Benrahou, Khaliss, Yacoubi Soussane.xlsx - these cases studies are done by Benrahou Kenza Khaliss Oumaima Yacoubi Soussane Zakaria Exxon Johnson

Benrahou, Khaliss, Yacoubi Soussane.xlsx - these cases...

This preview shows page 1 out of 110 pages.

Unformatted text preview: these cases studies are done by : Benrahou Kenza Khaliss Oumaima Yacoubi Soussane Zakaria Exxon Johnson & McDonald Mobil 's Month Microsof Caterpillar Johnson Sandisk Qualcomm 3-Jan -0.08201 -0.02261 -0.0304 -0.00186 -0.11443 -0.24867 0.0349 3-Feb 0.00211 0.00293 0.06867 -0.01781 -0.04424 0.09363 -0.08178 3-Mar 0.02152 0.02734 0.04681 0.10334 0.06245 0.00839 0.04251 3-Apr 0.05576 0.00715 0.07622 -0.02609 0.18257 0.43876 -0.11444 3-May -0.03717 0.04119 -0.00856 -0.03141 0.09532 0.50165 0.05395 3-Jun 0.04185 -0.01346 0.06731 -0.04876 0.17779 0.1164 0.07124 3-Jul 0.03003 -0.00919 0.21847 0.00174 0.04306 0.39734 0.04285 3-Aug 0.00417 0.06661 0.06462 -0.03804 -0.02564 0.06633 0.10459 3-Sep 0.04827 -0.02918 -0.04163 -0.00121 0.04996 0.05409 0.00823 3-Oct -0.05396 -0.00055 0.06987 0.01636 0.06202 0.26491 0.13967 3-Nov -0.01645 -0.00355 0.0378 -0.0157 0.0412 0.00273 -0.06043 3-Dec 0.06457 0.1326 0.09165 0.04787 -0.03121 -0.24276 0.21055 4-Jan 0.01023 -0.00512 -0.05444 0.03407 0.03665 -0.11275 0.08678 4-Feb -0.04051 0.03996 -0.03046 0.01367 0.09946 -0.06372 0.07763 4-Mar -0.06031 -0.01375 0.04383 -0.05917 0.00954 0.11566 0.05072 4-Apr 0.04813 0.02308 -0.01227 0.06526 -0.0469 -0.18371 -0.05778 4-May 0.00383 0.0228 -0.03062 0.03637 -0.03048 0.06479 0.07541 4-Jun 0.08883 0.02682 0.05428 -0.00018 -0.01515 -0.12008 0.08812 4-Jul -0.00245 0.04256 -0.06974 -0.00772 0.05769 0.12125 -0.05166 4-Aug -0.03896 0.00151 -0.01075 0.05636 -0.01745 -0.03988 0.10157 4-Sep 0.01282 0.04837 0.1066 -0.03046 0.03738 0.24711 0.02602 4-Oct 0.01157 0.01842 0.00622 0.03639 0.03996 -0.28331 0.06557 4-Nov 0.06864 0.04673 0.1367 0.03811 0.07341 0.08194 0.00048 4-Dec -0.00336 0.0002 0.0651 0.05139 0.04294 0.10585 0.02042 5-Jan -0.01647 0.00663 -0.08204 0.02018 0.01029 -0.01081 -0.1217 5-Feb -0.03957 0.23217 0.06678 0.01832 0.0213 0.08826 -0.03008 5-Mar -0.03935 -0.0586 -0.03798 0.02378 -0.05865 0.03423 0.01609 5-Apr 0.04675 -0.04312 -0.03259 0.02189 -0.05877 -0.14748 -0.0475 5-May 0.02292 -0.00947 0.06882 -0.01749 0.05561 0.09578 0.07079 5-Jun -0.03721 0.0226 0.01275 -0.0313 -0.1031 -0.08625 -0.1143 5-Jul 0.031 0.02227 0.1365 -0.016 0.12324 0.4252 0.196 5-Aug 0.07224 0.02451 0.02931 -0.00375 0.04107 0.14814 0.00811 5-Sep -0.06026 0.06077 0.05875 -0.00174 0.03205 0.24234 0.12692 5-Oct -0.00117 -0.11646 -0.1006 -0.01043 -0.05643 0.22056 -0.11151 5-Nov 0.08016 0.03883 0.09869 -0.00862 0.0924 -0.13281 0.14361 5-Dec -0.05527 -0.03205 -0.00017 -0.02672 -0.00384 0.23032 -0.05058 SUMMARY OUTPUT microsof Regression Statistics Multiple R 0.2659798 R Square 0.0707452 7.0745231 Adjusted R 0.0434142 Standard Er0.0443758 Observatio 36 ANOVA df Regression Residual Total SS MS F Significance F 1 0.0050972 0.00509722 2.58845896 0.1168925 34 0.0669531 0.00196921 35 0.0720503 CoefficientsStandard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Intercept 0.0003984 0.0079355 0.05020969 0.96024911 -0.015728 0.0165253 -0.01572838 S&P 500 0.4583448 0.2848864 1.60886885 0.11689251 -0.120614 1.0373036 -0.12061396 SUMMARY OUTPUT Exxon Mobil Regression Statistics Multiple R 0.3477463 R Square 0.1209275 Adjusted R 0.0950724 