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Baruch College, CUNY *

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2000

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Industrial Engineering

Date

Jan 9, 2024

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xlsx

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93

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Gender Major Category Number F A Airbags and seatb 236 8.93% F M Body and glass 218 8.25% M C Brakes 121 4.58% M C Fuel/emission/ex 197 7.45% F C Interior electron 291 11.01% M A Powertrain 384 14.53% M A Steering 1042 39.42% M A Tires and wheels 154 5.83% F C Totals 2643 100.00% F A F C F M Business Analyt Percentage M C Already Use 9 M M Beyond the next 13 M M Don't know 15 F C No plans to use 9 M C Within the next f 54 M M M A Energy Source Percentage F C Solar 3.9 M A Other 5.4 M A Hydro 6.1 F C Wind 6.8 F A Nuclear 9.9 F C Coal 23.3 F M Natural gas 44.6 M M less than half re 32.1 M M F A Technology Frequency F M Wearable techno 6 6.67% M A Blockchain techn 5 5.56%
F A Artificial Intellig 18 20.00% F M IoT: retail insura 27 30.00% F A IoT: commercial i 2 2.22% F M Social media 32 35.56% F M 90 100.00% F A M M M A M C Category Successful Not Successful Total Film and Video 15,281 22,000 37,281 Games 4,696 8,242 12,938 Music 17,750 18,633 36,383 Technology 2,184 6,795 8,979 Total 39,911 55,670 95,581 Category Successful Not Successful Total Film and Video 15.99% 23.02% 39.00% Games 4.91% 8.62% 13.54% Music 18.57% 19.49% 38.07% Technology 2.28% 7.11% 9.39% Total 41.76% 58.24% 100.00% Category Successful Not Successful Total Film and Video 40.99% 59.01% 100.00% Games 36.30% 63.70% 100.00% Music 48.79% 51.21% 100.00% Technology 24.32% 75.68% 100.00% Total 41.76% 58.24% 100.00%
Category Successful Not Successful Total Film and Video 38.29% 39.52% 39.00% Games 11.77% 14.81% 13.54% Music 44.47% 33.47% 38.07% Technology 5.47% 12.21% 9.39% Total 100.00% 100.00% 100.00% 100 10% Average Dwell Ti Frequency Less than 1 day 157 253 43.32% Between 1 and le 96 Between 2 and le 130 Between 8 and le 69 199 34.08% Between 31 and l 48 22.60% 90 days or more 84 132 584 al width = (highest value - lowest value)/ number of c 202.76 frequency distrib 8 26.67% 232.99 7 23.33% 243.28 6 20.00% 259.38 4 13.33% 265.76 1 3.33% 267.59 3 10.00% 271.67 1 3.33% 291.49 30 302.46 313.64 324.42 interval width 90.71 331.59
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362.57 366.73 385.44 422.54 449.63 461.62 477.66 489.67 494.17 507.17 525.62 566.59 579.74 610.78 740.08 746.03 793.87 837.73 percentage distribution 89 class interval = 7 3 10.00% 95 5 16.67% 98 5 16.67% 102 7 23.33% 108 6 20.00% 112 2 6.67% 115 2 6.67% 116 30 121 122 127 127 cumulative percentage distribution
133 80 0.00% 144 100 10.00% 144 120 26.67% 145 140 43.33% 147 160 66.67% 149 180 86.67% 153 200 93.33% 157 220 100.00% 163 164 170 City Annual_Time 170 1 40 174 2 53 177 3 47 181 4 74 186 5 37 204 6 31 206 7 24 8 46 9 28 10 70 11 32 12 34 13 22 14 44 15 36 16 17 17 19 18 46 19 62 20 53 21 23
22 35 23 34 24 25 25 23 26 37 27 36 28 46 29 39 30 64 31 43 10 12 14 25 6 18 Row1 Mean 13 Standard Error 1.64685190959 Median 11.5 Mode 10 Standard Deviati 5.7048623599 Sample Variance 32.5454545455 Kurtosis 0.36870364013 Skewness 0.98710267855 Range 19 Minimum 6
