Data Table mance of stock in rchases stock in the These values are ry variables. Comp of data ck in a similar Company returns are the same as and excess returns on the mave linear patterns Excess Retur Q Company X Excess Return -0.04 1201 -0.002526 0.177416 0.028569 0.043985 0.078945 -0.068046 -0.122481 -0.222091 0.053889 0.193263 0.163972 0.285951 0.028994 0.183345 -0.196549 -0.147858 -0.121200 0.109557 0.144045 -0.070541 0.036087 -0.016201 0.107021 0.145583 0.041533 -0.140469 0.028921 -0.008921 Market Excess Return -0.075995 0.009085 0.017849 -0.035210 0.082628 -0.010008 -0.016052 -0.098008 -0.059922 -0.019000 0.060161 0.023447 0.044484 0.071023 0.024596 -0.002125 0.036343 -0.048169 0.041871 0.022109 -0.015470 0.013470 -0.041225 0.102882 -0.004175 0.010562 0.026978 0.010013 0.003746 Index Excess Return -0.074772 0.002757 0.017833 0.033771 0.085472 -0.014719 0.011851 -0.100337 0.057307 -0.013182 0.054249 0.018875 0.036740 0.062432 0.017734 0.005132 0.034 105 -0.052202 0.040189 0.015036 -0.023543 0.007712 -0.047844 0.107807 -0.022983 0.006767 -0.025063 0.024701 -0.001646 Company Y Excess Return 0.042074 0.059742 0.015252 0.020261 0.10559 -0.026736 -0.057653 - 0.081519 0.038301 -0.015853 0.08401 -0.011601 0.11693 0.020627 -0.119963 -0.100873 0.037497 -0.088986 0.037567 -0.035751 0.065176 -0.056189 -0.050179 0.041855 0.008263 -0.024067 -0.042283 0.083455 0.010651 cess Return Print Done 1 Data Table performance of stock in that purchases stock in the by 100.) These values are planatory variables. Compl e table of data. bck in a simila returns are the and excess o returns have linear patterns mpany X Excess Return -0.197756 -0.028902 -0.016045 -0.021606 0.161221 0.095094 0.036862 -0.006589 -0.109359 -0.030885 -0.007300 0.105089 -0.305196 - 0.299841 -0.043125 0.120279 0.313316 0.025820 0.073633 0.117168 0.116056 -0.091585 -0.100548 -0.024141 -0.097211 0.288801 0.074045 -0.072821 0.278116 -0.138509 -0.022488 0.037062 -0.023528 0.009790 0.008637 0.037766 0.015470 0.010199 0.003300 0.022617 -0.027676 0.027478 0.002660 0.003198 0.036929 0.001719 0.015949 -0.019996 0.017343 0.029051 -0.026438 -0.048356 0.006673 0.006267 -0.031058 0.027850 0,038804 -0.022029 0.010628 -0.040813 AR -0.020496 0.036395 -0.026715 0.006415 -0.000118 0.027917 0.007791 0.004538 0.008282 0.016099 0.027656 0.020589 -0.002028 -0.007756 0.031845 -0.012271 0.017228 -0.015434 0.007770 0.030072 -0.032029 -0.048136 0.008826 0.009239 0.029845 0.028663 0.033915 -0.030630 0.017039 -0.043237 0.075403 -0.034786 -0.07555 -0.070405 -0.174039 0.036221 -0.264173 0.019925 0.064182 -0.066953 -0.046675 0.093809 -0.066749 -0.101106 0.031309 -0.084321 0.093047 0.174157 0.046523 -0.002489 -0.061847 0.03055 0.049824 0.09888 -0.070557 0.050571 0.10738 0.012684 0.066128 -0.050791 A. Market Excess Return answer Print Dono NacBook Pro 1 Data Table performance of stock in that purchases stock in th by 100.) These values are planatory variables. Compl e table of data. bck in a simi returns are and excess eturns have linear patterns mpany X Excess Return Q -0.130009 0.042578 0.031139 -0.022566 -0.111764 0.080573 0.084385 0.112466 -0.035452 -0.046461 -0.138016 -0.029918 0.049204 -0.168699 -0.137514 -0.008494 -0.110865 -0.012325 0.067795 -0.200050 0.043083 0.098202 -0.089320 0.032260 0.044999 -0.139122 -0.207547 -0.026379 0.118772 -0.072878 0.026330 -0.00013 0.008226 0.016183 0.035587 0.022886 0.020669 0.028684 0.026411 0.036205 0.004731 0.032129 -0.016003 0.038631 0.010300 0.023927 0.012330 0.006986 0.020872 0.022884 -0.012337 -0.058379 0.028373 0.048630 0.009411 0.061669 -0.015761 0.049450 -0.004928 -0.049079 0.038001 0.067368 -0.043237 0.007794 0.020182 0.032105 0.023196 0.023679 0.030947 0.016604 0.027282 -0.004876 0.035805 -0.009706 0.036815 0.012423 0.028332 0.002914 0.004086 0.008778 0.018840 -0.001749 -0.050315 0.014707 0.049865 0.021974 0.069423 0.025989 0.057287 0.001997 0.046884 0.054040 0.054351 -UUDUTYT 0.034468 -0.022822 0.042933 0.087192 0.147671 -0.019885 0.027427 