A prediction equation for starting salaries (in $1,000s) and SAT scores was performedusing simple linear regression. In the regression printout shown below, what can besaid about the level of significance for the overall model?SUMMARY OUTPUTRegression StatisticsMultiple RR SquareAdjusted R Square0.9350181230.874258890.860287655Standard Error3.307295949Observations11ANOVAdfSignificance FMSRegression684.465210.938211684.465232462.575642.42144E-05Residual98.44385847Total10782.9090909Coefficients-29.14060468t Stat-3.364937.910476Stændard ErrorP-ValueLower 95%InterceptSAT0.0083242.42E-05-48.731083870.0467288668.6600809970.0654438410.008273059SAT is not a good predictor for starting salary.The significance level for SAT indicates the slope is equal to zero.The significance level for SAT indicates the slope is not equal to zero.The significance level for the intercept indicates the model is not valid.

From the given information, the p-value for SAT is 0.0000242.

A prediction equation for starting salaries (in $1,000s) and SAT scores was performed using simple linear regression. In the regression printout shown below, what can be said about the level of significance for the overall model?

A prediction equation for starting salaries (in $1,000s) and SAT scores was performed using simple linear regression. In the regression printout shown below, what can be said about the level of significance for the overall model?

MATLAB: An Introduction with Applications

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ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

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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 prediction equation for starting salaries (in $1,000s) and SAT scores was performed
using simple linear regression. In the regression printout shown below, what can be
said about the level of significance for the overall model?
SUMMARY OUTPUT
Regression Statistics
Multiple R
R Square
Adjusted R Square
0.935018123
0.87425889
0.860287655
Standard Error
3.307295949
Observations
11
ANOVA
df
Significance F
MS
Regression
684.4652
10.93821
1
684.4652324
62.57564
2.42144E-05
Residual
98.44385847
Total
10
782.9090909
Coefficients
-29.14060468
t Stat
-3.36493
7.910476
Stændard Error
P-Value
Lower 95%
Intercept
SAT
0.008324
2.42E-05
-48.73108387
0.046728866
8.660080997
0.065443841
0.008273059
SAT is not a good predictor for starting salary.
The significance level for SAT indicates the slope is equal to zero.
The significance level for SAT indicates the slope is not equal to zero.
The significance level for the intercept indicates the model is not valid.

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