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STAT151 Chapter Notes - Chapter 7-8: Standard Deviation, Dependent And Independent Variables, Summary Statistics

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1 Dec 2018

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STAT151 Full Course Notes

STAT151 Full Course Notes

Document Summary

To fit a straight line through the data so that we can predict values of the response at specified values of x. Y intercept y = bo + b1x bo = the b1 = the slope x = t he value of . *the slope is the amount by which y changes when x increases by 1. *the b"s are called the coefficients of the linear model. The line of best fit/least squares regression line . Gives an estimate (predicted response) for y given the value of x. Error of prediction/deviation/residuals line residual = observed - predicted = y1 - 1. The best fitted line is the one that minimizes the sum of the squared residuals between. Conclusion: data points and the line itself. min (residuals)^2 - min (y1- 1)^2. Minimize the sum by choosing the appropriate parameters bo and b1.

Related textbook solutions

Introductory Statistics

OER Edition, 2013

ISBN: 9781938168208

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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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