0.12962 -0.052905 -0.090147 0.02435 -0.007868 -0.059095 0.183236 0.128516 -0.096813 -0.036157 -0.010033 -0.076474 0.081322 0.06314 0.084261 0.031781 0.234265 -0.053819 0.031533 -0.085259 -0.049522 0.165011 0.078103 Market Excess Return Print Done answerts) NEBOOK PO returns are the and excess retur urchases stock in the 2.) These values are ory variables. Comp of data have linear patterns X Excess Return 0.07.2010 -0.026330 -0.146488 0.223701 0.238750 -0.007346 -0.218632 0.038468 0.265249 0.390926 0.286277 0.159624 -0.008733 -0.031239 0.073538 0.202996 -0.103403 0.218509 -0.029513 0.143003 0.277898 0.002647 -0.158161 0.028354 0.276184 -0.045581 0.047070 0.198659 0.167953 -0.033298 0 262033 U.USDUUT 0.067368 0.040337 0.072128 -0.040317 0.053792 -0.038331 0.026650 0.013078 0.000284 0.069284 0.046863 0.006588 -0.029460 0.027904 -0.027208 -0,161677 0.059864 0.071026 0.058713 0.059287 0.034820 -0.041398 0.033885 0.045299 -0.024237 0.046834 -0.034389 -0.013877 -0.026469 0.058435 U.05090 0.054351 0.039719 0.073901 -0.061339 0.048723 -0.038474 0.040720 0.010956 0.005977 0.066678 0.045375 0.004845 -0.022495 0.035262 -0.015446 -0.149815 0.058592 0.076911 0.055872 0.052547 0.037498 -0.035556 0.034647 0.034066 -0.028357 0.050278 -0.035618 -0.010234 -0.032416 0,058941 UTOJUTT 0.076103 0.039726 0.167373 -0.043589 0.041155 -0.074691 0.109973 -0.049209 -0.080469 0.055796 -0.009779 0.11134 0.012078 -0.026795 0.150076 -0.15247 0.137016 0.152362 0.11016 0.112671 -0.00956 -0.075851 0.040226 0.178385 0.10674 0.110234 -0.031337 -0.011756 -0.032201 -0.191529 of Excess Return Print Done wer(s) Data Table returns are and excess monthly performance of stock in portfolio that purchases stock in th multiplied by 100.) These values are Y as explanatory variables. Comp o view the table of data. excess returns have linear patterns Not of Company X Excess Return 0.217769 0.046572 0.004756 0.100603 0.180375 -0.091060 -0.327771 0.243355 -0.034736 0.194344 -0.582493 -0.245606 -0.161633 -0.103321 0.448454 -0.159763 0.204873 0.151174 -0.220481 0.102750 -0.194947 -0.015693 -0.100611 0.130062 0.211114 0.026586 0.127301 -0.123547 0.089400 0.023675 0.033047 0.080049 -0.044028 0.027572 0.049143 -0.084005 -0.043859 0.047900 -0.021940 0.070802 -0.066134 -0.029838 -0.107542 0.015479 0.034201 -0.102887 -0.074655 0.079956 0.007402 -0.020177 -0.021212 -0.002123 -0.004103 0.025728 0.076880 0.016308 -0.017370 -0.023031 0.043242 -0.051171 AL 0.015379 0.054159 -0.055285 -0.024307 0.092238 -0.035507 -0.026759 0.020282 -0.021153 0.055571 -0.058541 -0.010176 -0.084918 -0.000889 0.029397 -0.095837 -0.058639 0.073065 0.002010 -0.027714 -0.013719 -0.067217 -0.084473 0.016020 0.073334 0.006142 -0.017032 -0.021974 0035264 -0.062878 0.046446 0.042816 0.036193 -0.087776 0.143947 -0.059627 0.040691 0.016707 0.021333 0.170228 -0.152782 -0.120763 -0.05453 -0.095742 0.312315 -0.11057 -0.041683 0.193340 -0.030931 0.008074 -0.072082 -0.051683 0.085071 0.170171 0.000006 0.044871 -0.109518 -0.000641 0.058552 -0.00294 imeplot of Market Excess Retur Print Done o select your answer Data Table acked on monthly performance of stock in eighted portfolio that purchases stock in the being multiplied by 100.) These values are Company Y as explanatory variables. Comp icon to view the table of data. pekinas returns a and exces of these excess returrys have linear patterns a timoplot of Company X Excess Return 0.129646 -0.047464 0.087785 0.158817 -0.006982 0.065402 0.122273 0.351064 0.278113 -0.041106 0.192470 0.165105 -0.073244 -0.136629 0.100108 -0.076440 0.156200 0.098571 0.140421 0.071438 0.174627 0.056840 0.046896 -0.098164 -0.0878.86 0.118740 -0.155066 -0.045738 0.182097 -0.005894 -0.011475 -0.025099 0.013487 0.020661 -0.038520 0.001633 0.019459 0.016573 0.046734 0.033507 -0.028163 0.020928 -0.019142 -0.027321 0.035479 0.009243 0.040835 -0.008759 0.007756 -0.023433 0.037388 0.000271 0,036544 -0.004982 0.015409 0.009498 -0.035156 -0.004285 -0.005792 0.020684 -0.017088 -0.017528 0.011451 