Convert the following fraction to a decimal and then classify if the decimal is terminating , non terminating , repeating or non repeating . 5/6

1. We plotted the arm span in the x-axis because the arm span is the independent variable that we are using the calculate how tall is a person based on their arm span. I plotted the person's height in the y-axis because the persons's height is a dependent variable; after all, we are estimating how tall a person is based on their arm span.

We can conclude that:

The x-axis represent the arm span because that is the independent variable.

The y-axis represent how tall a person is based on his arm span because the persons height depends on the arm span.

2. From our picture we can infer that our point are: (24,51),(24,56) (25,53), (25,54), (26,55), (26,56), (27,54), (29,55),(29,58), (30,59), and (33,61).

From our picture we can infer that the point that appear to be co-linear are: (25,53), (26,54), (29,58), and (30,59). We are going to use the end points (25,53) and (30,59) to create our lineal model. To do it, we are going to find its slope using the slope formula: 

where

is the slope.

are the coordinates of the first point. 

are the coordinates of the second point.

Our first point is (25,53), so  and ; our second point is (30,59), so  and . Lets replace those values in our slope formula:

Now that we have our slope, we are going to use the point-slope formula to complete our linear model. Point-slope formula: 

where

is the slope.

are the coordinates of the first point.

We already know what those values are, so lets replace them in our formula:

We can conclude that the equation of the line that best fit our data is .

3. The slope represent that each time that  increases by 5 units,  increases 6 units. In the context of our model it means that every time the arm span increase 5 inches, the person's height increases 6 inches.

Remember that in a linear function of the form ,  is the y-intercept. Knowing this, we can infer that the y-intercept of our model is 23. The y-intercept represent that when the arm span of a person is 0 inches, their height is 23, which doesn't make much sense. This is a perfect example why you shouldn't trust models every time. 

4. The residual () is the difference between the observable y-value -in our case the person's height, and the y-value in our linear equation. In other words: 

observable y-value - predicted y-value.

Lets fin our residuals using that formula:

(24,-0.8)

(24, 4.2)

(25, 1)

(26,-0.2)

(26,1.8)

(27,-1.4)

(29,-2.8)

(29,0.2)

Now, we are going to plot a residual plot using our residual points. If the residual plot (picture 1) does not follow a pattern, our linear model is a good fit for the data, but if the residual plot, follows a pattern our linear model is not a good fit for the data.

Since our residual plot does not follow any pattern, we can conclude that our linear model is a good fit for the data.

5. To find how tall is a person whose arm span is 66 inches, we just need to replace  with 66 in our linear equation and simplify: inches

We can conclude that a person whose arm span is 66 inches is 102.2 inches tall.

6. To find the arm span of a 74-inch-tall person, we are going to replace  with 64 in our linear equation an solve for : inches

We can conclude arm span of a 74-inch-tall person is 42.5 inches.

Step-by-step explanation: