Breakfast cereals again We saw in Chapter 7 that thecalorie count of a breakfast cereal is linearly associatedwith its sugar content. Can we predict the calories of aserving from its vitamin and mineral content? Here’s amultiple regression model of Calories per serving on itsSodium (mg), Potassium (mg), and Sugars (g):Dependent variable is CaloriesR-squared = 38.4, R-squared (adjusted) = 35.9,s = 15.60 with 77 - 4 = 73 degrees of freedomSource Sum ofSquares dfMeanSquare F-Ratio P-ValueRegression 11091.8 3 3697.28 15.2 60.0001Residual 17760.1 73 243.289Variable Coefficient SE(Coeff) t-Ratio P-ValueIntercept 83.0469 5.198 16.0 60.0001Sodium 0.05721 0.0215 2.67 0.0094Potass -0.01933 0.0251 -0.769 0.4441Sugars 2.38757 0.4066 5.87 60.0001Assuming that the conditions for multiple regressionare met,a) What is the regression equation?b) Do you think this model would do a reasonably goodjob at predicting calories? Explain.c) Would you consider removing any of these predictorvariables from the model? Why or why not?d) To check the conditions, what plots of the data mightyou want to examine?
Breakfast cereals again We saw in Chapter 7 that thecalorie count of a breakfast cereal is linearly associatedwith its sugar content. Can we predict the calories of aserving from its vitamin and mineral content? Here’s amultiple regression model of Calories per serving on itsSodium (mg), Potassium (mg), and Sugars (g):Dependent variable is CaloriesR-squared = 38.4, R-squared (adjusted) = 35.9,s = 15.60 with 77 - 4 = 73 degrees of freedomSource Sum ofSquares dfMeanSquare F-Ratio P-ValueRegression 11091.8 3 3697.28 15.2 60.0001Residual 17760.1 73 243.289Variable Coefficient SE(Coeff) t-Ratio P-ValueIntercept 83.0469 5.198 16.0 60.0001Sodium 0.05721 0.0215 2.67 0.0094Potass -0.01933 0.0251 -0.769 0.4441Sugars 2.38757 0.4066 5.87 60.0001Assuming that the conditions for multiple regressionare met,a) What is the regression equation?b) Do you think this model would do a reasonably goodjob at predicting calories? Explain.c) Would you consider removing any of these predictorvariables from the model? Why or why not?d) To check the conditions, what plots of the data mightyou want to examine?
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
Related questions
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
Breakfast cereals again We saw in Chapter 7 that the
calorie count of a breakfast cereal is linearly associated
with its sugar content. Can we predict the calories of a
serving from its vitamin and mineral content? Here’s a
multiple regression model of Calories per serving on its
Sodium (mg), Potassium (mg), and Sugars (g):
Dependent variable is Calories
R-squared = 38.4, R-squared (adjusted) = 35.9,
s = 15.60 with 77 - 4 = 73 degrees of freedom
Source
calorie count of a breakfast cereal is linearly associated
with its sugar content. Can we predict the calories of a
serving from its vitamin and mineral content? Here’s a
multiple regression model of Calories per serving on its
Sodium (mg), Potassium (mg), and Sugars (g):
Dependent variable is Calories
R-squared = 38.4, R-squared (adjusted) = 35.9,
s = 15.60 with 77 - 4 = 73 degrees of freedom
Source
Sum of
Squares df
Mean
Square F-Ratio P-Value
Regression 11091.8 3 3697.28 15.2 60.0001
Residual 17760.1 73 243.289
Variable Coefficient SE(Coeff) t-Ratio P-Value
Intercept 83.0469 5.198 16.0 60.0001
Sodium 0.05721 0.0215 2.67 0.0094
Potass -0.01933 0.0251 -0.769 0.4441
Sugars 2.38757 0.4066 5.87 60.0001
Assuming that the conditions for multiple regression
are met,
a) What is the regression equation?
b) Do you think this model would do a reasonably good
job at predicting calories? Explain.
c) Would you consider removing any of these predictor
variables from the model? Why or why not?
d) To check the conditions, what plots of the data might
you want to examine?
Squares df
Mean
Square F-Ratio P-Value
Regression 11091.8 3 3697.28 15.2 60.0001
Residual 17760.1 73 243.289
Variable Coefficient SE(Coeff) t-Ratio P-Value
Intercept 83.0469 5.198 16.0 60.0001
Sodium 0.05721 0.0215 2.67 0.0094
Potass -0.01933 0.0251 -0.769 0.4441
Sugars 2.38757 0.4066 5.87 60.0001
Assuming that the conditions for multiple regression
are met,
a) What is the regression equation?
b) Do you think this model would do a reasonably good
job at predicting calories? Explain.
c) Would you consider removing any of these predictor
variables from the model? Why or why not?
d) To check the conditions, what plots of the data might
you want to examine?
Expert Solution
This question has been solved!
Explore an expertly crafted, step-by-step solution for a thorough understanding of key concepts.
This is a popular solution!
Trending now
This is a popular solution!
Step by step
Solved in 3 steps with 3 images
Knowledge Booster
Learn more about
Need a deep-dive on the concept behind this application? Look no further. Learn more about this topic, statistics and related others by exploring similar questions and additional content below.Recommended textbooks for you
MATLAB: An Introduction with Applications
Statistics
ISBN:
9781119256830
Author:
Amos Gilat
Publisher:
John Wiley & Sons Inc
Probability and Statistics for Engineering and th…
Statistics
ISBN:
9781305251809
Author:
Jay L. Devore
Publisher:
Cengage Learning
Statistics for The Behavioral Sciences (MindTap C…
Statistics
ISBN:
9781305504912
Author:
Frederick J Gravetter, Larry B. Wallnau
Publisher:
Cengage Learning
MATLAB: An Introduction with Applications
Statistics
ISBN:
9781119256830
Author:
Amos Gilat
Publisher:
John Wiley & Sons Inc
Probability and Statistics for Engineering and th…
Statistics
ISBN:
9781305251809
Author:
Jay L. Devore
Publisher:
Cengage Learning
Statistics for The Behavioral Sciences (MindTap C…
Statistics
ISBN:
9781305504912
Author:
Frederick J Gravetter, Larry B. Wallnau
Publisher:
Cengage Learning
Elementary Statistics: Picturing the World (7th E…
Statistics
ISBN:
9780134683416
Author:
Ron Larson, Betsy Farber
Publisher:
PEARSON
The Basic Practice of Statistics
Statistics
ISBN:
9781319042578
Author:
David S. Moore, William I. Notz, Michael A. Fligner
Publisher:
W. H. Freeman
Introduction to the Practice of Statistics
Statistics
ISBN:
9781319013387
Author:
David S. Moore, George P. McCabe, Bruce A. Craig
Publisher:
W. H. Freeman