 2. 3 3 4 a) Obtain the least squares fit of a straight line to the amount of additive. b) Test whether or not the slope B-0. Take a = 0.10 as your level of significance.
Apply the linear least-squares method to each set of data to obtain a mathematical model showing the relationships between variables.
A regression line was calculated to relate the length (cm) of newborn boys to their weight in kg. The least squares regression line is weight = -5.94 + 0.1875 length. Explain in words what this model means (slop and intercept) The new- born boy was 48 cm long, what is the predicted weight of this boy? It is known that the boy is weighed 3 kg. what was his residual? What does that say about him?
Use the least squares regression line of this data set to predict a value. Meteorologists in a seaside town wanted to understand how their annual rainfall is affected by the temperature of coastal waters. For the past few years, they monitored the average temperature of coastal waters (in Celsius), x, as well as the annual rainfall (in millimetres), y. Rainfall statistics • The mean of the x-values is 11.503. • The mean of the y-values is 366.637. • The sample standard deviation of the x-values is 4.900. • The sample standard deviation of the y-values is 44.387. • The correlation coefficient of the data set is 0.896. The least squares regression line of this data set is: y = 8.116x + 273.273 How much rainfall does this line predict in a year if the average temperature of coastal waters is 15 degrees Celsius? Round your answer to the nearest integer. millimetres
Use least squares regression to fit a line to the table data below.
An engineer is testing a new car model to determine how its fuel efficiency, measured in L/(100 km), is related to its speed, which is measured in km/hour. The engineer calculates the average speed for 30 trials. The average speed is an example of a (statistic or parameter) The engineer would like to find the least squares regression line predicting fuel used (y) from speed (x) for the 30 cars he observed. He collected the data below. Speed 62 65 80 82 85 87 90 96 98 100 Fuel 12 13 14 13 14 14 15 15 16 15 Speed 100 102 104 107 112 114 114 117 121 122 Fuel 16 17 16 17 18 17 18 17 18 19 Speed 124 127 127 130 132 137 138 142 144 150 Fuel 18 19 20 19 21 23 22 23 24 26 The regression line equation is Round each number to four decimal places.

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Fit a straight line to the given data by the method of least squares and use its to predict the extraction efficiency one can expect when the extraction time is 35 minutes.