In this following test, a group of scientists conducted a correlation analysisbetween the total length of possums with the length of possums' heads.Which of the following conclusions is correct?> cor.test(possum$totalL, possum$headL)Pearson's product-moment correlationdata: possum$totalL and possum$headLt = 9.6569, df = 102, p-value = 4.681e-16%3D%3Dalternative hypothesis: true correlation is not equal to 095 percent confidence interval:0.5750415 0.7798823sample estimates:cor0.6910937There is no difference between total length and head length.There is no association between total length and head length.There is a significant association between total length and head length.There is a significant functional relationship between total length and head length.

To test: H0 : there is no significant association between toatl lenghth and head length vs H1…

In this following test, a group of scientists conducted a correlation analysis between the total length of possums with the length of possums' heads. Which of the following conclusions is correct? > cor.test(possum$totalL, possum$headL) Pearson's product-moment correlation data: possum$totalL and possum$headL t = 9.6569, df = 102, p-value = 4.681e-16 alternative hypothesis: true correlation is not equal to 0 95 nercent confidence interval:

In this following test, a group of scientists conducted a correlation analysis between the total length of possums with the length of possums' heads. Which of the following conclusions is correct? > cor.test(possum$totalL, possum$headL) Pearson's product-moment correlation data: possum$totalL and possum$headL t = 9.6569, df = 102, p-value = 4.681e-16 alternative hypothesis: true correlation is not equal to 0 95 nercent confidence interval:

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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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

In this following test, a group of scientists conducted a correlation analysis
between the total length of possums with the length of possums' heads.
Which of the following conclusions is correct?
> cor.test(possum$totalL, possum$headL)
Pearson's product-moment correlation
data: possum$totalL and possum$headL
t = 9.6569, df = 102, p-value = 4.681e-16
%3D
%3D
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
0.5750415 0.7798823
sample estimates:
cor
0.6910937
There is no difference between total length and head length.
There is no association between total length and head length.
There is a significant association between total length and head length.
There is a significant functional relationship between total length and head length.

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