Standard Er0.0526437 Observatio 36 ANOVA df Regression Residual Total SS MS F Significance F 1 0.012962 0.01296202 4.67712822 0.0376909 34 0.0942264 0.00277136 35 0.1071884 CoefficientsStandard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Intercept 0.0092585 0.009414 0.98348493 0.33231598 -0.009873 0.02839 -0.00987301 S&P 500 0.7309071 0.3379656 2.16266692 0.0376909 0.0440783 1.417736 0.0440783 SUMMARY OUTPUT Caterpillar Regression Statistics Multiple R 0.5734138 R Square 0.3288034 Adjusted R 0.3090623 Standard Er0.0569916 Observatio 36 ANOVA df Regression Residual Total SS MS F Significance F 1 0.0540987 0.05409874 16.6557988 0.0002565 34 0.1104334 0.00324804 35 0.1645322 CoefficientsStandard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Intercept 0.0150229 0.0101915 1.47406424 0.14966385 -0.005689 0.0357345 -0.00568869 S&P 500 1.4932047 0.3658783 4.08115165 0.00025646 0.7496506 2.2367588 0.74965061 SUMMARY OUTPUT Johnson & Johnson Regression Statistics Multiple R 0.006613 R Square 4.37E-005 Adjusted R -0.029367 Standard Er0.0353742 Observatio 36 ANOVA df Regression Residual Total SS MS F Significance F 1 1.86E-006 1.861E-006 0.00148692 0.9694662 34 0.0425455 0.00125134 35 0.0425473 CoefficientsStandard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Intercept 0.0052072 0.0063258 0.82316382 0.41615138 -0.007648 0.0180627 -0.00764837 S&P 500 0.008757 0.2270978 0.03856064 0.96946621 -0.452761 0.4702753 -0.45276127 SUMMARY OUTPUT McDonald's Regression Statistics Multiple R 0.5812103 R Square 0.3378055 Adjusted R 0.3183292 Standard Er0.0562227 Observatio 36 ANOVA df Regression Residual Total SS MS F Significance F 1 0.0548255 0.05482551 17.3444284 0.0002015 34 0.1074735 0.00316099 35 0.1622991 CoefficientsStandard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Intercept 0.0092989 0.010054 0.92490069 0.36153591 -0.011133 0.0297311 -0.01113321 S&P 500 1.5032012 0.3609417 4.16466426 0.00020149 0.7696793 2.2367231 0.76967931 SUMMARY OUTPUT Sandisk Regression Statistics Multiple R 0.3509957 R Square 0.123198 Adjusted R 0.0974097 Standard Er0.1856369 Observatio 36 ANOVA df Regression Residual Total SS MS F Significance F 1 0.1646301 0.16463011 4.77728195 0.0358213 34 1.1716754 0.03446104 35 1.3363055 CoefficientsStandard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Intercept 0.0429654 0.0331964 1.29427919 0.20429125 -0.024498 0.1104286 -0.02449779 S&P 500 2.6048362 1.1917632 2.18569942 0.03582133 0.1828819 5.0267904 0.18288192 SUMMARY OUTPUT Qualcomm Regression Statistics Multiple R 0.4319323 R Square 0.1865655 Adjusted R 0.162641 Standard Er0.0788674 Observatio 36 ANOVA df Regression Residual Total SS MS F Significance F 1 0.0485045 0.04850454 7.79808019 0.0085239 34 0.2114821 0.00622006 35 0.2599866 CoefficientsStandard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Intercept 0.0140866 0.0141034 0.99880978 0.32494268 -0.014575 0.0427482 -0.01457495 S&P 500 1.4138944 0.5063177 2.79250429 0.00852391 0.384933 2.4428558 0.384933 SUMMARY OUTPUT Procter & Gamble Regression Statistics Multiple R 0.3598021 R Square 0.1294575 Adjusted R 0.1038533 Standard Er0.0350892 Observatio 36 ANOVA df Regression Residual Total SS MS F Significance F 1 0.0062254 0.00622536 5.05610729 0.0311312 34 0.0418627 0.00123126 35 0.048088 CoefficientsStandard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Intercept 0.0054753 0.0062748 