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Maximum 25 Sum 156 Count 12 practice problems for the quiz no of days (x) frequency x(f) mean 1 100 100 2 200 400 mode = 600 high 3 300 900 sample mean (m 4 600 2400 5 400 2000 8 200 1600 1800 7400 sample size 1800 third quartile 450
Automaker Number A 291 9.33% B 477 15.30% C 476 15.27% D 555 17.80% E 571 18.31% F 250 8.02% Other 498 15.97% Totals 3118 100.00% Machine LanguagPercentage Self-Learning Ro Percentage Already Use 4 Already Use 5 Beyond the next 14 Beyond the next 12 Don't know 18 Don't know 11 No plans to use 12 No plans to use 47 Within the next f 52 Within the next f 25 Overloaded Male Female Total Yes 495 662 1157 No 285 78 363 Total 780 740 1520 Overloaded Male Female Total Yes 32.57% 43.55% 76.12% No 18.75% 5.13% 23.88% Total 51.32% 48.68% 100.00% Overloaded Male Female Total Yes 42.78% 57.22% 100.00% No 78.51% 21.49% 100.00% Construct contingency tables based o
Total 51.32% 48.68% 100.00% Overloaded Male Female Total Yes 63.46% 89.46% 76.12% No 36.54% 10.54% 23.88% Total 100.00% 100.00% 100.00%
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13 11 7 9 10 21
hest frequency 4.11111111111
on total percentages, row percentages, and column perce
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Overloaded Male Female Total Yes 239 277 516 No 301 264 565 Total 540 541 1081 p 0.477 feels overloaded p 0.256 female yes 1.000 Money Market cd Five-Year CD 5.35 595.00% 5.32 588.00% 5.3 584.00% 5.29 5.83 5.25 5.79 Money Market Five-Year CD Mean 5.302 Mean 5.858 Standard Error 0.016552945357247 Standard Error 0.0270924344 Median 5.3 Median 5.84 Mode #N/A Mode #N/A Standard Deviation 0.037013511046644 Standard Deviation 0.0606 Sample Variance 0.0014 Sample Variance 0.00367 Kurtosis 0.48857158079807 Kurtosis 0.7214397612 Skewness -0.21298200186674 Skewness 0.8379416131 Range 0.1 Range 0.16 Minimum 5.25 Minimum 5.79 Maximum 5.35 Maximum 5.95 Sum 26.51 Sum 29.29 Count 5 Count 5 0.6981% 1.0342% y x Monthly Rent ($) Size (Square Feet) 900 850 1,450 1,200 800 950 1,600 1,100 500 1000 1500 2000 2500 f(x) = 1.10277777777778 x + 88.0555555555553 R² = 0.777797517704247 Size (Square Feet)
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2,000 1,900 900 750 1,800 1,350 1,400 1,100 SUMMARY OUTPUT Regression Statistics Multiple R 0.881928295103545 R Square 0.777797517704247 Adjusted R Square 0.740763770654954 Standard Error 228.284 Observations 8 ANOVA df SS MS F ignificance Regression 1 1094506.94 1094506.94444444 21.002398614 0.003759 Residual 6 312680.556 52113.4259259259 Total 7 1407187.5 Coefficients Standard Error t Stat P-value Lower 95% Intercept 88.0555555555554 288.256765 0.305476111268312 0.7703240632 -617.2833 Size (Square Feet) 1.10277777777778 0.2406321 4.58283739774106 0.0037592726 0.513972 33489 21469 x 12 n 12020 sse r2 0.641 Average Room Price (US$) 179 173 143.00000 min 171 154.75000 q1 169 167.00000 median 165 171.50000 q3 143 179.00000 154 155 16.75 600 800 1000 1200 1400 1600 0 500 This chart isn't available in your version of Excel. Editing this shape or saving this workbook into a different file format will permanently break the chart.
