In previous blog, it was shown how to make use of scatterplot to detect linear trend and correlation analysis to quantify Pearson correlation coefficient value.

The Pearson correlation coefficient value was determined as 0.999 ( positive correlated ) and it was very strong linear relationship between the Y and X. ( Note that Pearson correlation value lie in the range -1 ≤ r ≤ +1 )

It can then be proceeded Regression analysis for constructing an estimating equation that relates Y and X.

In Minitab, click **Stat>Regression>Fitted Line Plot. **Fill up relevant Y and X field below and by default, click **OK.**

The Fitted Line Plot was generated and results interpreted below.

The analysis result from Session window interpreted as follows:

**Regression Analysis: Y versus X **

** **

The regression equation is

Y = 4.739 + 1.251 X

S = 0.0776200 R-Sq = 99.7% R-Sq(adj) = 99.7%

Analysis of Variance

Source DF SS MS F P

Regression 1 27.3417 27.3417 4538.14 0.000

Error 13 0.0783 0.0060

Total 14 27.4200

The Null Hypothesis and Alternate Hypothesis are identified below.

Ho : Null Hypothesis => The prediction equation is __not__ statistically significant – no relationship exists between X and Y_{}

Ha : Alternate Hypothesis => The prediction equation is statistically significant – a relationship does exists between X and Y

Since Pvalue = 0.000, reject Ho and accept Ha.

Hence, the estimating equation is a significant model that can be used for prediction.