state year pregnancyratetotal abortionratetotal AL 1988 100.5 21.3 AK 1988 120.4 19.8 AZ 1988 125.4 27 AR 1988 96.9 15 CA 1988 141.8 44.9 CO 1988 102.6 21.6 CT 1988 107.4 31 DE 1988 110.7 28.9 DC 1988 159.7 79.3 FL 1988 113.5 28.3 GA 1988 104.4 22.2 HI 1988 137.6 43.6 ID 1988 100.5 12.7 IL 1988 112.2 27 IN 1988 93.4 16.2 IA 1988 93.3 17.1 KS 1988 97.9 13 KY 1988 83.2 11.6 LA 1988 105 16.7 ME 1988 88.9 14.6 MD 1988 115.5 34.9 MA 1988 103.3 29.6 MI 1988 106.5 28.2 MN 1988 97.2 17 MS 1988 96 10.8 MO 1988 101.1 20.5 MT 1988 93.3 13.2 NE 1988 98.1 15.5 NV 1988 126.1 37.2 NH 1988 101.1 21.9 NJ 1988 116.4 36 NM 1988 118.3 23.4 NY 1988 123.5 41.5 NC 1988 101 24.2 ND 1988 95.3 10.4 OH 1988 97.9 20 OK 1988 98.7 17.2 OR 1988 101.3 23.4 PA 1988 95.2 19.6 RI 1988 98 24.2 SC 1988 101.5 19.8 SD 1988 99.4 9.4 TN 1988 92.7 17.4 TX 1988 116.8 24.2 UT 1988 125.3 11.9 VT 1988 95.6 21.6 VA 1988 103.1 26.6 WA 1988 108.8 27.8 WV 1988 76.5 11.7 WI 1988 94 16.6 WY 1988 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?