Listed below is a company's sales in the period 2000 to 2011 along with the national income of the country, where the business is set up.   Year National Income (in millions of $) Company's sales (in thousands of $) 2000 305 470 2001 316 485 2002 358 499 2003 350 515 2004 375 532 2005 392 532 2006 400 556 2007 398 576 2008 430 583 2009 456 587 2010 578 601 2011 498 605   Consider the following as the estimated regression analysis using excel that could be used to estimate the company's yearly sales, given the yearly national income.      What is the estimated regression equation that can be used to predict the company's sales using the national income? a. y^=328.9817+0.534x b. y^=0.534+328.9817x c. y=328.9817+0.534x d. y=0.534+328.9817x

Functions and Change: A Modeling Approach to College Algebra (MindTap Course List)
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Chapter5: A Survey Of Other Common Functions
Section5.6: Higher-degree Polynomials And Rational Functions
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Listed below is a company's sales in the period 2000 to 2011 along with the national income of the country, where the business is set up.

 

Year

National Income

(in millions of $)

Company's sales

(in thousands of $)

2000

305

470

2001

316

485

2002

358

499

2003

350

515

2004

375

532

2005

392

532

2006

400

556

2007

398

576

2008

430

583

2009

456

587

2010

578

601

2011

498

605

 

Consider the following as the estimated regression analysis using excel that could be used to estimate the company's yearly sales, given the yearly national income. 

 

 

What is the estimated regression equation that can be used to predict the company's sales using the national income?

a. y^=328.9817+0.534x
b. y^=0.534+328.9817x
c. y=328.9817+0.534x
d. y=0.534+328.9817x

 

Regression Statistics
Multiple R
R Square
Adjusted R Square 0.778450674
0.893639481
0.798591522
Standard Error
21.80419761
Observations
12
ANOVA
df
SS
MS
F
Significance F
Regression
1
18850.68633
18850.686
39.650343 8.945E-05
Residual
10
4754.230333 475.42303
Total
11
23604.91667
Coefficients Standard Error
t Stat
Р-value
Lower 95% Upper 95% Lower 99.0% Upper 99.0%
Intercept
328.9816769
34.89143634 9.4287227
2.718E-06
251.23871 406.7246418 218.4012012
439.56215
National Income
0.534023863
0.084808072 6.2968518
8.945E-05
0.3450597 0.722988022 0.265243958
0.8028038
Transcribed Image Text:Regression Statistics Multiple R R Square Adjusted R Square 0.778450674 0.893639481 0.798591522 Standard Error 21.80419761 Observations 12 ANOVA df SS MS F Significance F Regression 1 18850.68633 18850.686 39.650343 8.945E-05 Residual 10 4754.230333 475.42303 Total 11 23604.91667 Coefficients Standard Error t Stat Р-value Lower 95% Upper 95% Lower 99.0% Upper 99.0% Intercept 328.9816769 34.89143634 9.4287227 2.718E-06 251.23871 406.7246418 218.4012012 439.56215 National Income 0.534023863 0.084808072 6.2968518 8.945E-05 0.3450597 0.722988022 0.265243958 0.8028038
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