Compute the sample correlation coefficient for the following data: x- 1, 3, 4, 7, 9. Y- 5, 7, 10, 11, 15. Can you be 95% confident that a linear relation exists between the variables. If so, is it positive or negative? Justify the answer.

must reference formula from guide attached!

Independent Samples, confidence interval for (p1 - |.I2): (' ') 312 +322+ 0.05, then Case 3 (see below} should be used. Range. We defined the range earlier as the span between the smallest value in the set and the largest. Range = Largest value Smallest Value Interquarte range: 1' QR = 03 - 01 The mean for a population is given by: 2 I: 252 '1 _ N _ 6 _ 42 where N is the size of the entire population. The sample mean is given by: _ 2x, 116,260 x _ n _ 20 _ 5313 where n is the sample size. Formula for the location of a specific percentile: . _ P I (m) 11 Where: i is the index p is the desired percentage n is the number of observations in the data set. Population variance formula: 2 thi \" r02 0' = N where N is the size of the entire population. Sample variance formula: 52 = 20:. - 2): n 1 where n is the sample size. \fSTANDARD NORMAL DISTRIBUTION: Table Values Re oresent AREA to the LEFT of the Z score. Z .00 .01 .02 .03 .04 .05 .06 .07 .08 .09 0.0 .50000 .50399 .50798 .5119? .51595 .51994 .52392 .52790 .53188 .53 586 0.1 .53983 .54380 .54776 .55172 .5556? .55962 .56356 .56749 .57142 .57535 0.2 .57926 .5831? .58706 .59095 .59483 .5987] .60257 .60642 .61026 .61409 0.3 .61791 .62172 .62552 .62930 .6330? .63683 .64058 .6443] .64803 .65173 0.4 .65542 .65910 .66276 .66640 .67003 .67364 .67724 .68082 .68439 .68793 0.5 .69146 .69497 .69847 .70194 .70540 .70884 .71226 .71566 .71904 .72240 0.6 .72575 .7290? .7323? .73565 .7389] .742 I 5 .74537 .7485? .75175 .75490 0.7 .75804 .76115 .76424 .76730 .77035 .7733? .7763? .77935 .78230 .78524 0.8 .78814 .79103 .79389 .79673 .79955 .80234 .805] 1 .80785 .8105? .8132? 0.9 .81594 .81859 .8212] .8238] .82639 .82894 .8314? .83398 .83646 .8389] 1.0 .84134 .84375 .84614 .84849 .85083 .85314 .85543 .8 5769 .85993 .86214 1.1 .86433 .86650 .86864 .87076 .87286 .87493 .87698 .87900 .88100 .88298 1.2 .88493 .88686 .8887? .89065 .8925] .89435 .896] 7 .89796 .89973 .9014? 1.3 .90320 .90490 .90658 .90824 .90988 .91149 .91309 .91466 .9162] .91774 1.4 .91924 .92073 .92220 .92364 .92507 .92647 .92785 .92922 .93056 .93189 1.5 .93319 .93448 .93 574 .93699 .93822 .93943 .94062 .94179 .94295 .94408 1.6 .94520 .94630 .94738 .94845 .94950 .95053 .95154 .9 5254 .95352 .95449 1.7 .95543 .9563? .95 728 .95818 .9590? .95994 .96080 .96164 .96246 .9632? 1.8 .9640? .96485 .96562 .96638 .96712 .96784 .96856 .96926 .96995 .97062 1.9 .97128 .97193 .9725? .97320 .9738] .9744] .97500 .97558 .97615 .97670 2.0 .97725 .97778 .9783] .97882 .97932 .97982 .98030 .9807? .98124 .98169 2.1 .98214 .9825? .98300 .98341 .983 82 .98422 .98461 .9 8500 .9853? .98574 2.2 .98610 .98645 .98679 .98713 .98745 .98778 .98809 .9 8840 .98870 .98899 2.3 .98928 .98956 .98983 .99010 .99036 .9906] .99086 .99111 .99134 .99158 2.4 .99180 .99202 .99224 .99245 .99266 .99286 .99305 .99324 .99343 .9936] 2.5 .99379 .99396 .99413 .99430 .99446 .9946] .9947? .99492 .99506 .99520 2.6 .99534 .99547 .99560 .99573 .995 85 .99598 .99609 .9962] .99632 .99643 2.7 .99653 .99664 .99674 .99683 .99693 .99702 .997] 1 .99720 .99728 .99736 2.8 .99744 .99752 .99760 .9976? .99774 .9978] .99788 .99795 .9980] .99807 2.9 .99813 .99819 .99825 .9983] .99836 .9984] .99846 .9985] .99856 .9986] 3.0 .99865 .99869 .99874 .99878 .99882 .99886 .99889 .99893 .99896 .99900 3.1 .99903 .99906 .99910 .99913 .99916 .99918 .9992] .99924 .99926 .99929 3.2 .9993] .99934 .99936 .99938 .99940 .99942 .99944 .99946 .99948 .99950 3.3 .99952 .99953 .99955 .99957 .99958 .99960 .9996] .99962 .99964 .99965 3.4 .99966 .99968 .99969 .99970 .9997] .99972 .99973 .99974 .99975 .99976 3.5 .9997? .99978 .99978 .99979 .99980 .9998] .9998] .99982 .99983 .99983 3.6 .99984 .99985 .99985 .99986 .99986 .99987 .9998? .99988 .99988 .99989 3.7 .99989 .99990 .99990 .99990 .9999] .9999] .99992 .99992 .99992 .99992 3.8 .99993 .99993 .99993 .99994 .99994 .99994 .99994 .99995 .99995 .99995 3.9 .99995 .99995 .99996 .99996 .99996 .99996 .99996 .99996 .9999? .9999? Linear Regression: The equation of a line is given by: y = mx + in Where: m is the slope of the line. b is the y-intercept of the line. The best fit line may be calculated by: (/1 Hr. Cr" ll ~