A temperature dataset gives y mean April temperature (Fahrenheit) and x = geographic latitude for 20 U.S. cities. The simplelinear regression equation for the sample is ŷ = 119 - 1.64x.(a) The value of latitude for a certain city is 41. What is the predicted April temperature for this city? (Round your answerto the nearest tenth.)degrees Fahrenheit(b) The mean April temperature for this city is 48. What is the residual for the city? (Round your answer to the nearesttenth.)degrees FahrenheitYou may need to use the appropriate table in the Appendix of Tables to answer this question.

Answered: A temperature dataset gives y = mean…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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1. Develop a simple linear regression equation for starting salaries using an independent variable that has the closest relationship with the salaries. Explain how you chose this variable.
The table below shows the average weekly wages (in dollars) for state government employees and federal government employees for 10 years, construct and interpret a 98% prediction interval for the average weekly wages of federal government employees when the average weekly wages of state government employees is $888 the equation of the regression line is y= 1.453x-47.386. Wages(state), X | 739|760|784|822|898|921|937|954|965| Wages (federal), Y |1,036|1,054|1,105|1,136|1,192|1,253|1,266|1,327|1,400|
I need help with this last part of this exercise.
The applet displays a scatterplot with the following points: (7,10),(27,35),(24,25),(8,12),(10,19),(15,22). Identify the slope of the regression line, intercept of the regression line, and correlation coefficient. Report your answers accurate to within two decimal places.
Daphne, who graduated from one of the prestigious schools in Cebu, was asked by her boss to estimate the demand of the customer for the succeeding month. The resulting estimated regression equation is y = 1.304 + 0.602x, with the following range of independent variables: 0, 5, 8, 13, 16, 18, and 23. How will her boss interpret the estimated intercept?
Biochemical oxygen demand (BOD) measures organic pollutants in water by measuring the amount of oxygen consumed by microorganisms that break down these compounds. BOD is hard to measure accurately. Total organic carbon (TOC) is easy to measure, so it is common to measure TOC and use regression to predict BOD. A typical regression equation for water entering a municipal treatment plant is BOD = -55.47 + 1.505 TOC Both BOD and TOC are measured in milligrams per liter of water. (a) What does the slope of this line say about the relationship between BOD and TOC? TOC rises (falls) by 1.505 mg/l for every 55.47 mg/l increase (decrease) in BOD BOD rises (falls) by 55.47 mg/l for every 1 mg/l increase (decrease) in TOC TOC rises (falls) by 1.505 mg/l for every 1 mg/l increase (decrease) in BOD BOD rises (falls) by 1.505 mg/l for every 1 mg/l increase (decrease) in TOC
Listed below are systolic blood pressure measurements (in mm Hg) obtained from the same woman. Find the regression equation, letting the right am blood pressure be the predictor (x) variable. Find the best predicted systolic blood pressure in the left arm given that the systolic blood pressure in the right arm is 100 mm Hg Use a significance level of 0.05. Right Arm 103 102 95 78 Left Arm 175 169 147 146 144 m Click the icon to view the critical values of the Pearson correlation coetticient r The regression oquation is y =+O (Round to one decimal place as needed) Given that the systolic blood pressure in the right arm is 100 mm Hg, the best predicted systolic blood pressure in the left arm is mm Hg (Round to one decimal place as needed.)
The mall has a set of data with employee age (X) and the corresponding number of annual on-the-job-accidents(Y). Analysis on the set finds that the regression equation is Y=100-3X. What is the likely number of accidents for someone aged 30? 97 100 10 none of the above
The data show the bug chirps per minute at different temperatures. Find the regression equation, letting the first variable be the independent (x) variable. Find the best predicted temperature for a time when a bug is chirping at the rate of 3000 chirps per minute. Use a significance level of 0.05. What is wrong with this predicted value? Chirps in 1 min 784 838 822 949 1188 813 Temperature (°F) 65.7 67.8 66.2 79.9 92.3 73.5 What is the regression equation? What is the best-predicted temperature for a time when a bug is chirping at the rate of 3000 chirps per minute? The best predicted temperature when a bug is chirping at 3000chirps per minute are? What is wrong with this predicted value? Choose the correct answer below. A. It is unrealistically high. The value 3000 is far outside of the range of observed values. B. The first variable should have been the dependent variable. C. It is only an…
Listed below are foot lengths (mm) and heights (mm) of males. Find the regression equation, letting foot length be the predictor (x) variable. Find the best predicted height of a male with a foot length of 272.8 mm. How does the result compare to the actual height of 1776 mm? Foot Length 281.8 277.9 253.3 259.2 279.0 258.0 274.0 262.4 Height 1784.7 1771.2 1676.0 1645.9 1858.9 1710.1 1788.9 1737.0 The regression equation is y=enter your response here+enter your response herex. (Round the y-intercept to the nearest integer as needed. Round the slope to two decimal places as needed.) The best predicted height of a male with a foot length of 272.8 mm is enter your response heremm. (Round to the nearest integer as needed.)
Nine pairs of data yield a regression equation of y=0.93x + 19.4, with r= 0.967 and an average y value of 64.70. What is best predicted value for y when x =65? Is it 79.85, 25.74, 89.61, 57.82, 70.55, 96.70, 19.40, or 64.70?
Two variable are found to have a strong negative linear correlation. Pick the regression equation that best fits this scenario. y=0.82x−28 ˆy=0.32x−28 y= -0.82x+28 ˆy= -0.32x+28

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