Average Room Price (US$) Mean 163.625 Standard Error 4.22973107892216 Median 167 Mode #N/A Standard Deviation 11.9634861140054 Sample Variance 143.125 Kurtosis -0.50690440140893 Skewness -0.58840926541189 Range 36 Minimum 143 Maximum 179 Sum 1309 Count 8 Increase use of social B2B B2C Total Yes 564 779 1343 No 436 221 657 Total 1000 1000 2000 yes yes yes yes
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F Upper 95%Lower 95.0% Upper 95.0% 793.3944 -617.2833 793.3944 1.691583 0.513972 1.691583 0 1800 2000
202.76 232.99 243.28 start end Frequency Percentages 259.38 200 300 8 26.67% 265.76 300 400 7 23.33% 267.59 400 500 6 20.00% 271.67 500 600 4 13.33% 291.49 600 700 1 3.33% 302.46 700 800 3 10.00% 313.64 800 900 1 3.33% 324.42 30 331.59 362.57 366.73 385.44 422.54 449.63 461.62 477.66 15 start end frequency 489.67 13 5 9 9 494.17 18 10 14 14 507.17 21 15 19 18 525.62 7 20 24 6 566.59 15 25 29 3 579.74 14 30 34 0 610.78 5 740.08 14 746.03 18 793.87 15 837.73 21 6 16 10 17 11 7 19 17 16 15 12 26 8 16 12
9 5 17 24 12 11 13 25 9 10 12 15 23 18 19 12 24 29 17 23 9 15 11
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Brand Advertised Time of Ad (sec.) Company 2 30 Company 3 30 Company 4 30 Company 5 30 Company 6 30 Company 10 30 Company 14 30 Company 15 30 Company 16 30 Company 18 30 Company 20 30 Company 21 30 Company 23 30 Company 24 30 Company 25 30 Company 26 30 Company 28 30 Company 29 30 Company 30 30 Company 31 30 Company 33 30 Company 35 30 Company 36 30 Company 37 30 Company 38 30 Company 39 30 Company 40 30 Company 41 30 Company 42 30 Company 43 30 Company 44 30 Company 45 30 Company 47 30 Company 48 30 Company 49 30 Company 50 30 Company 51 30 Company 52 30 Company 53 30 Company 54 30 Company 1 60 Company 7 60 Company 8 60 Company 9 60
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Company 11 60 Company 12 60 Company 13 60 Company 17 60 Company 19 60 Company 22 60 Company 27 60 Company 32 60 Company 34 60 Company 46 60 Company 55 60 Company 56 60 Company 57 60 n Average Room Price (US$) 1 143 2 151 3 152 4 164 5 170 6 172 7 174 8 175 Q1 = (n+1)/4 Q3 = 3(n+1)/4 The five-number summary for a data set consists of the smallest value, the first quartile, the median, the thir The median is the middle value in an ordered array of data that has been ranked from smallest to largest. Ha the values are larger than or equal to the median. To compute the median for a set of data, first rank the valu
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median (n+1)/2 Product Calories x Sugar y A 280 6.3 B 320 5.3 C 330 18.7 D 370 19.1 E 390 11.9 F 430 21.5 G 620 19.2 covariance 427.048 correlation wrong Calories Calories 10783.6734693878 Sugar 366.041 Calories x Calories x 1 Sugar y 0.569 Product Calories Sugar A 240 11 B 250 4.4 C 370 20.3 D 490 31 E 540 18.6 F 580 28 G 610 19.9 1042.667 0.739 Product Calories Sugar A 260 10.7 the values are larger than or equal to the median. To compute the median for a set of data, first rank the valu compute the rank of the value that is the median.
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B 320 4.3 C 430 25 D 470 28.9 E 490 16.7 F 520 25.4 G 540 21.4 covariance 687.9523809524 correlaton 0.738 Carrier Download Speed Upload Speed Carrier 1 22.9 13.9 Carrier 2 22.8 12.6 Carrier 3 20.8 9 Carrier 4 17.4 10.9 Carrier 5 10.7 6.2 Carrier 6 10.7 6 Carrier 7 10.1 5.9 Carrier 8 6.9 3 Carrier 9 4.6 3.8 covariance 25.679 correlaton 0.951 Type Market_Cap Risk Growth Small Low Growth Small Low 9 13 Column1 7 11 Mean 8.7 14 Standard Error 1.04403065089106 4 Median 8 7 Mode 7 5 Standard Deviatio 3.30 7 Sample Variance 10.9 10 Kurtosis -0.918393112652853 Skewness 0.283902670195589 Range 10
