a.Interpret the slope in this modelb.Determine the multiple regression model for this analysis.c.Based on your answer in (b), explain on the strength and the variation of the model Based on the given multiple regression output tables below, answer the followingquestions:Model SummaryAdjusted RSquareStd. Error of theEstimateDurbin-ModelRR SquareWatson1.842..710.630109.4301.158ANOVASum ofSquaresModeldfMean SquareFSig.Regression321946.823107315.68.960.0027Residual131723.201111974.8Total453670.0014CoefficientsStandardizedCoefficientsUnstandardized CoefficientsModelBStd. ErrorBetaSig.(Constant)657.053167.463.92.0024X Variable 1X Variable 2X Variable 35.71031.792-.1013.19.0087-0.41690.322-.077-1.29.2222-3.47151.443-7.996-2.41.0349

A regression model can be defined as a relationship between the explanatory variable and the…

Answered: a.Interpret the slope in this model…

a.Interpret the slope in this model b.Determine the multiple regression model for this analysis. c.Based on your answer in (b), explain on the strength and the variation of the model

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

18th Edition

ISBN:9780079039897

Author:Carter

Publisher:Carter

Chapter4: Equations Of Linear Functions

Section4.6: Regression And Median-fit Lines

Problem 4GP

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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

a.Interpret the slope in this model

b.Determine the multiple regression model for this analysis.

c.Based on your answer in (b), explain on the strength and the variation of the model

Based on the given multiple regression output tables below, answer the following
questions:
Model Summary
Adjusted R
Square
Std. Error of the
Estimate
Durbin-
Model
R
R Square
Watson
1
.842.
.710
.630
109.430
1.158
ANOVA
Sum of
Squares
Model
df
Mean Square
F
Sig.
Regression
321946.82
3
107315.6
8.96
0.0027
Residual
131723.20
11
11974.8
Total
453670.00
14
Coefficients
Standardized
Coefficients
Unstandardized Coefficients
Model
B
Std. Error
Beta
Sig.
(Constant)
657.053
167.46
3.92
.0024
X Variable 1
X Variable 2
X Variable 3
5.7103
1.792
-.101
3.19
.0087
-0.4169
0.322
-.077
-1.29
.2222
-3.4715
1.443
-7.996
-2.41
.0349

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