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MATH 1P98 Lecture Notes - Lecture 8: Standardized Mortality Ratio, Chapman & Hall, Analysis Of Variance

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orangeferret123

9 Sep 2017

School

Brock University

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Mathematics and Statistics

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Professor

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$MATH 1P98 Full Course Notes$

MATH 1P98 Full Course Notes

Document Summary

Example: fifteen students in a biology course were randomly selected and the number of hours of sleep the night before the midterm and the midterm mark were recorded for each student. Regression describe the distribution of value of y as a function of values of x. Linear the relationship will be a line. Equation of a straight line y = mx+b. True linear relationship between x and y (from population) Y = bo + b1 x + error y-int slope. On average, the two-linear relationship is m(y|x) = bo + b1. Mean value of y, given a specific value of x. This is relationship that work in general, work for population (all students) The (cid:498)best fit line(cid:499) based on the sample. As always, the question is (cid:498)what does this sample line say about the true population line? (cid:499) Residuals: the vertical distance from a data point to the (cid:498)best fit line(cid:499)

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Related Questions

Researchers measured the blood pressures of 22 students in

two situations: when the students were relaxed and when the students were taking an

important examination. The file

lists the systolic and diastolic pressures for

each student in each situation. Dataset is available at

systolic_url = "https://mcs.utm.utoronto.ca/~nosedal/data/systolic.txt"

to answer each of the following questions. You have to show

Answers, even if correct, with no justifications will not receive any

, compute the change in systolic pressure by subtracting systolic pressure when

relaxed from systolic pressure during the exam; call this variable

, repeat part a) for diastolic pressure. Call the resulting variable

, make a scatterplot of

, fit a regression line to the data and add

this line to your plot. Use: 2 c) your last name as the main title for your plot. (In my

case it would be: 2 c) Nosedal).

Important note. Use

as your response variable and label horizontal and vertical axes.

d) What is the fitted regression model? Provide slope and y-intercept

, make a residual plot from the regression in part c). Does this plot call into

question the use of a linear model? Why?

2 e) your last name

as the main title for your plot. (In my case it would

f) Note the outlier in the residual plot (and on the scatterplot from part c)).

, delete the outlier from the data set. Then repeat parts c) and e). Note. Use:

as the main title for your plots. (In my case it would be:

g) What is the fitted regression model (after the outlier has been removed)? Provide slope

and y-intercept

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