1 -Assume that we have two variables A and B and n pair of values for each of them. According to the summary resultof linear regression model between A and B obtained from R below, which statement is true? 55Call:1m (formula = B A)Residuals:Min10Median30Мах-16.340 -10.793-9.653-8.50258.325Coefficients:Estimate Std. Error t value Pr (>lt|)0.373(Intercept)19.631520.64570.9519.96090.371726.800 2.58e-08 ***Signif. codes:O AAAT 0.001 NAA0.01 A 0.050.1 1Residual standard error: 26.46 on 7 degrees of freedomMultiple R-squared:F-statistic: 718.2 on 1 and 7 DF,0.9903,Adjusted R-squared:0.989p-value: 2.58le-08a) 99.03% of the total variation of the values of A in our sample is accounted for by a linear relationship withvalues of Bb)It is concluded that A and B variables don't have linear relation with each otherC)P-value for A shows us: variable A is not significant for the modeld)If we increase A by 1, B will increase by 9.9609 with respect to the fitted regression linee)Bis independent value whereas A is dependent valueLeave blank

from the above information we get R2=0.9903 p value=2.581e-08 which is very small

of linear regression model between A and B obtained from R below, which statement is true? Call: 1m (formula = B A) Residuals: Min 10 Median 3Q Max -16.340 -10.793 -9.653 -8.502 58.325 Coefficients: Estimate Std. Error t value Pr (>[t|) (Intercept) 19.6315 20.6457 0.951 0.373 9.9609 0.3717 26.800 2.58e-08 *** Signif. codes: O 1* 0.001 A 0.01 A 0.05 0.1 1 Residual standard error: 26.46 on 7 degrees of freedom 0.9903, Adjusted R-squared: 0.989 Multiple R-squared: F-statistic: 718.2 on 1 and 7 DF, p-value: 2.58le-08 a) 99.03% of the total variation of the values of A in our sample is accounted for by a linear relationship win values of B b) It is concluded that A and B variables don't have linear relation with each other C) P-value for A shows us: variable A is not significant for the model d) If we increase A by 1, B will increase by 9.9609 with respect to the fitted regression line e) Bis independent value whereas A is dependent value ve blank

of linear regression model between A and B obtained from R below, which statement is true? Call: 1m (formula = B A) Residuals: Min 10 Median 3Q Max -16.340 -10.793 -9.653 -8.502 58.325 Coefficients: Estimate Std. Error t value Pr (>[t|) (Intercept) 19.6315 20.6457 0.951 0.373 9.9609 0.3717 26.800 2.58e-08 *** Signif. codes: O 1* 0.001 A 0.01 A 0.05 0.1 1 Residual standard error: 26.46 on 7 degrees of freedom 0.9903, Adjusted R-squared: 0.989 Multiple R-squared: F-statistic: 718.2 on 1 and 7 DF, p-value: 2.58le-08 a) 99.03% of the total variation of the values of A in our sample is accounted for by a linear relationship win values of B b) It is concluded that A and B variables don't have linear relation with each other C) P-value for A shows us: variable A is not significant for the model d) If we increase A by 1, B will increase by 9.9609 with respect to the fitted regression line e) Bis independent value whereas A is dependent value ve blank

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

1 -
Assume that we have two variables A and B and n pair of values for each of them. According to the summary result
of linear regression model between A and B obtained from R below, which statement is true? 55
Call:
1m (formula = B A)
Residuals:
Min
10
Median
30
Мах
-16.340 -10.793
-9.653
-8.502
58.325
Coefficients:
Estimate Std. Error t value Pr (>lt|)
0.373
(Intercept)
19.6315
20.6457
0.951
9.9609
0.3717
26.800 2.58e-08 ***
Signif. codes:
O AAAT 0.001 NAA
0.01 A 0.05
0.1 1
Residual standard error: 26.46 on 7 degrees of freedom
Multiple R-squared:
F-statistic: 718.2 on 1 and 7 DF,
0.9903,
Adjusted R-squared:
0.989
p-value: 2.58le-08
a) 99.03% of the total variation of the values of A in our sample is accounted for by a linear relationship with
values of B
b)
It is concluded that A and B variables don't have linear relation with each other
C)
P-value for A shows us: variable A is not significant for the model
d)
If we increase A by 1, B will increase by 9.9609 with respect to the fitted regression line
e)
Bis independent value whereas A is dependent value
Leave blank

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