Attempts: Keep the Highest: /12 4. using Excel - Obtaining estimates for a simpl
ID: 3265172 • Letter: A
Question
Attempts: Keep the Highest: /12 4. using Excel - Obtaining estimates for a simple linear regression Aa Aa To answer the questions that follow, download an Excels spreadsheet containing the demographic data for a sample of 30 adults by clicking on the following words in bold: Download Excel File. use Excel to obtain an estimated regression equation predicting the value of income from age (years). (Note: use the income [in $1,000s] and age tyears] variables, not the income or age category variables.) use Exel to obtain an estimated regresson equation predicting the value of income from age (years). (Note: use x, where y is the predicted value of and x is The regression equation is y- the value of 369 -14.038 2.848 ween age and income. You conduct a hypothesis test with the null hypothesis Ha: 10. Based on these results, with a significance level You believe there is a linear rel hypothesis Ho: 1-0 versus th of = .05, you related 0.882 een age and income. You 0.882 versus t 0.882 ween reject the null hypothesis. You conclude that age and income are linearly Orade it Now Save& Continue Continue without savin ht Notices Terms of Use Privacy Notice Security Notice AccessibilityExplanation / Answer
We can use the estimated the regression coefficient of the simple linear model by excel command.
The step of compute estimate is
Enter the data in excel- Data-Data Analysis-Regression
The Model is
Income =a+b*age
Income is a dependent variable
Age is a independent variable
The regression equation is
Y= -14.037+2.847X
where y is the predict value of income and X is the value of age.
From the table, the p-value(0.0035) is less than 0.05. You reject the null hypothesis. You cannot conclude that age and income are linearly related.
SUMMARY OUTPUT Regression Statistics Multiple R 0.520999 R Square 0.27144 Adjusted R Square 0.24542 Standard Error 49.44741 Observations 30 ANOVA df SS MS F Significance F Regression 1 25506.66 25506.66 10.43197 0.003157 Residual 28 68461.3 2445.046 Total 29 93967.95 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept -14.0376 38.02215 -0.36919 0.71476 -91.9224 63.84728 X Variable 1 2.847797 0.88171 3.229857 0.003157 1.041696 4.653899Related Questions
Navigate
Integrity-first tutoring: explanations and feedback only — we do not complete graded work. Learn more.