0.017171 -0.035234 0.001166 0.008292 0.012989 0.037166 0.030991 -0.027064 0.017152 -0.021353 -0.022113 0.027520 -0.002445 0.033458 -0.014151 0.004038 -0.020353 0.032198 -0.004184 0.021925 -0.002894 0.007506 0.008666 -0.035169 -0.003950 0.001185 0.017082 -0.049077 -0.040829 0.006147 -0.005809 -0.013085 -0.026344 0.011155 0.045572 0.050569 0.044473 -0.05397 -0.008669 -0.016096 -0.166148 -0.01073 -0.02013 0.122342 -0.034472 -0.007857 0.017888 0.065172 -0.078538 -0.014407 -0.013769 0.024152 -0.005116 -0.030181 -0.042456 0.003718 0.045689 Tut a timeplot of Market Excess Return 04 Print Dono lick to select your answers). Q esc & Data werstrecked on monthly performance of stock in Company. The data included 200 monthly terms on Company as well as rolumns on the entire stock marta stock index, and book na si Company Mature on su wind portfolio that purchases stock in the markets in proportion to the size of the company rather than one of each sock) Then excess returns were computed (Expessura e re excess permite chly without being multipled by 100. These we given the accompanying table in chronological order Formule the regression wth come runs on Company as the responw and come one who must be now, wd Company Yos explanatory Complete para (a) through below. mclick the icon to view the table of data (Do any of these stur have linear parts over time? Umelots of each one to se. Do any months stand out an unusual Construct a time of Company X Excess Retum OB Corindo Mar Excess Retur OC Q OU G2 Construct a time lot of Index Excess Retur , OD 02 oo Construct a medio el Company YExcess Return OA OB oc mo whether the Domyoftheates are income Click to you ower Data were tracked on monthly performance of stock in Company X. The data included 200 monthly returns on Company X, as well as rolumns on the entire stock market a stock index, and stock na si Co on a value-weighted portfolio that purchases stock in the major markets in proportion to the size of the company rather than one of each stock.) Then excess retums were computed. (Excess retums are the only without being multiplied by 100.) These values are given in the accompanying table in chronological order Formulate the regression with excess retums on Company X as the response and excess stume Index, and Company Y as explanatory variables, Complete parts (a) through ( below. Click the icon to view the table of data. Do any of these variables have a patterns over time? Company X Excess Return Market Excess Reum Index Exco Rotum Company YExcess Retum Do ary months stand out as unusual? Select all that apply A Yes, the timeplot for Company Y Ex Return has at least one outler UB. Yes, the meclot for Index Excess Return has a least one outlet Yes. The implotto Market Excess Return has a lot one cutie D. Yes, the timelor for Company X Excess Return has that one outier DE Notre do not appear to be any others 6) the indicated miple regression Does the estimated multiple regression explain stately significant variation in the excess ruums on Company X Loty - Goa X Excess Ruum X-Manet Excess Return X, index Excess Return, and X-Company YExces Retur. Complete the multiple regression equation below. med Y.XXX Round to four decimal places as needed. Round of X, to the decimal places ned. Houndofur values to two decades as needed Does the multiple regression explain tally cont variation in the returns on CUMA 005 Click to select your Data were tracked on monthly performance of stock in Company X. The data included 200 monthly retums on Company X, as well as returns on the entire stock market, a stock indee on a value-weighted portfolio that purchases stock in the major markets in proportion to the size of the company rather than one of each stock) Then excess returns were computed, only without being multiplied by 100.) These values are given in the accompanying table in chronological order. Formulate the regression with excess robums on Company X as there index, and Company Y as explanatory