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How well does the regression line fit the observed data, what is the sample correlation coefficient between the two variables in this model and how can it be interpreted? Write the sample regression equation clearly defining the variables used.

How well does the regression line fit the observed data, what is the sample correlation coefficient between the two variables in this model and how can it be interpreted? Write the sample regression equation clearly defining the variables used.

College Algebra
College Algebra
1st Edition
ISBN:9781938168383
Author:Jay Abramson
Publisher:Jay Abramson
Chapter6: Exponential And Logarithmic Functions
Section6.8: Fitting Exponential Models To Data
Problem 1TI: Table 2 shows a recent graduate’s credit card balance each month after graduation. a. Use...
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How well does the regression line fit the observed data, what is the sample correlation coefficient between the two variables in this model and how can it be interpreted? Write the sample regression equation clearly defining the variables used.

 

Shengyu is an analyst for a company that provides clients with advice about economic conditions. He notes that the United States (US) is experiencing high inflation at the moment. He wonders if high inflation in the US somehow feeds through to cause high inflation in China. To start his analysis, he downloads data on inflation from the OECD website and runs a simple regression of Chinese inflation on US inflation for data from Quarter 1 2005 to Quarter 2 2021 (each quarter's data on inflation is in terms of annual percentage change). The Excel output for this is as follows: spér DO 19646 SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square 0.273039 Standard Error Observations 0.533126 0.284223 1.614377 66 ANOVA df SS MS Significance F Regression Residual 66.23271501 66.23272 25.41338 4.0545E-06 64 166.7977302 2.606215 Total 65 233.0304453 Coefficients Standard Error t Stat P-value Intercept X Variable 1 Lower 95% Upper 95% Lower 95.0% Upper 95.0% 0.397429191 2.071456 0.042352 0.02930082 1.617213 0.02930082 1.61721298 0.193938053 5.041168 4.05E-06 0.59023857 1.365110 0.59023857 1.36511017 0.823257 0.977674
Transcribed Image Text:Shengyu is an analyst for a company that provides clients with advice about economic conditions. He notes that the United States (US) is experiencing high inflation at the moment. He wonders if high inflation in the US somehow feeds through to cause high inflation in China. To start his analysis, he downloads data on inflation from the OECD website and runs a simple regression of Chinese inflation on US inflation for data from Quarter 1 2005 to Quarter 2 2021 (each quarter's data on inflation is in terms of annual percentage change). The Excel output for this is as follows: spér DO 19646 SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square 0.273039 Standard Error Observations 0.533126 0.284223 1.614377 66 ANOVA df SS MS Significance F Regression Residual 66.23271501 66.23272 25.41338 4.0545E-06 64 166.7977302 2.606215 Total 65 233.0304453 Coefficients Standard Error t Stat P-value Intercept X Variable 1 Lower 95% Upper 95% Lower 95.0% Upper 95.0% 0.397429191 2.071456 0.042352 0.02930082 1.617213 0.02930082 1.61721298 0.193938053 5.041168 4.05E-06 0.59023857 1.365110 0.59023857 1.36511017 0.823257 0.977674
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