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STAT 11300 Study Guide - Final Guide: Dependent And Independent Variables, Scatter Plot, Confounding

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30 Oct 2014

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Purdue University

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Document Summary

Regression line- is a straight line that describes how a response variable y changes as an explanatory variable x changes. **to use equation for prediction, substitute your x-value into equation. Correlation- describes the direction and strength of a straight line relationship between two quantitative variables. Square of the correlation, r2- is the proportion of the variation in the values of y that is explained by the least-squares regression of y on x. Explanatory may have some cause but is not the sole cause. Both variables may result from a common cause. A sample of 100 pizzas were taken to examine the effect of warming time in the oven on the amount of bacteria in the pizza. The graph 1 on the handout has a scatterplot for this data. If a pizza were in the warming drawer for 55 minutes, what amount of bacteria would you predict? (a) 96 (b) 100 (c) 104 (d) 112 (e) 116.

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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