For the linear regression model Y = bo + b1(X):The p-value for the intercept is large: about 0.98The p-value for the slope is very small: less than 2 times 10^(-16)What can we conclude?Since the p-value for the intercept is large, we can conclude that there is not astrong correlation between X and Y.Since the p-value for the intercept is large, we can conclude that there is a verystrong correlation between X and Y.Since the p-value for the slope is very small, we can conclude that there is a veryweak correlation between X and Y.Since the p-value for the slope is very small, we can conclude that there is a verystrong correlation between X and Y.We are not able to assess the strength of the correlation between X and Y withthe output provided.

Answer: Option1 Since the p-value for the intercept is large,we can conclude that there is not a…

Answered: For the linear regression model Y = bo…

For the linear regression model Y = bo + b1(X): %3D The p-value for the intercept is large: about 0.98 The p-value for the slope is very small: less than 2 times 10^(-16) What can we conclude?

College Algebra

1st Edition

ISBN:9781938168383

Author:Jay Abramson

Publisher:Jay Abramson

Chapter6: Exponential And Logarithmic Functions

Section6.8: Fitting Exponential Models To Data

Problem 5SE: What does the y -intercept on the graph of a logistic equation correspond to for a population...

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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

For the linear regression model Y = bo + b1(X):
The p-value for the intercept is large: about 0.98
The p-value for the slope is very small: less than 2 times 10^(-16)
What can we conclude?
Since the p-value for the intercept is large, we can conclude that there is not a
strong correlation between X and Y.
Since the p-value for the intercept is large, we can conclude that there is a very
strong correlation between X and Y.
Since the p-value for the slope is very small, we can conclude that there is a very
weak correlation between X and Y.
Since the p-value for the slope is very small, we can conclude that there is a very
strong correlation between X and Y.
We are not able to assess the strength of the correlation between X and Y with
the output provided.

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