Directions for correlation and regression for example #1 in class on the TI30XII

1.      After turning on, go to EXIT STAT (2^nd STATVAR) to clear old work. (It will either clear it or give you an error if it was empty).

2.      Go to STAT (2^nd DATA)

3.      Select 2-VAR  (Recall, earlier in the semester when we were doing standard deviations that we selected 1-VAR).

4.      Go to DATA and input

    (Recall x= number of inches of snow per month, y=monthly snowplow bill)

5.      Go to STATVAR. Scroll through to see mans, standard deviations, and summations for both x and y. At the end is a (the slope of the regression line, known as b₁ in our book), b (the y-intercept in the regression line (b₀ in our book), and r (the correlation coefficient).

6.      Go to EXIT STAT (2^nd STATVAR) to clear your work before doing another example or before returning one of my calculators.

Results for Ex #1:

Calculator reads:

  26       =    95         =  244                  =    3325       = 900

a = 4.107           b =  -3.929        r=correlation=.9972

Conclusions:

So the correlation is strongly positive  at r =0.9972

The linear regression line is   4.107x – 3.929

Interpretation:

y-intercept: If we get no snow, my monthly bill is -$3.93

slope: For every inch of snow, my bill goes up $4.11.

Example #1

Predictor: x= hours of sleep

Response Variable: y= typing speed

x                      y                                                           xy

8                      30

6                      20

12                    45

              =                 =              =           =

r = 

Example #1

b=

 = =