I had to send a Word document of my discussion this week. its attached at the bottom.

Steven,

Correct. Yes, based on the chapter we learned how regression analysis can be used to determine how a dependent variable (y) is related to an independent variable (x). In simple linear regression, the regression model is y = β0 + β1x + ϵ, which related to the simple linear regression equation and sample data along with the least squares method is used to develop the estimated simple linear regression equation y ̂ = b0 + b1x, where b0 and b1 are sample statistics used to estimate the population parameters β0 and β1.

In this discussion example, the intercept is -207.7 and the height is 5.25 so the formula

When we say that there is a relationship between two variables then it’s mean that when one is changing (increasing or decreasing), then another variable is also changing by the change of the first variable.

The relationship might be positive or negative. In positive relationships, if one variable is increasing, then other is also increasing and vice versa.

We have the equation

y=5.2536x-207.75

R^2=0.5129

Y= 5.2536x -207.75

Y= 5.2536(65)-207.75

Y = 133.734

Y= 5.2536(100) – 207.75

Y= 317.61

Since the information of the stature of the individuals is somewhere in the range of 60 and 80 so 100 inches is not in the regression. Yet, as we probably are aware of R^2=0.5129*100 = 51.29% which implies the percent of variety of y that clarifies for the lineal model. We can discover that it isn’t very exact, we generally will encounter an error.

Steward Huang FACULTY MANAGER

RE: Week 7 Discussion- Relationship of Height and Weight

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