Lecture 6 ba 6 statistics for business analytics
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This document analyzes a linear regression model to determine the relationship between miles traveled and time required. It evaluates the fitness of the regression model using hypothesis testing with a 99% confidence level and 1% standard error. The results show a significance F value less than 0.01, providing overwhelming evidence of a linear relationship between miles traveled and time required.
Lecture 6 ba 6 statistics for business analytics
- 2. Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations ANOVA df SS MS F Significance F Regression Residual Total Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 99.0% Upper 99.0% Intercept Miles (x) (SUMMARY OUTPUT)
- 3. Evaluating the fitness of the regression model using Hypothesis Testing • Ho = There is no relationship exist between Miles travelled and time required • H1 = There is a relationship exist between Miles travelled and time required
- 4. Hypothesis Testing • Confidence level = 99% • Standard Error =1% • Significance value should be less than 0.01
- 5. Result • Significance F value is coming 0.00408017667978983 which less than 0.01 • Therefore, there is overwhelming evidence at 0.01 level that Ho ≠0. • So we find it reasonable to believe that there is a linear relationship between Miles travelled and Time required to cover miles.