variables. Complete parts () through ( below. DEL Click the icon to view the table of data Estimated Y-0.x, (XX (Round the constant to four decimal places as noeded. Round the coefficient of X, to three decimal places as needed. Round all other values to two decimal places as needed) Does the estimated multiple regression explain statistically significant variation in the excess returns on Company X? Use a 0,06 State the null and alternative hypotheses OA HOB2B-0 H: At least one is different from 0. OB. Ho: PB5 H:At least one is different from 0. OCH 1150 HEBBP3 OD. Ho: At least one is different from 0. HB "B" 0 Determine the test statistic (Round to two decimal places as noeded) Determine the p-value Click to select your answerte) Round to three decimal places as needed) State the appropriate conclusion the null hypothesis. There evidence to conclude that the multiple regression, taken as a whole statistically wgnificant variation in the excess ons on Company (c) Doon collinanity affect the estimated effects of these explanatory variables in the estimated equation? In particular, do the partial effects create a different sense of importance from what is suggested by magihai efecte? A. Yes. The marginal slope for Marvel Excess Ratum is negative and the partial slope is positive OB. Yes. The marginal kope for Index Excess Ratum is positive, and the partial slope is negative OG NO. The partial effects we similar to the marginal effects of all explanatory variables D. Yes. The marginal slope for Company YExcess Rotum is negative, and the partial slope is positive (a) Which explanatory variable has the West VIF? has the largest VIF, VIF Hound to the decimal plan ded) (e) How would you suggest improving this model or would you leave tas is? OA The model should be well as is since we are significant and there is not guarcolarity OB. The moon can be improved by removing Marka Excess Raumance that contratach the repression pustion OG The moon can be neroved by moving Index Eros Rebuince it is not want OD The modne motoved by removing Index Ecturn since lasciarlty that affects the regression on Click to see your own V) below inson Company X as the response and Click the icon to view the table of data. O A. Yes. The marginal slope for Market Excess Retum is negative, and the partial slope is positive. OB. Yes. The marginal slope for Index Excess Return is positive, and the partial slope is negative. OC. No. The partial effects are similar to the marginal effects of all explanatory variables. OD. Yes. The marginal slope for Company Y Excess Return is negative, and the partial slope is positive. (d) Which explanatory variable has the largest VIF? V has the largest VIF, with VF- (Round to one decimal place as needed.) (e) How would you suggest improving this model, or would you just leave it as is? O A. The model should be left as it is since all variables are significant and there is not significant collinearty. OB. The model can be improved by removing Market Excess Return since it has colinearity that affects the regression equation OC. The model can be improved by removing Index Excess Return since it is not significant OD. The model can be improved by removing Index Excess Rotun since has colinearity that affects the negrosion equation (Interpret nubstantively the fit of your model which might be the one the question starts with OA. The best model in the original model. In this model, all variables are significant and there is no significant collinearity OB. The best model is the one win index Exodus Retumn and Company Exons Return. In this model, both variables are graficant and there is no significant collinearly OC. The best model is the one with only Market Ecoss Return since this is the most significant variable OD. The best model in the one with Market Excess Return and Company YExcess Retum. In this model both variables are grificant and there is no significant coinearity Click to enot your answerts) MeBook Pro