0.87257872 0.38901358 -0.007277 0.0182272 -0.00727668 S&P 500 0.5065332 0.2252682 2.24857895 0.03113117 0.0487333 0.9643332 0.04873326 Procter & Gamble S&P 500 0.000465 -0.027415 -0.043356 -0.017004 0.087833 0.008358 0.013588 0.081044 0.021925 0.050899 -0.028752 0.011322 -0.009587 0.016224 -0.006601 0.017873 0.063352 -0.011944 0.063833 0.054962 -0.020857 0.007129 0.037822 0.050765 0.01657 0.017276 0.014147 0.012209 0.02312 -0.016359 0.013063 -0.016791 0.019574 0.012083 0.009831 0.017989 -0.037472 -0.034291 0.07325 0.002287 -0.033053 0.009364 -0.049704 0.014014 0.044939 0.038595 0.029918 0.032458 -0.029049 -0.02529 -0.00263 0.018903 -0.001695 -0.019118 0.026981 -0.020109 0.018467 0.029952 -0.043518 -0.000143 0.059905 0.035968 -0.002696 -0.011222 0.071738 0.006949 -0.053649 -0.017741 0.021432 0.035186 0.012065 -0.000952 Microsof 1Mean Standard Error Median Mode Standard Deviation Sample Variance Kurtosis Skewness Range Minimum Maximum Sum Count Confidence Level(95.0%) 0.005 0.008 0.004 #N/A 0.045 0.002 -0.940 0.024 0.171 -0.082 0.089 0.181 36.000 0.015 1-From the descriptive statistic we can notice three companies with the highe Comparing the standard deviation of different stocks and using it as a measur 2-Microsof= 0.0004 + 0.458*S&P 500 Exxon Mobil= 0.009 + 0.731*S&P 500 Caterpillar =0.015 + 1.493*S&P 500 Johnson & Johnson= 0.005+0.0087*S&P 500 McDonald’s= 0.009+1.503*S&P 500 SanDisk=0.043 +2.605*S&P 500 Qualcomm=0.014 +1.414*S&P 500 Procter & Gamble=0.0055 +0.5065*S&P 500 The stocks that are expected to perform best in an up market: All stocks with a beta higher than 1 will benefit from the market when it goes The stocks that are expected to hold their value best in down market are All the stocks with a beta lower than 1 will benefit from the market when it go ➢ Beta for each stock: Microsof: 0.458 Exxon Mobil: 0.731 Caterpillar :1.493 Johnson & Johnson: 0.0087 McDonald’s: 1.503 SanDisk: 2.605 Qualcomm: 1.414 Procter & Gamble: 0.5065 R square for each stock: 0.0707 0.1209 0.3288 0.0294 0.3378 0.1232 0.1866 0.1295 3-The return for individual stocks can be explained by using the R² ( R square Microsof: 7.07% of the returns are explained by the market. Exxon Mobil: 12.09% of the returns are explained by the market. Caterpillar: 32.88% of the returns are explained by the market. Johnson & Johnson: 2.94% of the returns are explained by the market. McDonald’s: 33.78% of the returns are explained by the market. SanDisk: 12.32% of the returns are explained by the market. Qualcomm: 18.66% of the returns are explained by the market. Procter & Gamble: 12.95% of the returns are explained by the market.   Microsof: 7.07% of the returns are explained by the market. Exxon Mobil: 12.09% of the returns are explained by the market. Caterpillar: 32.88% of the returns are explained by the market. Johnson & Johnson: 2.94% of the returns are explained by the market. McDonald’s: 33.78% of the returns are explained by the market. SanDisk: 12.32% of the returns are explained by the market. Qualcomm: 18.66% of the returns are explained by the market. Procter & Gamble: 12.95% of the returns are explained by the market.   