Common Core: High School - Statistics and Probability - Fit a Linear Function for a Scatter Plot: CCSS.Math.Content.HSS-ID.B.6c

Use technology to find the least squares regression equation for the following data set.

$\begin{tabular}{|r|r|} \hline x: Vehicle weight (in pounds) & v: Milage (in MPG) \ \hline 6002 & 9 \ \hline 3610 & 14 \ \hline 5671 & 24 \ \hline 7226 & 38 \ \hline 2950 & 21 \ \hline 5519 & 10 \ \hline 9288 & 38 \ \hline 2650 & 16 \ \hline 6142 & 12 \ \hline 8885 & 12 \ \hline 2670 & 49 \ \hline 9466 & 22 \ \hline 6258 & 33 \ \hline 2984 & 19 \ \hline 3825 & 24 \ \hline 540 & 15 \ \hline 6056 & 43 \ \hline 7012 & 45 \ \hline 1382 & 27 \ \hline 6067 & 23 \ \hline 8047 & 5 \ \hline 8322 & 23 \ \hline 10164 & 31 \ \hline 9161 & 34 \ \hline 638 & 5 \ \hline 1590 & 38 \ \hline 7930 & 30 \ \hline 6861 & 49 \ \hline 7699 & 40 \ \hline 9060 & 19 \ \hline 2651 & 41 \ \hline 3529 & 26 \ \hline 9230 & 26 \ \hline 3157 & 14 \ \hline 5283 & 9 \ \hline 7879 & 36 \ \hline 9385 & 36 \ \hline 1077 & 44 \ \hline 7592 & 26 \ \hline 3848 & 39 \ \hline \end{tabular}$

Use technology to find the least squares regression equation for the following data set.

$\begin{tabular}{|r|r|} \hline x: Vehicle weight (in pounds) & v: Milage (in MPG) \ \hline 5415 & 11 \ \hline 3576 & 5 \ \hline 3431 & 27 \ \hline 2393 & 36 \ \hline 9839 & 41 \ \hline 10786 & 34 \ \hline 1839 & 14 \ \hline 4888 & 18 \ \hline 3055 & 21 \ \hline 9752 & 49 \ \hline 10301 & 33 \ \hline 10341 & 7 \ \hline 1593 & 38 \ \hline 1668 & 5 \ \hline 8072 & 28 \ \hline 947 & 31 \ \hline 2182 & 11 \ \hline 4504 & 38 \ \hline 2213 & 9 \ \hline 645 & 20 \ \hline 6896 & 13 \ \hline 4978 & 46 \ \hline 7352 & 26 \ \hline 3807 & 17 \ \hline 5898 & 30 \ \hline 1329 & 41 \ \hline 5190 & 38 \ \hline 10103 & 23 \ \hline 2246 & 36 \ \hline 11124 & 10 \ \hline 1062 & 35 \ \hline 5795 & 8 \ \hline 4652 & 6 \ \hline 7243 & 44 \ \hline 8222 & 44 \ \hline 3687 & 28 \ \hline 7119 & 19 \ \hline 6051 & 41 \ \hline 4858 & 14 \ \hline 9312 & 33 \ \hline \end{tabular}$

Use technology to find the least squares regression equation for the following data set.

$\begin{tabular}{|r|r|} \hline x: Vehicle weight (in pounds) & v: Milage (in MPG) \ \hline 3034 & 28 \ \hline 8712 & 34 \ \hline 1391 & 19 \ \hline 4319 & 16 \ \hline 8106 & 26 \ \hline 3055 & 19 \ \hline 8621 & 13 \ \hline 2851 & 15 \ \hline 4297 & 41 \ \hline 4001 & 28 \ \hline 1686 & 49 \ \hline 9564 & 47 \ \hline 668 & 12 \ \hline 1391 & 20 \ \hline 5830 & 8 \ \hline 7488 & 43 \ \hline 8946 & 44 \ \hline 1599 & 22 \ \hline 7751 & 42 \ \hline 5585 & 42 \ \hline 6112 & 43 \ \hline 2666 & 23 \ \hline 7714 & 9 \ \hline 1254 & 34 \ \hline 1742 & 46 \ \hline 5558 & 33 \ \hline 6640 & 49 \ \hline 10833 & 9 \ \hline 1880 & 10 \ \hline 7733 & 32 \ \hline 2800 & 40 \ \hline 2196 & 28 \ \hline 6410 & 31 \ \hline 6326 & 9 \ \hline 1492 & 46 \ \hline 1755 & 12 \ \hline 6295 & 29 \ \hline 7302 & 21 \ \hline 8903 & 31 \ \hline 2752 & 21 \ \hline \end{tabular}$

Use technology to find the least squares regression equation for the following data set.

