Data was collected for a regression analysis where sleep quality (as a percentage) depends on the amountof caffeine consumed in a day (measured in mg). bo was found to be 92.7, b₁ was found to be -0.76, andR² was found to be 0.86. Interpret the slope of the line.O On average, each one mg increase in caffeine consumed increases a person's sleep quality by 92.7%.O On average, when x = = 0, a person has a sleep quality of -0.76%.O On average, each one mg increase in caffeine consumed decreases a person's sleep quality by 0.76%.On average, when x = 0, a person has a sleep quality of 92.7%.O We should not interpret the slope in this problem.O We should interpret the slope in this problem, but none of the above are correct.

For the given data Interpret the slope ?

Answered: Data was collected for a regression…

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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The data below is the amount of tar and nicotine in a sample of different brands of 100 mm, non-menthol, filtered cigarettes. a) Using the calculator, find a linear regression model that gives the amount of nicotine, N, as a function of the amount of tar, t. Write the function below. N(t)= b) use this model of nicotine to estimate the amount of nicotine in a cigarette if the amount of tar is listed as 21 mg. c) What is the correlation coefficient, r, for this model? r= Tar 5 16 17 13 13 14 9 15 2 15 13 14 15 16 7 17 Nicotine 0.4 1 1.2 0.8 0.8 1 0.8 1 0.2 1.1 0.8 1 0.9 1.1 0.6 1.3
You are studying how a penguin's flipper length (in mm) explains its body mass (in grams) using linear regression. You choose a non-directional alternative to be safe. You calculate an R2=0.7621. What is the interpretation of this value? O76.21% of the variability in body mass is explained by the flipper length. O 76.21% of the variability in flipper length is explained by the body mass. 0 Flipper length and body mass have a positive relationship becase R² is positive O76.21% of the body mass values will be inside the confidence interval
A doctor wanted to determine whether there is a relation between a male’s age and his HDL (so-called good cholesterol). He randomly selected 9 of his patients and determined their HDL cholesterol. The data is reported in the table below: a) Compute the regression equation for HDL as a function of age. b)Interpret the meaning of the regression parameters.
A regression was run to determine if there is a relationship between hours of TV watched per day (x) and number of situps a person can do (y).The results of the regression were: y=ax+b a=-0.634 b=30.709 r2=0.948676 r=-0.974 Use this to predict the number of situps a person who watches 10.5 hours of TV can do (to one decimal place)
A study of king penguins looked for a relationship between how deep the penguins dive to seek food and how long they stay underwater. For all but the shallowest dives, there is a linear relationship that is different for different penguins. The study report gives a scatterplot for one penguin titled, “The relation of dive duration (DD) to depth (D).” Duration DD is measured in minutes, and depth D is in meters. The report then says, “The regression equation for this bird is, DD=2.69+0.0138D.is, DD=2.69+0.0138D.” According to the regression line, how long does a typical dive to a depth of 200200 meters last? The dives varied from 40 meters40 meters to 300 meters300 meters in depth. Use the regression equation to determine DD for D=40D=40 and D=300D=300 and then plot the regression line from D=40D=40 to D=300.to D=300. Give your answers to two decimal places.
A regression was run to determine if there is a relationship between hours of TV watched per day (x) and number of situps a person can do (y). The results of the regression were: y=ax+b a=-1.244 b=35.185 r=0.974169 r=-0.987 Assume the correlation is significant, and use this to predict the mumber of situps a person who watches 5.5 hours of TV can do (to one decimal place)
The following multiple linear regression depicts the sales of Unilever Corporation: S = 10- 0.20P + 0.06TA + 0.08RA Where:S: Sales (Unit);P: Price ($/Unit);TA: Television advertising ($)RA: Radio advertising ($). 1. Interpret the slopes;2. Is television advertising more effective than radio advertising? Why or why not?3. What is the effect of a $1 increase in price, a $3 increase in TV advertising, and a $2decrease in radio advertising on sales?4. What is the effect of a $2 decrease in price, a $2 decrease in TV advertising, and a $4increase in radio advertising on sales?
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