Does the data look approximately linear Or does there appear to be some sort of non linear trend in our data The data look approximately linear What is the regression model created from our data set The mean of years x ni 1 xi n x 103 11 x 9 3636 The mean of the Stocks ni 1 yi n 480 11 43 6364 The sum of squares of x SSxx 1073 1 11 (103)2 SSxx 1073 10609 11 SSxx 1073 964 4545 SSxx 108 5455 The sum of squares of stock SSyy 22868 1 11 (480)2 SSyy 22868 230400 11 SSyy 22868 20945 4545 SSyy 1922 5455 The sum of cross products SSxy 4939 1 11 (103) (480) SSxy 4939 49440 11 SSxy 4939 4494 5455 SSxy 444 4545 The slope is 1 SSxy SSxx 1 444 4545 108 5455 1 4 0946 The y intercept is 0 1 x 0 43 6364 4 0946 x 9 3636 0 5 2956 What does the slope represent in our regression model The slope shows that the company's stock grows by $4094 64 for each year an employee works there If an employee has been with the company for 13 years, how much company stock would we expect them to have We'd anticipate him to have $58525 40 In the problem, it was mentioned there are other variables that could impact the amount of company stock awarded to an employee What are some other variables we may want to collect next time to add to our model We might need to gather information about education level, age, performance evaluations, and other factors THE REAL QUESTION IS THIS ONE, BY LOOKING AT THE CHART ABOVE PLEASE HELP ME ANSWER THIS QUESTION THANK U By looking at the graph, are the two variables have a strong correlation How do you know

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Question: Does the data look approximately linear? Or does there appear to be some sort of non-linear trend in our data? The data look approximately linear.

Does the data look approximately linear? Or does there appear to be some sort of non-linear trend in our data?

The data look approximately linear.

What is the regression model created from our data set?

The mean of years:

x = ni = 1 xi / n

x = 103/11

x = 9.3636

The mean of the Stocks:

= ni = 1 yi / n

= 480/11

= 43.6364

The sum of squares of x:

SSxx = 1073 - 1/11 (103)2

SSxx = 1073 - 10609/11

SSxx = 1073 - 964.4545

SSxx = 108.5455

The sum of squares of stock:

SSyy = 22868 - 1/11 (480)2

SSyy = 22868 - 230400/11

SSyy = 22868 - 20945.4545

SSyy = 1922.5455

The sum of cross products:

SSxy = 4939 - 1/11 (103) (480)

SSxy = 4939 - 49440/11

SSxy = 4939 - 4494.5455

SSxy = 444.4545

The slope is

1 = SSxy/SSxx

1 = 444.4545/108.5455

1 = 4.0946

The y-intercept is

0 = - 1 * x

0 = 43.6364 - 4.0946 x 9.3636

0 = 5.2956

What does the slope represent in our regression model?

The slope shows that the company's stock grows by $4094.64 for each year an employee works there.

If an employee has been with the company for 13 years, how much company stock would we expect them to have?

We'd anticipate him to have $58525.40

In the problem, it was mentioned there are other variables that could impact the amount of company stock awarded to an employee. What are some other variables we may want to collect next time to add to our model?

We might need to gather information about education level, age, performance evaluations, and other factors.

-------------------------

THE REAL QUESTION IS THIS ONE, BY LOOKING AT THE CHART ABOVE.. PLEASE HELP ME ANSWER THIS QUESTION .. THANK U.

By looking at the graph, are the two variables have a strong correlation? How do you know?

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