Upper 95.0% 0.0165253 1.0373036 Upper 95.0% 0.02839 1.417736 Upper 95.0% 0.0357345 2.2367588 Upper 95.0% 0.0180627 0.4702753 Upper 95.0% 0.0297311 2.2367231 Upper 95.0% 0.1104286 5.0267904 Upper 95.0% 0.0427482 2.4428558 Upper 95.0% 0.0182272 0.9643332 Exxon Mobil Mean Standard Error Median Mode Standard Deviation Sample Variance Kurtosis Skewness Range Minimum Maximum Sum Count Confidence Level(95.0%) Caterpillar 0.0166 Mean 0.0092 Standard Error 0.0128 Median #N/A Mode 0.0553 Standard Deviation 0.0031 Sample Variance 6.5843 Kurtosis 1.5183 Skewness 0.3486 Range -0.1165 Minimum 0.2322 Maximum 0.5989 Sum 36.0000 Count 0.0187 Confidence Level(95.0%) 0.0301 0.0114 0.0408 #N/A 0.0686 0.0047 0.2857 0.3306 0.3191 -0.1006 0.2185 1.0835 36.0000 0.0232 can notice three companies with the highest mean return than the market; SanDisk, Caterpillar, and Qualcomm. Microsof and Johnson of different stocks and using it as a measure of volatility, SanDisk is the most volatile stock. Johnson and Johnson, P and G are less volati 00 00 *S&P 500 *S&P 500 o perform best in an up market: will benefit from the market when it goes up like; Caterpillar, McDonald’s, Qualcomm, and SanDisk is the stock that will benefit the mo o hold their value best in down market are: n 1 will benefit from the market when it goes down like, Microsof, Exxon Mobil, Procter & Gamble, and Johnson & Johnson is the one R square for each stock: 707 0.1209 288 0.0294 0.3378 0.1232 866 0.1295 an be explained by using the R² ( R square are shown above) explained by the market. are explained by the market. re explained by the market. eturns are explained by the market. are explained by the market. explained by the market. are explained by the market. eturns are explained by the market. explained by the market. are explained by the market. re explained by the market. eturns are explained by the market. are explained by the market. explained by the market. are explained by the market. eturns are explained by the market. Johnson & Johnson Mean Standard Error Median Mode Standard Deviation Sample Variance Kurtosis Skewness Range Minimum Maximum Sum Count Confidence Level(95.0%) McDonald's 0.005 Mean 0.006 Standard Error -0.001 Median #N/A Mode 0.035 Standard Deviation 0.001 Sample Variance 0.434 Kurtosis 0.596 Skewness 0.163 Range -0.059 Minimum 0.103 Maximum 0.191 Sum 36.000 Count 0.012 Confidence Level(95.0%) 0.0245 0.0113 0.0370 #N/A 0.0681 0.0046 0.2244 0.2101 0.2970 -0.1144 0.1826 0.8811 36.0000 0.0230 erpillar, and Qualcomm. Microsof and Johnson and Johnson has the lowest monthly mean returns. k. Johnson and Johnson, P and G are less volatile compared to the other individual stocks that are more volatile than the market. nd SanDisk is the stock that will benefit the most from an up market since it’s the one with the highest beta, 2.6. & Gamble, and Johnson & Johnson is the one that is expected to benefit the most from a down market since it has a beta equal to 0.00 Sandisk Mean Standard Error Median Mode Standard Deviation Sample Variance Kurtosis Skewness Range Minimum Maximum Sum Count Confidence Level(95.0%) Qualcomm 0.069 Mean 0.033 Standard Error 0.074 Median #N/A Mode 0.195 Standard Deviation 0.038 Sample Variance -0.193 Kurtosis 0.309 Skewness 0.785 Range -0.283 Minimum 0.502 Maximum 2.493 Sum 36.000 Count 0.066 Confidence Level(95.0%) y mean returns. tocks that are more volatile than the market. ne with the highest beta, 2.6. rom a down market since it has a beta equal to 0.0087. 