Line of Best Fit
How do they relate?
Random
Simplify
Old stuff
100

This is the BEST line of best fit.

What is the LSRL?

100

Another name for relationship on a scatterplot.

What is association?

100

The estimated sum of the residuals is this.

What is 0?

100

(3x3)(9x8)

27x11

100

Solve:  f(x) = √(x + 6), for x = 19.

5

200

Actual - Predicted is how you find this.

The residual

200

A point that doesn't seem to match the rest of the data is known as this.

What is an outlier?

200

A study shows that if always wash your hands, you will never get sick.  Name a lurking variable.

One example:  The sickness is not passed along in this way (i.e. cancer).

200

-15x-6y

5x2y-2

-3y3

   x8

200

Given:  f(x) = √(x + 6)

If f(x) = 7, what is the value of x?

43

300

In order to find the upper and lower bounds you must first find the residual for this point.

What is the residual of the point furthest from the LSRL?

300

A correlation coefficient of -0.93 tells us what about the direction AND strength of the LSRL?

Direction is negative

Strength is strong.

300

For a set of data the residual is -14.7 minutes and the predicted was 88.5 minutes for a specific input. The actual value is this.

What is 73.8?

300

(m-7)8

1/(m56)

300

Solve:  2(c - 3) + c = 4c - 6

0

400

If the LSRL is y = 0.4x + 12 and the largest residual is 13, then this is the equation for the upper bound.

What is  y = 0.4x + 25?

400

In order for a Linear Model to be a good fit for the data, the residual plot will look like this.

What is scattered with no pattern?

400

The LSRL is y = 0.84x + 2.4, the predicted value for  18 is this.

What is 17.52?

400

(x + 5y)(3x - y)

3x2 + 14xy -5y2

400

(-2/4)m + 7 = (1/6)m + 3

6

500

The equations for the upper and lower bounds are 

y = 0.5x + 16 and y = 0.5x -3, respectively.  

The range for x = 4 will be this.

What is -1 to 18?

500

If the correlation coefficient is 0.5, what percent of the variability in the dependent variable can be explained by a linear relationship to the independent variable?

25%

500

The relationship between the cost of a burger (y) and how much the patty weighs (x) is described by the LSRL, y = 0.97x - 0.9.  Interpret the slope in the context of the problem.

The cost of the burger will increase by $0.97 for every additional ounce.

500

(x - 8)2

x2 - 16x + 64

500

42 = 163n+6

n = -5/3 or -1 2/3