Exercise 9.13 presents a regression model for predicting the average birth weight of babies based on length of gestation, parity, height, weight, and smoking status of the mother. Determine if the model assumptions are met using the plots below. If not, describe how to proceed with the analysis. 4040-40--40-100150200250Weight of motherSmokeShow transcribed image textExpert Answer oAnonymous_1989 answered this18,173 answersWas this answer heloful? 69Please don't hesltate to glve a "thumbs up" for the answer In case the answer has helped you1. ist graph ideally should have residuals lined to thetheoritical quantiles.Hence, residuals aligned to a line is a correct graph.Hence, this graph looks good2. Residual to fitted values graph is randomly spread,and hence looks good3. constant variance of residuals and Order of collectionLooks good4. Residual graph on length of gestation - looks good5. Parity residual graph - looks good, constant variance6. Height of mother reisdual plot - no problem, almost randomvariance7. Weight of mother residuals - constantly distributed.8. Constant variance of Smoke residualsComment >ResidualsResiduals

The plot of residuals versus predictors (weight of mother, smoke) are given, The residuals and the…

Exercise 9.13 presents a regression model for predicting the average birth weight of babies based on length of gestation, parity, height, weight, and smoking status of the mother. Determine if the model assumptions are met using the plots below. If not, describe how to proceed with the analysis.

Exercise 9.13 presents a regression model for predicting the average birth weight of babies based on length of gestation, parity, height, weight, and smoking status of the mother. Determine if the model assumptions are met using the plots below. If not, describe how to proceed with the analysis.

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

18th Edition

ISBN:9780079039897

Author:Carter

Publisher:Carter

Chapter10: Statistics

Section: Chapter Questions

Problem 27SGR

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Concept explainers

Correlation

Correlation defines a relationship between two independent variables. It tells the degree to which variables move in relation to each other. When two sets of data are related to each other, there is a correlation between them.

Linear Correlation

A correlation is used to determine the relationships between numerical and categorical variables. In other words, it is an indicator of how things are connected to one another. The correlation analysis is the study of how variables are related.

Regression Analysis

Regression analysis is a statistical method in which it estimates the relationship between a dependent variable and one or more independent variable. In simple terms dependent variable is called as outcome variable and independent variable is called as predictors. Regression analysis is one of the methods to find the trends in data. The independent variable used in Regression analysis is named Predictor variable. It offers data of an associated dependent variable regarding a particular outcome.

Question

40
40
-40-
-40-
100
150
200
250
Weight of mother
Smoke
Show transcribed image text
Expert Answer o
Anonymous_1989 answered this
18,173 answers
Was this answer heloful? 69
Please don't hesltate to glve a "thumbs up" for the answer In case the answer has helped you
1. ist graph ideally should have residuals lined to the
theoritical quantiles.
Hence, residuals aligned to a line is a correct graph.
Hence, this graph looks good
2. Residual to fitted values graph is randomly spread,
and hence looks good
3. constant variance of residuals and Order of collection
Looks good
4. Residual graph on length of gestation - looks good
5. Parity residual graph - looks good, constant variance
6. Height of mother reisdual plot - no problem, almost random
variance
7. Weight of mother residuals - constantly distributed.
8. Constant variance of Smoke residuals
Comment >
Residuals
Residuals

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