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Minimum 4 Maximum 14 Sum 87 Count 10 XI xi-mean)^2 4 16 6 36 15 225 Column1 13 169 11 121 Column1 12 144 11 121 Mean 9.6 8 64 Standard Error 1.0770329614269 9 81 Median 10 7 49 Mode 11 96 1026 Standard Deviation 3.40587727318528 variance 102.6 Sample Variance 11.6 Standard deviaton 10.12916580968 Kurtosis -0.7245484400657 formula is correct Skewness -0.0987137437977 for population standard deviation and variation Range 11 Minimum 4 Maximum 15 Sum 96 Count 10 3.136 Summated_Rating Cost_($_per_person) 55 37 66 50 67 60 69 57 65 42 SUMMARY OUTPUT 60 38 52 42 Regression Statistics 64 42 Multiple R 0.8079682746259 54 35 R Square 0.6528127328019 51 35 Adjusted R Square 0.6377176342281 51 43 Standard Error 7.72903272732229 50 39 Observations 25 76 83 61 53 ANOVA 55 39 df
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49 36 Regression 1 54 30 Residual 23 47 26 Total 24 57 40 58 45 Coefficients 66 64 Intercept b0, y intrcept -32.12 66 61 Summated_Rating b1, slope 1.29 51 27 71 45 68 48 y b0+b1x1 y 19.67 Monthly Rent ($) Size (Square Feet) 900 850 1,450 1,300 800 1,050 1,600 1,100 1,900 1,900 900 700 1,700 1,250 1,300 1,100 SUMMARY OUTPUT Regression Statistics 45 50 55 60 0 10 20 30 40 50 60 70 80 90 f(x) = 1.29460763960972 x − 32.1161251816484 R² = 0.652812732801903 Cost_($ 600 800 1000 1200 14 0 200 400 600 800 1000 1200 1400 1600 1800 2000 f(x) = 0.705652513675436 x + 225.670747 R² = 0.66214501543012 Chart Title
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Multiple R 0.813722935298 R Square, coefficient of r2 0.66214501543 Adjusted R Square 0.605835851335 Standard Error 259.9103697435 Observations 8 ANOVA df SS MS Regression 1 794367.098198822 794367.098198822 Residual 6 405320.401801178 67553.4003001963 Total 7 1199687.5 Coefficients Standard Error t Stat Intercept, bo, y 233.8 329.467364123337 0.7095980549822 Size (Square Feet), b1, x 0.9 0.273637241858344 3.42915421771939 mean for 1000 1172.13 for 1200 1359.80 acuals-predicted for 1000 1200.00 102.87 65.20 this is far closer to actual rent so this is better Wine Alcohol Content (%Quality Rating y 1 10 8 2 10.8 9 3 10.4 6 4 11.9 7 5 11.7 8 6 11.8 7 7 12.5 8 8 12.3 8 9 12.4 8 10 13.2 8 11 13.5 9 12 13.8 7 7.88 Summated_Rating Cost_($_per_person) 52 37 66 49 69 61 9.5 10 0 1 2 3 4 5 6 7 8 9 10 f(x) = R² = 0 90 100 Cost_($_per_per
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68 61 63 44 60 41 53 46 63 43 52 32 47 34 54 41 50 36 76 88 63 54 56 35 51 37 54 33 46 26 mean 46.555 54 42 58 43 66 62 64 61 55 27 71 48 65 48 Program Per-Year Tuition ($) Mean Starting Salary Upon Graduation ($) 64661 156658 67026 152365 65764 149351 67030 140891 67131 138747 67153 151350 66485 150045 67835 152600 63491 137999 64316 146174 65425 144802 60385 146425 60255 138776 56432 136015 55436 122617 55249 114671 55706 129109 50829 130017 54386 129457 49697 119818 72759 40 45 50 55 60 0 10 20 30 40 50 60 70 80 f(x) = 1.45207794265195 x − 40.57068173 R² = 0.709954113776952 20000 30000 40000 50000 0 20000 40000 60000 80000 100000 120000 140000 160000 180000 200000 f(x) = 2.43867068597158 x − 14306.7786625478 R² = 0.751217810843386 Chart Title
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44792 114527 48179 110087 50917 109113 45990 110947 37397 79908 47970 78874 47324 100156 49694 75068 37964 87265 34592 72745 43004 76165 41374 49616 50074 63668 34379 102065 23113 57819 42083 77757 39712 55929 Tear_Rating y Plate_Gap x 0.06 -1.01 0.16 0.11 0.37 0.12 0.75 0.09 0.37 0.42 0.29 -0.95 0.69 -0.22 1.81 0.07 0.25 0.43 0.09 0.04 0.11 -0.26 3.87 0.79 0.01 -0.84 0.54 0.16 0.07 -0.73 0.03 -0.25 0.44 -0.79 4.25 0.32 0.01 -0.73 3.9 4.9 Chart Title
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Annual_Revenue_(millions_of_do Franchise_Value_(millions_of_dollars) 215 363 280 644 225 531 151 156 226 521 251 581 233 533 207 338 181 278 187 346 193 414 189 258 193 338 267 635 368 263 641 -1.5 -1 -0.5 0 0.5 1 -1.1 -0.1 0.9 1.9 2.9 f(x) = 1.2734025872207 x + 0.962267913511729 R² = 0.298161632080108 200 300 400 500 600 700 f(x) = 4.07515642016699 x − 447.472339077637 R² = 0.91586108557563 Chart Title
