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Salary = Β 1 + Β 2 Profits

Population Regression Function: Salary i = Β 1 + Β 2 Profits i + u i. Salary = Β 1 + Β 2 Profits. Sample Regression Function. Β 1 = intercept. Β 2 = slope. Salary = 746.92 + 0.572* Profits. Β 2 = slope. Β 1 = intercept.

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Salary = Β 1 + Β 2 Profits

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  1. Population Regression Function: Salaryi = Β1 + Β2 Profitsi + ui Salary = Β1 + Β2 Profits Sample Regression Function Β1 = intercept Β2 = slope

  2. Salary = 746.92 + 0.572*Profits Β2 = slope Β1 = intercept

  3. t = t-statistics. Tests the null hypothesis that the parameter= 0, i.e. Β1 = 0 , B2 = 0. Std. Err. = Standard Error Which is an estimate of the standard deviation of Β1 and B2 P > |t| Is the 2-tailed p-value for testing the null hypothesis that theΒ1= 0, Β2 = 0 The chance that this interval can capture the true Β1(or Β2) is 95%

  4. Number of observations R-squared, Adj. R-squared are assessments of the goodness of fit. 0 ≤ R-squared ≤ 1 F-test, test for the null hypothesis that all the coefficients = 0

  5. Y-axis = Salary So, you can plug the “profits” in here and predict what the salary should be “on average”. Salary = 746.92 + 0.572*Profits Intercept = 746.92 Slope = 0.572 X-axis = Profits

  6. Y-axis = Salary Given Xi = 1900 (Yi , Xi) = (1834.38, 1900) Yi =ui Yi - (Yi , Xi) = (1142, 1900)

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