Ability to apply various statistical tools and techniques in the process of business analytics

1. Analyzing Linear
Regression

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.

6. Study Material