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Monthly Rent ($) y Size (Square Feet) x 875 750 1,450 1,200 850 950 1,450 1,100 1,900 2,000 900 700 1,800 1,300 1,400 1,050 1212.8 62.2 1388.58 36.4200000000001 Online Trailer Views (millions) x y Opening Weekend Box Office Gross ($millions) 59.804 30.876 10.15 9.748 11.937 1.057 8.663 25.405 83.436 100.864 35.133 64.309 20.566 15.962 5.115 10.602 2.604 5.938 48.337 39.412 140 160 180 200 220 240 260 280 300 0 100 200 600 800 1000 1200 1400 1600 1800 2000 2200 0 200 400 600 800 1000 1200 1400 1600 1800 2000 f(x) = 0.878873618970628 x + 333.899218539477 R² = 0.74535430235265 Chart Title 140 160 180 200
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4.896 18.664 25.554 12.041 5.999 4.257 56.542 148.26 8.226 15.034 9.954 7.386 12.089 5.638 1.687 0.932 55.1033 0.704 3.416 6.554 1.028 8.755 3.725 9.668 4.73 30.551 99.437 1.984 2.72 1.711 9.211 7.404 9.48 53.814 46.536 2.185 0.431 31.878 20.266 6.091 7.326 13.749 9.33 61.085 42.882 78.895 178.345 6.001 1.769 33.11 64.571 3.867 15.22 42.005 89.101 4.989 4.69 6.63 33.377 0.942 3.705 2.258 1.513 11.327 18.47 8.966 12.202 15.177 4.357 13.714 30.436 31.231 53.003 52.612 46.607 16.235 13.003 6.884 3.776 11.698 18.223 2.827 3.471 23.075 13.602 12.606 40.011 0.826 1.385 27.536 20.13 0 10 20 30 0 20 40 60 80 100 120 f(x) = 1.39960628904371 x − 0.88068981424714 R² = 0.643335555579867
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7.273 3.404 3.323 1.207 4.267 10.951 3.79 8.344 7.597 11.614 12.912 13.501 7.067 5.106 5.02 1.985 7.739 22.8 16.795 13.689 7.643 2.08 If SSR = 49, SSE=21 determine SST, then compute the coefficient of determinaton r2 and interpret SST = SSR + SSE 70 r2 = regression sum of squares/total sum of squares ssr/sst 0.7 40 r2 0.7 r2 0.25 r2 0.660 standard error sst=ssr +sse sse = sst-ssr 10120 syx standard error 31.8119 Program Per-Year Tuition ($) Mean Starting Salary Upon Graduation ($) 63583 153646 66844 153714 65561 145517 69038 145764 66749 142268 67149 154771 SUMMARY OUTPUT 66069 148355 69037 150718 Regression Statistics 64651 136890 Multiple R 0.8781835209563 62059 142518 R Square 0.7712062964793 67628 147494 Adjusted R Square 0.7646693335215 61468 146020 Standard Error 15937.53 60998 139404 Observations 37 56152 137168
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54959 124235 ANOVA 56824 118677 df 54974 125932 Regression 1 50793 127374 Residual 35 52577 130982 Total 36 50914 123967 47100 115715 Coefficients 48675 111936 Intercept y -12854.823246705 50649 112862 Program Per-Year Tuition ($) x 2.41737681995088 44552 105499 37885 80028 46740 78129 47930 103939 50045 76626 36833 83289 32744 72477 41867 73033 42741 54064 48580 63251 33963 102090 22750 55379 41563 80145 37726 51101 Program Per-Year Tuition ($) Mean Starting Salary Upon Graduation ($) 62441 154789 67393 153955 66830 149768 68174 140608 65420 140467 65644 156046 66227 146100 68679 149146 63255 135812 64271 143513 20000 30000 40000 50000 0 20000 40000 60000 80000 100000 120000 140000 160000 180000 f(x) = 2.41737681995088 x − 12854.82324670 R² = 0.771206296479284 Chart Tit
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65898 144891 SUMMARY OUTPUT 61007 143383 61108 143053 Regression Statistics 56254 136514 Multiple R 0.8566289157611 53826 123684 R Square 0.733813099318 56655 114911 Adjusted R Square 0.7262077592985 55518 127878 Standard Error 16945.71 51114 132301 Observations 37 54116 131489 49680 120642 ANOVA 46241 115712 df 46987 111132 Regression 1 48781 109984 Residual 35 46802 108285 Total 36 37462 82609 46980 77752 Coefficients 48133 100060 Intercept -12606.155236988 50649 74407 Program Per-Year Tuition ($) 2.41075545616218 39136 88752 34374 74023 44122 74971 41959 51643 48458 64721 33258 103252 23289 57314 42501 80307 40348 53772 Bag Plate gap (X) Tear rating (Y) 1 0.3 0.01 2 -0.3 0.09 3 2.1 0.48 4 1.8 0.84 5 0 0.33 6 0 0.34 7 -0.3 0.72 8 0 1.96 9 0.3 0.21 10 -1.8 0.16 11 -1.8 0.12 12 2.4 3.76 13 -2.1 0.04 14 0 0.57
