for judy only

2. Next, using Excel, calculate the relationship (r value) between the two variables. Recall that the Excel “formula” for correlation is “=CORREL.” What is the r value for the two variables that you have chosen? Is it positive or negative? Is it strong, medium, or weak? Note that it is best to have an r value that is medium or strong. It is recommended that you try a few different variables until you find two variables with an r value between .5 and 1 (or between -.5 and -1). Using Excel to calculate for my r and r squared I obtained an r value of 0.936 and an r squared of .876. This is a strong positive correlation.

3. Next, use Excel to create a scatterplot for the two variables. You decide which variable will be dependent (y) and which will be independent (x). On the scatterplot, include the “trendline” and the “equation for the line” using Excel options. Attach your scatterplot to your post.

4. Finally, using the equation of the line that you generated above, plug in any reasonable value for x (your chosen independent variable) and solve the equation for y (your chosen dependent variable). It is up to you to determine which of your two variables is x and which is y. What prediction do you get? Show all your work. In other words, type out the equation, plug in a value for x, and show your solution for y.

Using the equation y = .1534x + 3.309 and a weight of 140 pounds (x) I would expect a BMI of:

.1534(140) + 3.309 = 24.785

114.8 149.30000000000001 107.8 160.1 127.1 123.1 111.7 156.30000000000001 218.8 110.2 188.3 105.4 136.1 182.4 238.4 108.8 119 161.9 174.1 181.2 124.3 255.9 106.7 149.9 163.1 94.3 159.69999999999999 162.80000000000001 130 179.9 147.80000000000001 112.9 195.6 124.2 135 141.4 123.9 135.5 130.4 100.7 19.600000000000001 23.8 19.600000000000001 29.1 25.2 21.4 22 27.5 33.5 20.6 29.9 17.7 24 28.9 37.700000000000003 18.3 19.8 29.8 29.7 31.7 23.8 44.9 19.2 28.7 28.5 19.3 31 25.1 22.8 30.9 26.5 21.2 40.6 21.9 26 23.5 22.8 20.7 20.5 21.9

Weight

BMI