Data Table mance of stock in rchases stock in the These values are ry variables. Comp of data ck in a similar Company returns are the same as and excess returns on the mave linear patterns Excess Retur Q Company X Excess Return -0.04 1201 -0.002526 0.177416 0.028569 0.043985 0.078945 -0.068046 -0.122481 -0.222091 0.053889 0.193263 0.163972 0.285951 0.028994 0.183345 -0.196549 -0.147858 -0.121200 0.109557 0.144045 -0.070541 0.036087 -0.016201 0.107021 0.145583 0.041533 -0.140469 0.028921 -0.008921 Market Excess Return -0.075995 0.009085 0.017849 -0.035210 0.082628 -0.010008 -0.016052 -0.098008 -0.059922 -0.019000 0.060161 0.023447 0.044484 0.071023 0.024596 -0.002125 0.036343 -0.048169 0.041871 0.022109 -0.015470 0.013470 -0.041225 0.102882 -0.004175 0.010562 0.026978 0.010013 0.003746 Index Excess Return -0.074772 0.002757 0.017833 0.033771 0.085472 -0.014719 0.011851 -0.100337 0.057307 -0.013182 0.054249 0.018875 0.036740 0.062432 0.017734 0.005132 0.034 105 -0.052202 0.040189 0.015036 -0.023543 0.007712 -0.047844 0.107807 -0.022983 0.006767 -0.025063 0.024701 -0.001646 Company Y Excess Return 0.042074 0.059742 0.015252 0.020261 0.10559 -0.026736 -0.057653 - 0.081519 0.038301 -0.015853 0.08401 -0.011601 0.11693 0.020627 -0.119963 -0.100873 0.037497 -0.088986 0.037567 -0.035751 0.065176 -0.056189 -0.050179 0.041855 0.008263 -0.024067 -0.042283 0.083455 0.010651 cess Return Print Done 1 Data Table performance of stock in that purchases stock in the by 100.) These values are planatory variables. Compl e table of data. bck in a simila returns are the and excess o returns have linear patterns mpany X Excess Return -0.197756 -0.028902 -0.016045 -0.021606 0.161221 0.095094 0.036862 -0.006589 -0.109359 -0.030885 -0.007300 0.105089 -0.305196 - 0.299841 -0.043125 0.120279 0.313316 0.025820 0.073633 0.117168 0.116056 -0.091585 -0.100548 -0.024141 -0.097211 0.288801 0.074045 -0.072821 0.278116 -0.138509 -0.022488 0.037062 -0.023528 0.009790 0.008637 0.037766 0.015470 0.010199 0.003300 0.022617 -0.027676 0.027478 0.002660 0.003198 0.036929 0.001719 0.015949 -0.019996 0.017343 0.029051 -0.026438 -0.048356 0.006673 0.006267 -0.031058 0.027850 0,038804 -0.022029 0.010628 -0.040813 AR -0.020496 0.036395 -0.026715 0.006415 -0.000118 0.027917 0.007791 0.004538 0.008282 0.016099 0.027656 0.020589 -0.002028 -0.007756 0.031845 -0.012271 0.017228 -0.015434 0.007770 0.030072 -0.032029 -0.048136 0.008826 0.009239 0.029845 0.028663 0.033915 -0.030630 0.017039 -0.043237 0.075403 -0.034786 -0.07555 -0.070405 -0.174039 0.036221 -0.264173 0.019925 0.064182 -0.066953 -0.046675 0.093809 -0.066749 -0.101106 0.031309 -0.084321 0.093047 0.174157 0.046523 -0.002489 -0.061847 0.03055 0.049824 0.09888 -0.070557 0.050571 0.10738 0.012684 0.066128 -0.050791 A. Market Excess Return answer Print Dono NacBook Pro 1 Data Table performance of stock in that purchases stock in th by 100.) These values are planatory variables. Compl e table of data. bck in a simi returns are and excess eturns have linear patterns mpany X Excess Return Q -0.130009 0.042578 0.031139 -0.022566 -0.111764 0.080573 0.084385 0.112466 -0.035452 -0.046461 -0.138016 -0.029918 0.049204 -0.168699 -0.137514 -0.008494 -0.110865 -0.012325 0.067795 -0.200050 0.043083 0.098202 -0.089320 0.032260 0.044999 -0.139122 -0.207547 -0.026379 0.118772 -0.072878 0.026330 -0.00013 0.008226 0.016183 0.035587 0.022886 0.020669 0.028684 0.026411 0.036205 0.004731 0.032129 -0.016003 0.038631 0.010300 0.023927 0.012330 0.006986 0.020872 0.022884 -0.012337 -0.058379 0.028373 0.048630 0.009411 0.061669 -0.015761 0.049450 -0.004928 -0.049079 0.038001 0.067368 -0.043237 0.007794 0.020182 0.032105 0.023196 0.023679 0.030947 0.016604 0.027282 -0.004876 0.035805 -0.009706 0.036815 0.012423 0.028332 0.002914 0.004086 0.008778 0.018840 -0.001749 -0.050315 0.014707 0.049865 0.021974 0.069423 0.025989 0.057287 0.001997 0.046884 0.054040 0.054351 -UUDUTYT 0.034468 -0.022822 0.042933 0.087192 0.147671 -0.019885 0.027427 