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15 -2.7 0.02 16 -1.5 0.07 17 1.8 0.44 18 1.8 4.25 19 -0.3 0.01 SUMMARY OUTPUT Regression Statistics Multiple R 0.5771 R Square 0.3331 Adjusted R Square 0.2939 Standard Error 1.0367 Observations 19.0000 ANOVA df SS MS Regression 1 9.12483224739166 9.12483224739166 Residual 17 18.2695466999768 1.07467921764569 Total 18 27.3943789473684 Coefficients Standard Error t Stat Intercept 0.766417615617 0.237841620591134 3.22238645074882 Plate gap (X) 0.473115655744 0.162365704075006 2.91388910262413 Annual_Revenue_(millions_of_do Francise_Value_(millions_of_dollars) 243 583 214 459 233 453 257 687 274 597 267 738 239 560 264 705 199 286 225 449 184 328 240 601 187 316 232 465 242 602 SUMMARY OUTPUT
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Regression Statistics Multiple R 0.9239 R Square 0.8536 Adjusted R Square 0.8423 Standard Error 56.5470 Observations 15.0000 ANOVA df SS MS Regression 1 242308.585661658 242308.585661658 Residual 13 41568.3476716751 3197.56520551347 Total 14 283876.933333333 Coefficients Standard Error t Stat Intercept -586.5086551596 128.166541538503 -4.5761448200065 Annual_Revenue_(millions_of_do 4.75046566497 0.545709465584845 8.70511868413064 Monthly Rent ($) Size (Square Feet) 875 900 1,550 1,250 825 950 1,600 1,150 1,900 1,900 900 650 1,800 1,350 1,300 950 SUMMARY OUTPUT Regression Statistics Multiple R 0.854 R Square 0.729 Adjusted R Square 0.684 Standard Error 243.381 Observations 8.000 ANOVA df SS MS Regression 1 958032.767745953 958032.767745953 Residual 6 355404.732254047 59234.1220423412
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Total 7 1313437.5 Coefficients Standard Error t Stat Intercept 232.4564134496 289.415491979277 0.8031927104518 Size (Square Feet) 0.97696139477 0.242925552284068 4.02164937193265 Online Trailer Views (millions) Opening Weekend Box Office Gross ($millions) 60.291 28.906 8.777 7.433 8.823 2.642 9.323 26.836 86.371 102.982 34.817 61.382 21.798 21.024 3.347 10.727 2.013 5.748 45.346 33.592 9.259 20.675 27.949 17.075 1.744 1.051 55.151 149.684 7.868 10.493 SUMMARY OUTPUT 10.381 7.342 13.701 4.298 Regression Statistics 1.228 6.393 Multiple R 0.802 3.802 2.78 R Square 0.644 7.315 2.247 Adjusted R Square 0.638 8.992 7.87 Standard Error 20.164 10.502 0.014 Observations 66.000 32.597 99.047 2.625 1.798 ANOVA 0.729 11.958 df 10.352 15.363 Regression 1 53.325 52.257 Residual 64 4.85 4.007 Total 65 32.593 17.199 6.012 6.569 Coefficients 12.685 11.122 Intercept -0.4795365337089 58.003 38.765 Online Trailer Views (millions) 1.37570136675068 78.206 170.765 3.64 1.505 36.561 62.77 2.12 18.226
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44.429 91.886 4.989 4.69 6.63 33.377 0.942 3.705 2.258 1.513 11.327 18.47 8.966 12.202 15.177 4.357 13.714 30.436 31.231 53.003 52.612 46.607 16.235 13.003 6.884 3.776 11.698 18.223 2.827 3.471 23.075 13.602 12.606 40.011 0.826 1.385 27.536 20.13 7.273 3.404 3.323 1.207 4.267 10.951 3.79 8.344 7.597 11.614 12.912 13.501 7.067 5.106 5.02 1.985 7.739 22.8 16.795 13.689 7.643 2.08
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Score 6.59 6.56 6.47 6.25 6.22 5.87 5.61 5.56 5.38 30 5.28 Column1 5.31 5.27 Mean 4.90 5.2 Standard Error 0.13 5.2 Median 4.82 5.1 Mode 4.71 5.07 Standard Deviation 0.81 5.04 Sample Variance 0.66 4.96 Kurtosis -0.27 4.93 Skewness 0.42 4.88 Range 3.03 4.75 Minimum 3.56 4.71 Maximum 6.59 4.71 Sum 196.01 4.71 Count 40.00 4.66 4.57 16.55% 4.48 4.44 4.41 4.39 4.39 4.19 4.17 4.13 4.13 3.91 0.48 3.81 60 3.57 Column1 3.57 3.56 Mean 5.10 6.96 Standard Error 0.25 6.11 Median 5.29 6.05 Mode 5.61 6.06 Standard Deviation 1.05
