Interpret data visualization consisting of such tools as time series plots, box plots, and scatter charts using the following data sets. Scatter charts (X versus Y) – Analyze two different and distinct relationships: C22 (X, monthly advertising dollars) versus C23 (Y, monthly revenue dollars),Please indicate Equation, Slope, Intercept, R^2 value, 95% confidence, and P-value using regression in Excel and scatter graph. Is it significant? DO I reject the hypothesis?C22C23120001010008000920001000011000013000120000700090000800082000100009300060007500090009100011000105000SUMMARY OUTPUT Regression Statistics Multiple R0.87544177 R Square0.766398293 Adjusted R Square0.737198079 Standard Error6837.15011 Observations10 ANOVA dfSSMSFSignificance F Regression11226927027.027030001226927027.0270300026.246330.00090371 Residual8373972972.9729730046746621.62162160 Total91600900000.00000000 CoefficientsStandard Errort StatP-valueLower 95%Upper 95%Lower 95.0%Upper 95.0%Intercept46486.486499884.5662834.7029363920.00153623692.635869280.3372123692.6357669280.33721

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nterpret data visualization consisting of such tools as time series plots, box plots, and scatter charts using the following data sets. Scatter charts (X versus Y) – Analyze two different and distinct relationships: C22 (X, monthly advertising dollars) versus C23 (Y, monthly revenue dollars), Please indicate Equation, Slope, Intercept, R^2 value, 95% confidence, and P-value using regression in Excel and scatter graph. Is it significant? DO I reject the

nterpret data visualization consisting of such tools as time series plots, box plots, and scatter charts using the following data sets. Scatter charts (X versus Y) – Analyze two different and distinct relationships: C22 (X, monthly advertising dollars) versus C23 (Y, monthly revenue dollars), Please indicate Equation, Slope, Intercept, R^2 value, 95% confidence, and P-value using regression in Excel and scatter graph. Is it significant? DO I reject the

Linear Algebra: A Modern Introduction

4th Edition

ISBN:9781285463247

Author:David Poole

Publisher:David Poole

Chapter4: Eigenvalues And Eigenvectors

Section4.6: Applications And The Perron-frobenius Theorem

Problem 25EQ

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C22	C23
12000	101000
8000	92000
10000	110000
13000	120000
7000	90000
8000	82000
10000	93000
6000	75000
9000	91000
11000	105000

SUMMARY OUTPUT

Regression Statistics
Multiple R	0.87544177
R Square	0.766398293
Adjusted R Square	0.737198079
Standard Error	6837.15011
Observations	10

ANOVA
	df	SS	MS	F	Significance F
Regression	1	1226927027.02703000	1226927027.02703000	26.24633	0.00090371
Residual	8	373972972.97297300	46746621.62162160
Total	9	1600900000.00000000

	Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%	Lower 95.0%	Upper 95.0%
Intercept	46486.48649	9884.566283	4.702936392	0.001536	23692.6358	69280.33721	23692.63576	69280.33721