0.12962 -0.052905 -0.090147 0.02435 -0.007868 -0.059095 0.183236 0.128516 -0.096813 -0.036157 -0.010033 -0.076474 0.081322 0.06314 0.084261 0.031781 0.234265 -0.053819 0.031533 -0.085259 -0.049522 0.165011 0.078103 Market Excess Return Print Done answerts) NEBOOK PO returns are the and excess retur urchases stock in the 2.) These values are ory variables. Comp of data have linear patterns X Excess Return 0.07.2010 -0.026330 -0.146488 0.223701 0.238750 -0.007346 -0.218632 0.038468 0.265249 0.390926 0.286277 0.159624 -0.008733 -0.031239 0.073538 0.202996 -0.103403 0.218509 -0.029513 0.143003 0.277898 0.002647 -0.158161 0.028354 0.276184 -0.045581 0.047070 0.198659 0.167953 -0.033298 0 262033 U.USDUUT 0.067368 0.040337 0.072128 -0.040317 0.053792 -0.038331 0.026650 0.013078 0.000284 0.069284 0.046863 0.006588 -0.029460 0.027904 -0.027208 -0,161677 0.059864 0.071026 0.058713 0.059287 0.034820 -0.041398 0.033885 0.045299 -0.024237 0.046834 -0.034389 -0.013877 -0.026469 0.058435 U.05090 0.054351 0.039719 0.073901 -0.061339 0.048723 -0.038474 0.040720 0.010956 0.005977 0.066678 0.045375 0.004845 -0.022495 0.035262 -0.015446 -0.149815 0.058592 0.076911 0.055872 0.052547 0.037498 -0.035556 0.034647 0.034066 -0.028357 0.050278 -0.035618 -0.010234 -0.032416 0,058941 UTOJUTT 0.076103 0.039726 0.167373 -0.043589 0.041155 -0.074691 0.109973 -0.049209 -0.080469 0.055796 -0.009779 0.11134 0.012078 -0.026795 0.150076 -0.15247 0.137016 0.152362 0.11016 0.112671 -0.00956 -0.075851 0.040226 0.178385 0.10674 0.110234 -0.031337 -0.011756 -0.032201 -0.191529 of Excess Return Print Done wer(s) Data Table returns are and excess monthly performance of stock in portfolio that purchases stock in th multiplied by 100.) These values are Y as explanatory variables. Comp o view the table of data. excess returns have linear patterns Not of Company X Excess Return 0.217769 0.046572 0.004756 0.100603 0.180375 -0.091060 -0.327771 0.243355 -0.034736 0.194344 -0.582493 -0.245606 -0.161633 -0.103321 0.448454 -0.159763 0.204873 0.151174 -0.220481 0.102750 -0.194947 -0.015693 -0.100611 0.130062 0.211114 0.026586 0.127301 -0.123547 0.089400 0.023675 0.033047 0.080049 -0.044028 0.027572 0.049143 -0.084005 -0.043859 0.047900 -0.021940 0.070802 -0.066134 -0.029838 -0.107542 0.015479 0.034201 -0.102887 -0.074655 0.079956 0.007402 -0.020177 -0.021212 -0.002123 -0.004103 0.025728 0.076880 0.016308 -0.017370 -0.023031 0.043242 -0.051171 AL 0.015379 0.054159 -0.055285 -0.024307 0.092238 -0.035507 -0.026759 0.020282 -0.021153 0.055571 -0.058541 -0.010176 -0.084918 -0.000889 0.029397 -0.095837 -0.058639 0.073065 0.002010 -0.027714 -0.013719 -0.067217 -0.084473 0.016020 0.073334 0.006142 -0.017032 -0.021974 0035264 -0.062878 0.046446 0.042816 0.036193 -0.087776 0.143947 -0.059627 0.040691 0.016707 0.021333 0.170228 -0.152782 -0.120763 -0.05453 -0.095742 0.312315 -0.11057 -0.041683 0.193340 -0.030931 0.008074 -0.072082 -0.051683 0.085071 0.170171 0.000006 0.044871 -0.109518 -0.000641 0.058552 -0.00294 imeplot of Market Excess Retur Print Done o select your answer Data Table acked on monthly performance of stock in eighted portfolio that purchases stock in the being multiplied by 100.) These values are Company Y as explanatory variables. Comp icon to view the table of data. pekinas returns a and exces of these excess returrys have linear patterns a timoplot of Company X Excess Return 0.129646 -0.047464 0.087785 0.158817 -0.006982 0.065402 0.122273 0.351064 0.278113 -0.041106 0.192470 0.165105 -0.073244 -0.136629 0.100108 -0.076440 0.156200 0.098571 0.140421 0.071438 0.174627 0.056840 0.046896 -0.098164 -0.0878.86 0.118740 -0.155066 -0.045738 0.182097 -0.005894 -0.011475 -0.025099 0.013487 0.020661 -0.038520 0.001633 0.019459 0.016573 0.046734 0.033507 -0.028163 0.020928 -0.019142 -0.027321 0.035479 0.009243 0.040835 -0.008759 0.007756 -0.023433 0.037388 0.000271 0,036544 -0.004982 0.015409 0.009498 -0.035156 -0.004285 -0.005792 0.020684 -0.017088 -0.017528 0.011451 