state x data pregnancyratetotal Y data abortionratetotal AL 100.5 21.3 AK 120.4 19.8 AZ 125.4 27 AR 96.9 15 CA 141.8 44.9 CO 102.6 21.6 CT 107.4 31 DE 110.7 28.9 DC 159.7 79.3 FL 113.5 28.3 GA 104.4 22.2 HI 137.6 43.6 ID 100.5 12.7 IL 112.2 27 IN 93.4 16.2 IA 93.3 17.1 KS 97.9 13 KY 83.2 11.6 LA 105 16.7 ME 88.9 14.6 MD 115.5 34.9 MA 103.3 29.6 MI 106.5 28.2 MN 97.2 17 MS 96 10.8 MO 101.1 20.5 MT 93.3 13.2 NE 98.1 15.5 NV 126.1 37.2 NH 101.1 21.9 NJ 116.4 36 NM 118.3 23.4 NY 123.5 41.5 NC 101 24.2 ND 95.3 10.4 OH 97.9 20 OK 98.7 17.2 OR 101.3 23.4 PA 95.2 19.6 RI 98 24.2 SC 101.5 19.8 SD 99.4 9.4 TN 92.7 17.4 TX 116.8 24.2 UT 125.3 11.9 VT 95.6 21.6 VA 103.1 26.6 WA 108.8 27.8 WV 76.5 11.7 WI 94 16.6 WY 96.2 13

Data Set #1 - With Technology Here is a folder with the corresponding ::raw data sets:: labeled by subject, some of which have several quantitative variables. Find the data set corresponding to the scatter plot you chose above.

Copy and paste this data into your own spreadsheet for analysis. For more info about using spreadsheets to help with your analysis, see ::Google Sheets Walkthrough to Scatter plots and Regression::.

Use the spreadsheet functions to compute the slope, the intercept, and the correlation coefficient. If you decide to remove any outliers above, go find those data points in the columns and delete them. Did the slope, intercept, and correlation coefficient change dramatically? Looking at what those outliers represent in the data set, speculate why they do not follow the pattern of the rest of the data. Write a new sentence (similar to above) interpreting what the new slope means in the context of your variables. Was your estimation above close?

How close were your estimates from above?