A prediction equation for sales and payroll was performed using simple linearregression. In the regression printout shown below, which of the followingstatements is not true?SUMMARY OUTPUTRegression StatisticsMultiple RR SquareAdjusted R Square0.8333330.6944440.618056Standard ErTor1.311011ObservationsANOVADfSignificance FMSF15.6256.87515.6251.718759.090909RegressionResidual0.0393524Total22.5P-Valuet Stat1.1477470.414578 3.015113 0.039352CoefficientsStandard ErrorLower 95%Lipper 95%InterceptРayroll (X)21.7425440.31505-2.838086.8380771.250.0989472.401053Payroll is not a good predictor of Sales based on a = 0.01.There is evidence of a positive linear relationship between Sales and Payroll based on a = 0.05.Payroll is a good predictor of Sales based on a = 0.05.The coefficient of determination is equal to 0 833333

Simple linear regression is a linear regression model with a single Explanatory variable. That is,…

prêdičtión équâtion för šālėš ānd payroll performed using simple regression. In the regression printout shown below, which of the following statements is not true? SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square 0.833333 0.694444 0.618056 Standard Error 1.311011 Observations ANOVA Regression Residual Total Df MS 15.625 1.71875 Significance F 0.039352 15.625 6.875 9.090909 22.5 Upper 95% 6.838077 2.401053 Cafficients * Stat 1.147747 0.414578 3.015113 0.039352 Lower 95% -2.83808 0.098947 Standard ErrOr P-Value Intercept Payroll (X) 1.742544 0.31505 1.25 Payroll is not a good predictor of Sales based on a = 0.01. There is evidence of a positive linear relationship between Sales and Payroll based on a Payroll is a good predictor of Sales based on a = 0.05. The coefficient of determination is egual to 0 833333

prêdičtión équâtion för šālėš ānd payroll performed using simple regression. In the regression printout shown below, which of the following statements is not true? SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square 0.833333 0.694444 0.618056 Standard Error 1.311011 Observations ANOVA Regression Residual Total Df MS 15.625 1.71875 Significance F 0.039352 15.625 6.875 9.090909 22.5 Upper 95% 6.838077 2.401053 Cafficients * Stat 1.147747 0.414578 3.015113 0.039352 Lower 95% -2.83808 0.098947 Standard ErrOr P-Value Intercept Payroll (X) 1.742544 0.31505 1.25 Payroll is not a good predictor of Sales based on a = 0.01. There is evidence of a positive linear relationship between Sales and Payroll based on a Payroll is a good predictor of Sales based on a = 0.05. The coefficient of determination is egual to 0 833333

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

A prediction equation for sales and payroll was performed using simple linear
regression. In the regression printout shown below, which of the following
statements is not true?
SUMMARY OUTPUT
Regression Statistics
Multiple R
R Square
Adjusted R Square
0.833333
0.694444
0.618056
Standard ErTor
1.311011
Observations
ANOVA
Df
Significance F
MS
F
15.625
6.875
15.625
1.71875
9.090909
Regression
Residual
0.039352
4
Total
22.5
P-Value
t Stat
1.147747
0.414578 3.015113 0.039352
Coefficients
Standard Error
Lower 95%
Lipper 95%
Intercept
Рayroll (X)
2
1.742544
0.31505
-2.83808
6.838077
1.25
0.098947
2.401053
Payroll is not a good predictor of Sales based on a = 0.01.
There is evidence of a positive linear relationship between Sales and Payroll based on a = 0.05.
Payroll is a good predictor of Sales based on a = 0.05.
The coefficient of determination is equal to 0 833333

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