0.017171 -0.035234 0.001166 0.008292 0.012989 0.037166 0.030991 -0.027064 0.017152 -0.021353 -0.022113 0.027520 -0.002445 0.033458 -0.014151 0.004038 -0.020353 0.032198 -0.004184 0.021925 -0.002894 0.007506 0.008666 -0.035169 -0.003950 0.001185 0.017082 -0.049077 -0.040829 0.006147 -0.005809 -0.013085 -0.026344 0.011155 0.045572 0.050569 0.044473 -0.05397 -0.008669 -0.016096 -0.166148 -0.01073 -0.02013 0.122342 -0.034472 -0.007857 0.017888 0.065172 -0.078538 -0.014407 -0.013769 0.024152 -0.005116 -0.030181 -0.042456 0.003718 0.045689 Tut a timeplot of Market Excess Return 04 Print Dono lick to select your answers). Q esc & Data werstrecked on monthly performance of stock in Company. The data included 200 monthly terms on Company as well as rolumns on the entire stock marta stock index, and book na si Company Mature on su wind portfolio that purchases stock in the markets in proportion to the size of the company rather than one of each sock) Then excess returns were computed (Expessura e re excess permite chly without being multipled by 100. These we given the accompanying table in chronological order Formule the regression wth come runs on Company as the responw and come one who must be now, wd Company Yos explanatory Complete para (a) through below. mclick the icon to view the table of data (Do any of these stur have linear parts over time? Umelots of each one to se. Do any months stand out an unusual Construct a time of Company X Excess Retum OB Corindo Mar Excess Retur OC Q OU G2 Construct a time lot of Index Excess Retur , OD 02 oo Construct a medio el Company YExcess Return OA OB oc mo whether the Domyoftheates are income Click to you ower Data were tracked on monthly performance of stock in Company X. The data included 200 monthly returns on Company X, as well as rolumns on the entire stock market a stock index, and stock na si Co on a value-weighted portfolio that purchases stock in the major markets in proportion to the size of the company rather than one of each stock.) Then excess retums were computed. (Excess retums are the only without being multiplied by 100.) These values are given in the accompanying table in chronological order Formulate the regression with excess retums on Company X as the response and excess stume Index, and Company Y as explanatory variables, Complete parts (a) through ( below. Click the icon to view the table of data. Do any of these variables have a patterns over time? Company X Excess Return Market Excess Reum Index Exco Rotum Company YExcess Retum Do ary months stand out as unusual? Select all that apply A Yes, the timeplot for Company Y Ex Return has at least one outler UB. Yes, the meclot for Index Excess Return has a least one outlet Yes. The implotto Market Excess Return has a lot one cutie D. Yes, the timelor for Company X Excess Return has that one outier DE Notre do not appear to be any others 6) the indicated miple regression Does the estimated multiple regression explain stately significant variation in the excess ruums on Company X Loty - Goa X Excess Ruum X-Manet Excess Return X, index Excess Return, and X-Company YExces Retur. Complete the multiple regression equation below. med Y.XXX Round to four decimal places as needed. Round of X, to the decimal places ned. Houndofur values to two decades as needed Does the multiple regression explain tally cont variation in the returns on CUMA 005 Click to select your Data were tracked on monthly performance of stock in Company X. The data included 200 monthly retums on Company X, as well as returns on the entire stock market, a stock indee on a value-weighted portfolio that purchases stock in the major markets in proportion to the size of the company rather than one of each stock) Then excess returns were computed, only without being multiplied by 100.) These values are given in the accompanying table in chronological order. Formulate the regression with excess robums on Company X as there index, and Company Y as explanatory