3. The following data were collected during an experiment in which laboratoryanimals were inoculated with a pathogen. The variables are time in hours afterinoculation and temperature in degrees Celsius.Time Temperature Time Temperature2438.84441.12839.54841.43240.35241.63640.75641.84041.06041.9(a) Find the simple linear regression equation. (Show you calculation.)(b) Give practical interpretations of Bo and B1.

3. It is been asked to find the simple linear regression using the given data.

Answered: 3. The following data were collected…

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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I’m interested in seeing whether or not there is a relationship between the number of hours someone sleeps and traffic accidents. I gather data from the Department of Transportation and get the following data: X (hours of sleep) Y (Traffic accidents) 5 2 5 1 3 4 4 4 2 6 6 3 8 1 9 1 5 0 6 4 Using the above data, create a linear regression equation to predict traffic accidents from hours of sleep. What is the Pearson correlation coefficient? Is it strong or weak? Positive or negative? What is the linear regression equation? (in the form of Y’ = bX + a) How many traffic accidents might we predict with someone that gets 0 hours of sleep? How many traffic accidents might we predict with someone that gets 11 hours of sleep?
The following table shows the hot dogs bought from a street vendor over the course of eight days ("Demand"). Also shown is the temperature for each day in degrees Celsius. Complete parts a and b below. Temperature (°c) 22 25 19 8 11 17 20 Demand 46 28 35 42 18 25 43 35 a. Calculate the slope and y-intercept for the linear regression equation for these data. y = (Round to two decimal places as needed.)
The table below shows the number of state-registered automatic weapons and the murder rate for several Northwestern states. xx 11.7 8.3 7 3.4 2.4 2.6 2.6 0.9 yy 14 11.1 10.1 6.7 6.1 6.4 6.5 4.9 xx = thousands of automatic weaponsyy = murders per 100,000 residents Determine the regression equation in y = ax + b form and write it below. (Round to 2 decimal places):____________________ A) How many murders per 100,000 residents can be expected in a state with 10.8 thousand automatic weapons? Round to 3 decimal places. Answer = B) How many murders per 100,000 residents can be expected in a state with 4.4 thousand automatic weapons? Round to 3 decimal places. Answer =
Solve Qg, h , i. Slove Qg and h without using Excel. Qi using Rstudio.
q12
The following table shows both current and projected data on the number, in millions, of Facebook users in a certain country. t = years since 2012 F = number of Facebookusers (millions) 0 9.7 1 10.5 2 10.8 3 12.2 4 13.1 5 13.2 6 13.8 (a) Plot the data. (b) Find the equation of the regression line. (Round regression line parameters to two decimal places.) F(t) = Add the graph of the regression line to the plot from part (a). (c) Explain in practical terms the meaning of the slope of the regression line. (Round your answers to two decimal places). The slope of the regression line is million users per year. The slope means that, from 2012 on, the number of Facebook users in the country is expected to increase by million each year.
The table below shows the number of state-registered automatic weapons and the murder rate for several Northwestern states. xx 11.7 8.1 6.7 3.3 2.7 2.3 2.2 0.3 yy 13.8 10.7 9.9 6.6 6.1 5.9 6.1 4.5 xx = thousands of automatic weaponsyy = murders per 100,000 residents Determine the regression equation in y = ax + b form and write it below. (Round to 2 decimal places) A) How many murders per 100,000 residents can be expected in a state with 6.4 thousand automatic weapons? Answer = Round to 3 decimal places. B) How many murders per 100,000 residents can be expected in a state with 6.7 thousand automatic weapons? Answer = Round to 3 decimal places.
46. Table 1.3 lists the ages and weights of nine girls. TABLE 1.3 Girls' Ages and Weights Age (months) Weight (pounds) 22 19 23 21 24 25 27 28 29 31 31 28 34 32 38 34 43 39 (a) Find the linear regression equation for the data. (b) Find the slope of the regression line. What does the slope represent? (c) Superimpose the graph of the linear regression equation on a scatter plot of the data. (d) Use the regression equation to predict the approximate weight of a 30-month-old girl..

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