A normal quantile plot of the residuals from this regression model should look like1. Randomly scattered noise (watermelon seeds)2. A normal distribution3. A parabola4. A uniform distribution5. A line6. Symmetric Data from 2007 measured the Incarceration Rate (i.e. number of people in prisonper 100,000) for 200+ countries around the world. This measured both theincarceration rate Pre-trial (held pending trial) and the rate of people in prison aftertrial (the US has the highest post-trial rate of any country - 787 per 100,000). Thefitted line plot belowses regression to calculate the prison rate after trial based onthe pre-trial rate.Fitted Line PlotPrison_rate= 64 + 2.1 Pretrial_rate800• USA700600Russia5004003002001000Prison_rateSR-Sq096.635041.4%●50000..100Pretrial_rate150●200

Given: A fitted line pot is given.

Answered: A normal quantile plot of the residuals…

Glencoe Algebra 1, Student Edition, 9780079039897, 0079039898, 2018

18th Edition

ISBN:9780079039897

Author:Carter

Publisher:Carter

Chapter4: Equations Of Linear Functions

Section: Chapter Questions

Problem 4SGR

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Question

A normal quantile plot of the residuals from this regression model should look like

1. Randomly scattered noise (watermelon seeds)

2. A normal distribution

3. A parabola

4. A uniform distribution

5. A line

6. Symmetric

Data from 2007 measured the Incarceration Rate (i.e. number of people in prison
per 100,000) for 200+ countries around the world. This measured both the
incarceration rate Pre-trial (held pending trial) and the rate of people in prison after
trial (the US has the highest post-trial rate of any country - 787 per 100,000). The
fitted line plot belowses regression to calculate the prison rate after trial based on
the pre-trial rate.
Fitted Line Plot
Prison_rate= 64 + 2.1 Pretrial_rate
800
• USA
700
600
Russia
500
400
300
200
100
0
Prison_rate
S
R-Sq
0
96.6350
41.4%
●
50
000
..
100
Pretrial_rate
150
●
200

Features Features Normal distribution is characterized by two parameters, mean (µ) and standard deviation (σ). When graphed, the mean represents the center of the bell curve and the graph is perfectly symmetric about the center. The mean, median, and mode are all equal for a normal distribution. The standard deviation measures the data's spread from the center. The higher the standard deviation, the more the data is spread out and the flatter the bell curve looks. Variance is another commonly used measure of the spread of the distribution and is equal to the square of the standard deviation.

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