### Teward Huang Faculty Manager Gu Wk 7 Relationship

Teward Huang Faculty Manager Gu Wk 7 Relationship

 Relationship of Height and Weight Each week, you will be asked to respond to the prompt or prompts in the discussion forum. Your initial post should be 75-150 words in length, and is due on Sunday. By Tuesday, you should respond to two additional posts from your peers. What does it mean when we say that there is a relationship between two variables? What kinds of relationships can there be between two variables. Give an example of two variables that are related. For example, my daughter has an hourly salary. Her paycheck amount is related to how many hours she worked. Give an example of two variables that are NOT related. Using the given Height and Weight data set, follow the steps in the weekly video or on pages 584-585 of the textbook for performing a regression analysis using Excel to analyze the Height and Weight Data set (assume height is the input variable x and weight is the output variable y). Once you have performed the analysis in Excel, state the correct simple linear regression equation and use the regression equation to predict the weight (in pounds) of a person who is 65 inches tall and the weight (in pounds) of a person who is 100 inches tall. Why might the regression equation you have found NOT be a good predication of the weight of someone who is 100 inches tall? How does this lesson apply to the workplace? View your discussion rubric. 6

Steven Harris

Week 7 discussion COLLAPSE

I had to send a Word document of my discussion this week. its attached at the bottom. Week 7 Discussion.docx (13.484 KB)

teward Huang FACULTY MANAGER Professors resonse don not respond

RE: Week 7 discussion

COLLAPSE

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

Weight= -207.7 + 5.25 Height

Aminata Sesay

Week 7 Discussion- Relationship of Height and Weight

COLLAPSE

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

COLLAPSE

Sounds good. The simple linear regression formula for the Height/weight data is y= 5.2536(x) – 207.75. To compute the estimated weight for a person who is 65 inches tall and a person who is 100 inches tall, you should substitute these values for x. The equations look like the following. Estimate weight for 65 inches = y(weight) = 5.2536 (65) – 207.75. The estimated weight is 133.7 lbs. Estimated weight for 100 inches = y(weight) = 5.2536 (100) – 207.75. The estimated weight is 317.7 lbs. 