0.0284 0.0144 0.0387 #N/A 0.0862 0.0074 -0.4891 0.0024 0.3323 -0.1217 0.2106 1.0210 36.0000 0.0292 Procter & Gamble Mean Standard Error Median Mode Standard Deviation Sample Variance Kurtosis Skewness Range Minimum Maximum Sum Count Confidence Level(95.0%) S&P 500 0.0106 Mean 0.0062 Standard Error 0.0133 Median #N/A Mode 0.0371 Standard Deviation 0.0014 Sample Variance -0.5558 Kurtosis 0.1563 Skewness 0.1415 Range -0.0536 Minimum 0.0878 Maximum 0.3812 Sum 36.0000 Count 0.0125 Confidence Level(95.0%) 0.0101 0.0044 0.0103 #N/A 0.0263 0.0007 0.1435 0.5312 0.1153 -0.0343 0.0810 0.3634 36.0000 0.0089 Percent Under Fatal Accidents 21 per 1000 Licenses 13 12 8 12 11 17 18 8 13 8 9 17 8 16 15 9 8 14 8 15 10 10 16 12 9 10 9 11 12 14 14 11 14 18 10 14 16 12 15 13 9 17 2.962 0.708 0.885 1.652 2.091 2.627 3.83 0.368 1.142 0.645 1.028 4.1 2.19 3.623 2.623 0.835 0.82 2.89 1.267 3.224 1.014 0.493 2.801 1.405 1.433 0.039 0.338 1.849 2.246 2.855 2.352 1.294 1.443 3.614 1.926 1.643 2.943 1.913 2.814 2.634 0.926 3.256 SUMMARY OUTPUT SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations ANOVA Regression Residual Total Intercept Percent Under 21 2- The regression equation is Price = –1.59741 + 0.28705 Percent u Interpretation: The constant, b0 = –1.59741 is the intercept; it ind The coefficient b 1 = 0.28705 indicates that when the drivers unde 3-From our analysis we can conclude that the percentage of drive the number of fatal accident is 69.72% explained by the variation Recommendations: more variables should be added to the model Regression Statistics 0.8393874795 0.7045713408 0.6971856243 0.5893502881 42 df SS 1 40 41 Coefficients -1.5974138789 0.2870531707 MS 33.1344176368 33.134418 13.8933504822 0.3473338 47.027768119 Standard Error t Stat F Significance F .39647813 3.79357E-012 P-value Lower 95% 0.3716714538 -4.297919 .000107273 -2.348589907 0.0293897693 9.7671121 3.7936E-012 0.2276542312 Upper 95% -0.8462378502 0.3464521102 –1.59741 + 0.28705 Percent under 21 59741 is the intercept; it indicated the average Fatal Accidents per 1000 Licenses when Percent under 21 i s that when the drivers under age 21 increases by 1%, the fatal Accidents per 1000 Licenses increase by 0. that the percentage of drivers under the age of 21 is a highly significant predictor of the number of fatal a % explained by the variation of the percentage of drivers under age of 21. ould be added to the model in order to cover the remaining 30% and therefore increase the R square. 4.5 4 3.5 3 2.5 Fatal Accidents per 2 1000 licences 1.5 1 0.5 0 6 8 10 12 14 Percent Under 21 Lower 95.0% Upper 95.0% -2.3485899075 -0.8462378502 0.2276542312 0.3464521102 Percent Under 21 Mean Standard Error Median Mode Standard Deviation Sample Variance Kurtosis Skewness Range Minimum Maximum Sum Count 12.261904762 0.4832376338 12 8 3.1317378002 9.8077816492 -1.1371094981 0.2103572731 10 8 18 515 42 16 when Percent under 21 is ‘0’. 00 Licenses increase by 0.28705. of the number of fatal accident since it has a high R square (0.6972). This means that the variation of ncrease the R square. nces 8 10 12 14 16 18 Percent Under 21 Fatal Accidents per 1000 Licenses Mean Standard Error Median Mode Standard Deviation Sample Variance Kurtosis Skewness Range Minimum Maximum Sum Count 1.9224047619 0.1652572839 1.881 #N/A 1.0709896053 1.1470187346 -0.9748887539 0.1931644044 4.061 0.039 4.1 80.741 42 20 (0.6972). This means that the variation of Observati on 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 Brand Canon