Answered: A Simple Linear Regression (SLR) was performed where the monthly Revenue ("Rev", the y-variable) was regressed on the monthly Advertising Expenditures…

MATLAB: An Introduction with Applications

6th Edition

ISBN: 9781119256830

Author: Amos Gilat

Publisher: John Wiley & Sons Inc

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Linear Correlation

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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 p…

Question

A Simple Linear Regression (SLR) was performed where the monthly Revenue ("Rev", the y-variable) was regressed on the monthly Advertising Expenditures ("Expend", the x-variable). Excel was used to construct the 98% Confidence Interval (CI) estimate of beta subscript 1. The Excel-generated Regression output is provided below:

ANOVA

	df	SS	MS	F	Significance F
Regression	1	492.528125	492.528125	10.65525634	0.046980871
Residual	3	138.671875	46.22395833
Total	4	631.2

	Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%	Lower 98.0%	Upper 98.0%
Intercept	23.1328125	5.324310936	4.344752359	0.022510469	6.188478833	40.07714617	-1.043301388	47.30892639
Expend	3.1015625	0.950164031	3.264239014	0.046980871	0.077716489	6.125408511	-1.212850033	7.415975033

a. Enter the value of the Left-Hand Endpoint (LHEP) of the 98% Confidence Interval (CI) estimate of beta subscript 1. Round off your answer to the fourth decimal place.

The LHEP of the 98% CI for beta subscript 1, rounded off as instructed, is: Blank 1. Fill in the blank, read surrounding text.

b. Enter the value of the Right-Hand Endpoint (RHEP) of the 98% Confidence Interval (CI) estimate of beta subscript 1. Round off your answer to the fourth decimal place.

The RHEP of the 98% CI for beta subscript 1, rounded off as instructed, is: Blank 2. Fill in the blank, read surrounding text.

c. Select the number of the following statement that gives the correct interpretation of the CI in this problem:

1: the CI is a NEGATIVE CI, and therefore REV and EXPEND are NOT significantly Linearly related.

2: The CI is a POSITIVE CI, and therefore REV and EXPEND are NOT significantly Linearly related.

3: The CI is a MIXED CI, and therefore REV and EXPEND are NOT significantly Linearly related.

4: The CI is a NEGATIVE CI, and therefore REV and EXPEND ARE significantly Negatively Linearly related.

5: The CI is a POSITIVE CI, and therefore REV and EXPEND ARE significantly Positively Linearly related.

Answer to part (c):

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