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5.7 Sample Variance 1.10 5.61 Kurtosis -0.30 5.61 Skewness -0.47 5.36 Range 3.71 5.29 Minimum 3.25 5.18 Maximum 6.96 5.09 Sum 86.78 4.78 Count 17.00 4.75 4.18 coefficient of variance 20.53% 3.48 standard deviation/mean *100% 3.32 3.25 n 8 2.25 q1 6.75 q3 143 min 23 151.75 q1 167 median 172.50 q3 175 rd quartile, and the largest value. alf the values are smaller than or equal to the median, and half ues from smallest to largest. Then, use the following formula to five number summary consists of smallest value, the first quartile, the median, the third quartile, and the largest value This chart isn't available in your ve Editing this shape or saving this w
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Correlaton Sugar 38.3563265306 Sugar y 1 4 Column1 ues from smallest to largest. Then, use the following formula to 200 250 300 350 400 450 500 0 5 10 15 20 25 6.3 5.3 18.7 19.1 11.9 21.5 Chart Title
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10 7 Mean 6.60 3 Standard Error 1.36 9 Median 7.00 Mode #N/A Standard Deviation 3.05 Sample Variance 9.30 Kurtosis -2.50 Skewness -0.16 Range 7.00 Minimum 3.00 Maximum 10.00 Sum 33.00 Count 5.00 coef = standard dev/ mean 46.21% z = (x- mean)/ standard deviation 4 -0.85 10 1.11 7 0.13 3 -1.18 Note use subtotal to average the toal in list 3YrReturn% 29.81 24.76
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SS MS F Significance F
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2583.4672213 2583.4672213 43.246669 1.03900940116943E-06 1373.9727787 59.73794690002 3957.44 Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% 11.7797177858 -2.72639173245 0.0120349 -56.4843280277575 -7.7479 -56.48433 0.19686197324 6.576219969198 1.039E-06 0.887367620460554 1.70185 0.887368 65 70 75 80 _per_person) 400 1600 1800 2000 7590518 e
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F Significance F 11.7590986489 0.013985685881 P-value Lower 95% Upper 95% Lower 95.0% pper 95.0% 0.50458173802 -572.388197077 1039.967 -572.388197076852 1040 0.01398568588 0.268778092026 1.6079105 0.268778092025838 1.60791 10.5 11 11.5 12 12.5 13 13.5 14 14.5 = 0.133333333333333 x + 6.14666666666667 0.033535353535353 Quality Rating y rson)
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65 70 75 80 341712 60000 70000 80000
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Chart Title
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40 50 60 70 80 90
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t its meaning Lower 95.0% -37139.1 This is the one we are using in regression 1.965556
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SS MS F Significance F 29966520349.6 29966520349.58 117.97624 9.33407641734556E-13 8890164932.15 254004712.347 38856685281.7 Standard Error t Stat P-value Lower 95% Upper 95% 11962.06 -1.07463269706 0.2898921 -37139.1007126797 11429 0.22 10.86168669284 9.334E-13 1.9655560312896 2.8692 60000 70000 80000 05 tle
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Lower 95.0% -39374.64 1.912515 SS MS F Significance F 27706804844.8 27706804844.77 96.48656 1.35087348882024E-11 10050499938.3 287157141.0947 37757304783.1 Standard Error t Stat P-value Lower 95% Upper 95% 13185.7456519 -0.95604416844 0.3456072 -39374.6420247838 14162 0.24542553475 9.822757271853 1.351E-11 1.91251513227149 2.909
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F Significance F 8.49074970239 0.009673474437 P-value Lower 95% Upper 95% Lower 95.0% pper 95.0% 0.00500068333 0.264615659437 1.2682196 0.264615659436715 1.26822 0.00967347444 0.130553963981 0.8156773 0.130553963980732 0.81568