variables. Complete parts () through ( below. DEL Click the icon to view the table of data Estimated Y-0.x, (XX (Round the constant to four decimal places as noeded. Round the coefficient of X, to three decimal places as needed. Round all other values to two decimal places as needed) Does the estimated multiple regression explain statistically significant variation in the excess returns on Company X? Use a 0,06 State the null and alternative hypotheses OA HOB2B-0 H: At least one is different from 0. OB. Ho: PB5 H:At least one is different from 0. OCH 1150 HEBBP3 OD. Ho: At least one is different from 0. HB "B" 0 Determine the test statistic (Round to two decimal places as noeded) Determine the p-value Click to select your answerte) Round to three decimal places as needed) State the appropriate conclusion the null hypothesis. There evidence to conclude that the multiple regression, taken as a whole statistically wgnificant variation in the excess ons on Company (c) Doon collinanity affect the estimated effects of these explanatory variables in the estimated equation? In particular, do the partial effects create a different sense of importance from what is suggested by magihai efecte? A. Yes. The marginal slope for Marvel Excess Ratum is negative and the partial slope is positive OB. Yes. The marginal kope for Index Excess Ratum is positive, and the partial slope is negative OG NO. The partial effects we similar to the marginal effects of all explanatory variables D. Yes. The marginal slope for Company YExcess Rotum is negative, and the partial slope is positive (a) Which explanatory variable has the West VIF? has the largest VIF, VIF Hound to the decimal plan ded) (e) How would you suggest improving this model or would you leave tas is? OA The model should be well as is since we are significant and there is not guarcolarity OB. The moon can be improved by removing Marka Excess Raumance that contratach the repression pustion OG The moon can be neroved by moving Index Eros Rebuince it is not want OD The modne motoved by removing Index Ecturn since lasciarlty that affects the regression on Click to see your own V) below inson Company X as the response and Click the icon to view the table of data. O A. Yes. The marginal slope for Market Excess Retum is negative, and the partial slope is positive. OB. Yes. The marginal slope for Index Excess Return is positive, and the partial slope is negative. OC. No. The partial effects are similar to the marginal effects of all explanatory variables. OD. Yes. The marginal slope for Company Y Excess Return is negative, and the partial slope is positive. (d) Which explanatory variable has the largest VIF? V has the largest VIF, with VF- (Round to one decimal place as needed.) (e) How would you suggest improving this model, or would you just leave it as is? O A. The model should be left as it is since all variables are significant and there is not significant collinearty. OB. The model can be improved by removing Market Excess Return since it has colinearity that affects the regression equation OC. The model can be improved by removing Index Excess Return since it is not significant OD. The model can be improved by removing Index Excess Rotun since has colinearity that affects the negrosion equation (Interpret nubstantively the fit of your model which might be the one the question starts with OA. The best model in the original model. In this model, all variables are significant and there is no significant collinearity OB. The best model is the one win index Exodus Retumn and Company Exons Return. In this model, both variables are graficant and there is no significant collinearly OC. The best model is the one with only Market Ecoss Return since this is the most significant variable OD. The best model in the one with Market Excess Return and Company YExcess Retum. In this model both variables are grificant and there is no significant coinearity Click to enot your answerts) MeBook Pro