Screen shot 2015 12 18 at 4.35.26 pm

Use technology to find the least squares regression equation for the following data set.

$\begin{tabular}{|r|r|} \hline x: Vehicle weight (in pounds) & v: Milage (in MPG) \ \hline 9161 & 35 \ \hline 9215 & 38 \ \hline 5733 & 47 \ \hline 1207 & 39 \ \hline 6653 & 7 \ \hline 10291 & 35 \ \hline 6751 & 13 \ \hline 2676 & 18 \ \hline 11101 & 23 \ \hline 4562 & 11 \ \hline 10598 & 35 \ \hline 2797 & 37 \ \hline 1259 & 10 \ \hline 2142 & 47 \ \hline 3841 & 32 \ \hline 10298 & 11 \ \hline 4193 & 20 \ \hline 7256 & 39 \ \hline 3337 & 28 \ \hline 1558 & 32 \ \hline 2141 & 29 \ \hline 3321 & 36 \ \hline 4117 & 32 \ \hline 717 & 35 \ \hline 4334 & 21 \ \hline 7667 & 47 \ \hline 6712 & 43 \ \hline 10408 & 46 \ \hline 4333 & 46 \ \hline 4034 & 40 \ \hline 9115 & 29 \ \hline 9654 & 9 \ \hline 10500 & 48 \ \hline 6640 & 9 \ \hline 3903 & 28 \ \hline 6037 & 32 \ \hline 7400 & 48 \ \hline 7200 & 35 \ \hline 4405 & 6 \ \hline 1837 & 26 \ \hline \end{tabular}$

Use technology to find the least squares regression equation for the following data set.

$\begin{tabular}{|r|r|} \hline x: Vehicle weight (in pounds) & v: Milage (in MPG) \ \hline 2278 & 28 \ \hline 8206 & 32 \ \hline 3246 & 7 \ \hline 6339 & 15 \ \hline 11870 & 49 \ \hline 4599 & 49 \ \hline 6120 & 39 \ \hline 6065 & 33 \ \hline 5271 & 18 \ \hline 565 & 9 \ \hline 5502 & 17 \ \hline 5209 & 6 \ \hline 5206 & 44 \ \hline 5856 & 10 \ \hline 11133 & 7 \ \hline 7417 & 45 \ \hline 2104 & 14 \ \hline 5708 & 42 \ \hline 1470 & 9 \ \hline 10426 & 17 \ \hline 10043 & 37 \ \hline 7178 & 12 \ \hline 10235 & 20 \ \hline 3858 & 33 \ \hline 4149 & 45 \ \hline 4851 & 22 \ \hline 11321 & 25 \ \hline 5322 & 18 \ \hline 11132 & 21 \ \hline 8122 & 17 \ \hline 6002 & 42 \ \hline 3120 & 14 \ \hline 4537 & 32 \ \hline 7257 & 20 \ \hline 1884 & 11 \ \hline 3035 & 9 \ \hline 8723 & 25 \ \hline 8582 & 35 \ \hline 10486 & 37 \ \hline 1231 & 49 \ \hline \end{tabular}$

Use technology to find the least squares regression equation for the following data set.