Canon Canon Canon Canon Canon Canon Canon Canon Canon Canon Canon Canon Nikon Nikon Nikon Nikon Nikon Nikon Nikon Nikon Nikon Nikon Nikon Nikon Nikon Nikon Nikon Weight Price ($) Megapixel s (oz.) 330 10 7 200 12 5 300 12 7 200 10 6 180 12 5 200 12 7 200 14 5 130 10 7 130 12 5 110 16 5 90 14 5 100 10 6 90 12 7 270 16 5 300 16 7 200 14 6 400 14 7 120 14 5 170 16 6 150 12 5 230 14 6 180 12 6 130 12 6 80 12 7 80 14 7 100 12 4 110 12 5 130 14 4 Score 66 66 65 62 62 61 60 60 59 55 52 51 46 65 63 61 59 57 56 56 55 53 53 52 50 46 45 42 Question 3: simple linear regression between the overall score and the price of the camera SUMMARY OUTPUT Regression Statistics Multiple R 0.6832118 R Square 0.4667784 Adjusted R 0.4462699 Standard Er4.9823791 Observatio 28 ANOVA df Regression Residual SS MS F Significance F 1 565.00194 565.00194 22.760217 6.16E-005 26 645.42663 24.824101 Total 27 1210.4286 CoefficientsStandard Error t Stat P-value Lower 95% Upper 95%Lower 95.0% Intercept 46.668802 2.2384394 20.848812 9.39E-018 42.067624 51.26998 42.067624 Price ($) 0.0552492 0.0115808 4.770767 6.16E-005 0.0314446 0.0790538 0.0314446 Question 4: simple linear regression (Canon only) SUMMARY OUTPUT Regression Statistics Multiple R 0.826999 R Square 0.6839274 Adjusted R 0.6551935 Standard Er3.6185363 Observatio 13 ANOVA df Regression Residual Total SS MS F Significance F 1 311.66045 311.66045 23.80213 0.0004879 11 144.03186 13.093805 12 455.69231 CoefficientsStandard Error t Stat P-value Lower 95% Upper 95%Lower 95.0% Intercept 47.288021 2.5728889 18.379349 1.32E-009 41.625131 52.950911 41.625131 Price ($) 0.0664848 0.0136275 4.8787427 0.0004879 0.036491 0.0964787 0.036491 Price ($) Mean Standard Error Median Mode Standard Deviation Sample Variance Kurtosis Skewness Range Minimum Maximum Sum Count Megapixels Weight (oz.) 175.35714 Mean 15.647254 Standard Error 160 Median 200 Mode 82.797484 Standard Deviation 6855.4233 Sample Variance 0.6634436 Kurtosis 1.0569954 Skewness 320 Range 80 Minimum 400 Maximum 4910 Sum 28 Count 12.857143 Mean 0.3477604 Standard Error 12 Median 12 Mode 1.8401748 Standard Deviation 3.3862434 Sample Variance -0.63315 Kurtosis 0.2257311 Skewness 6 Range 10 Minimum 16 Maximum 360 Sum 28 Count Price 70 60 50 40 30 20 10 0 50 100 150 200 250 300 350 400 of the camera 450 Megapixels 70 60 50 40 30 20 10 0 9 10 11 12 13 14 20 10 0 9 10 11 12 13 14 Upper 95.0% 51.2699799613 0.0790538251 2- The linear relationship for the overall score and the price ($) of cameras is types of the cameras will be the best predictors for the overall scores of the c 3-The estimated regression equation that could be used to predict the overal + 0.0552x The p-value that correspond to the test statistic 22.76 is 0.000 which is less th reject the null hypothesis. We can conclude that there is no linear relationshi the camera. 4-The estimated regression equation that could be used to predict the overal + 0.0664x The p-value that correspond to the test statistic 23.8 is 0.000 which is less tha reject the null hypothesis. We can conclude that there is no linear relationshi Canon. Upper 95.0% 52.9509111326 0.0964786567 Score 5.8214286 Mean 0.1858313 Standard Error 6 Median 5 Mode 0.9833266 Standard Deviation 0.9669312 Sample V...
View Full Document

  • Left Quote Icon

    Student Picture

  • Left Quote Icon

    Student Picture

  • Left Quote Icon

    Student Picture