$\begin{tabular}{|r|r|} \hline x: Vehicle weight (in pounds) & v: Milage (in MPG) \ \hline 2651 & 43 \ \hline 8351 & 46 \ \hline 11009 & 29 \ \hline 4599 & 28 \ \hline 4142 & 33 \ \hline 10683 & 28 \ \hline 6367 & 14 \ \hline 6227 & 49 \ \hline 2626 & 35 \ \hline 8047 & 38 \ \hline 4850 & 34 \ \hline 4153 & 41 \ \hline 10773 & 33 \ \hline 1335 & 41 \ \hline 599 & 43 \ \hline 11213 & 26 \ \hline 4710 & 6 \ \hline 9313 & 36 \ \hline 2369 & 47 \ \hline 6975 & 47 \ \hline 1966 & 32 \ \hline 5773 & 47 \ \hline 4882 & 49 \ \hline 4563 & 15 \ \hline 1318 & 13 \ \hline 6092 & 49 \ \hline 11295 & 29 \ \hline 881 & 12 \ \hline 7323 & 46 \ \hline 5380 & 33 \ \hline 7290 & 10 \ \hline 7518 & 27 \ \hline 1902 & 21 \ \hline 11147 & 29 \ \hline 5096 & 41 \ \hline 11686 & 39 \ \hline 3539 & 36 \ \hline 3415 & 6 \ \hline 7761 & 44 \ \hline 11231 & 45 \ \hline \end{tabular}$

Use technology to find the least squares regression equation for the following data set.

$\begin{tabular}{|r|r|} \hline x: Vehicle weight (in pounds) & v: Milage (in MPG) \ \hline 11144 & 20 \ \hline 1855 & 10 \ \hline 3944 & 42 \ \hline 4130 & 25 \ \hline 6980 & 46 \ \hline 1253 & 8 \ \hline 882 & 22 \ \hline 7179 & 5 \ \hline 6441 & 36 \ \hline 7517 & 46 \ \hline 2851 & 46 \ \hline 7141 & 19 \ \hline 10278 & 42 \ \hline 11216 & 21 \ \hline 2401 & 37 \ \hline 8199 & 47 \ \hline 3486 & 34 \ \hline 1365 & 6 \ \hline 9592 & 38 \ \hline 7283 & 35 \ \hline 5121 & 11 \ \hline 10096 & 13 \ \hline 4884 & 21 \ \hline 1020 & 16 \ \hline 8909 & 47 \ \hline 11949 & 40 \ \hline 6921 & 11 \ \hline 1720 & 16 \ \hline 9961 & 9 \ \hline 3943 & 34 \ \hline 2200 & 25 \ \hline 11200 & 11 \ \hline 2355 & 22 \ \hline 8015 & 40 \ \hline 11842 & 48 \ \hline 1381 & 16 \ \hline 9604 & 45 \ \hline 2075 & 17 \ \hline 4849 & 34 \ \hline 9335 & 12 \ \hline \end{tabular}$

Use technology to find the least squares regression equation for the following data set.

$\begin{tabular}{|r|r|} \hline x: Vehicle weight (in pounds) & v: Milage (in MPG) \ \hline 8597 & 30 \ \hline 6477 & 10 \ \hline 3096 & 31 \ \hline 10547 & 9 \ \hline 5574 & 11 \ \hline 3567 & 22 \ \hline 3141 & 23 \ \hline 4174 & 15 \ \hline 9247 & 32 \ \hline 1614 & 8 \ \hline 6038 & 16 \ \hline 8913 & 38 \ \hline 10956 & 45 \ \hline 4794 & 32 \ \hline 10449 & 10 \ \hline 9828 & 32 \ \hline 3973 & 6 \ \hline 6581 & 47 \ \hline 1627 & 20 \ \hline 3161 & 39 \ \hline 8944 & 15 \ \hline 3168 & 44 \ \hline 3635 & 8 \ \hline 8754 & 6 \ \hline 762 & 5 \ \hline 7362 & 7 \ \hline 10234 & 38 \ \hline 11446 & 11 \ \hline 1644 & 26 \ \hline 10164 & 8 \ \hline 4367 & 46 \ \hline 7297 & 33 \ \hline 4732 & 9 \ \hline 5897 & 34 \ \hline 2919 & 34 \ \hline 6402 & 10 \ \hline 7421 & 40 \ \hline 4548 & 32 \ \hline 8426 & 14 \ \hline 2456 & 6 \ \hline \end{tabular}$

Use technology to find the least squares regression equation for the following data set.

Fit a Linear Function for a Scatter Plot: CCSS.Math.Content.HSS-ID.B.6c

Practice Questions