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F Significance F 75.7790913048 8.76531872E-07 P-value Lower 95% Upper 95% Lower 95.0% pper 95.0% 0.00051977572 -863.395634307 -309.6217 -863.395634306587 -309.62 8.7653187E-07 3.571532039985 5.9293993 3.57153203998502 5.9294 F Significance F 16.1736636708 0.006945814472
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P-value Lower 95% Upper 95% Lower 95.0% pper 95.0% 0.45249861152 -475.717783779 940.63061 -475.717783779481 940.631 0.00694581447 0.38254398194 1.5713788 0.382543981939792 1.57138 64.4% Lower 95.0% -7.146006 1.12007 SS MS F Significance F 46993.703651 46993.70365101 115.58273 5.6306625358755E-16 26021.1617052 406.5806516445 73014.8653563 Standard Error t Stat P-value Lower 95% Upper 95% 3.33702270294 -0.14370190928 0.8861874 -7.14600574449597 6.18693 0.12796101775 10.75094111426 5.631E-16 1.12006984699486 1.63133
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ersion of Excel. workbook into a different file format will permanently break the chart.
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550 600 650 19.2
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Upper 95.0% -7.74792233553915 1.70184765875888
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Upper 95.0% 11429.4542192697 2.86919760861215
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Upper 95.0% 14162.3315508079 2.90899578005287
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Upper 95.0% 6.18693267707815 1.6313328865065
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BASIC PROBABILITY Table_Data B B` A 20 40 P A' 40 80 F sum probabillity of occurrence = X/T T 180 A 0.3333333333 p(p) A' 0.6666666667 p(p|s) A and B 0.1111111111 p(p|s') A OR B 0.5555555556 p(F or S') p(F n S') warranty domestic foreign total yes 0.012 0.038 0.05 no 0.638 0.312 0.95 total 0.65 0.35 1 Increase use of social mediaB2B B2C Total Yes 569 762 1331 No 431 238 669 Total 1000 1000 2000 p(b2b) p(a|b)/p(b) The probability that he or she plans to increase use of social media is p(anb) 0.2845 p(b) 0.5 now total 0.569 b2c p(anb) 0.381 p(b) 0.5 now total 0.762 PAY FULL AMOUNT EACH Generation X Boomers Total Are the two events, increase use of social media and business focus, independent? Ex Use the contingency table to the right to determine the probability of events. a. What is the probability of event A? b. What is the probability of event A ? ′ c. What is the probability of event A and B? d. What is the probability of event A or B?
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Yes 410 580 990 No 590 420 1,010 Total 1,000 1,000 2,000 p(anb) | generation x 0.205 p(b) 0.5 total P(a|b) 0.410 p(anb) | Boomers 0.29 p(b) 0.5 total P(a|b) 0.58 JUSTIFIED? Professor Technology LeadeTotal Yes 708 240 948 No 1,430 1,898 3,328 Total 2,138 2,138 4,276 p(anb) 0.166 0.056 p(b) 0.500 0.500 p(a|b) 0.331 0.112 p(anb) , non justifiable 0.334 0.444 p(b) 0.500 0.500 p(a|b), non justifiable 0.669 0.888 cmts Overloaded with informatioMale Female Total yes 133 214 347 no 120 33 153 Total 253 247 500 suppose chosen is female, did not feel overloaded=? Given female P(not overloaded with info 0.1336 p(male|overloaded with inf 0.3833 p(male n overloaded/ total overloaded) IS feeling overloaded with information and the gender independent ? P(male|overloaded) 0.3833 p(Male) 0.5060 Warranty reapirs needed orcountry x not country x Total yes 0.015 0.035 0.05 no 0.385 0.565 0.95 Total 0.4 0.6 1
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car needs warranty repair p(repair|countryx) 0.038 0.058 are need for warranty repair and location of car manufactor independent based on country x 0.0375 0.05 Annual Performance First Week Higher Lower total Higher 43 7 50 Lower 15 15 30 total 58 22 80 select random year and find probability of market finished higher for the year. empirical approach P = X/T 0.7250 higher/total given market finished highe 0.7414 Die 1 rolls a 1 Die 1 rolls a 2 Die 1 rolls Die 1 rolls Die 1 rolls Die 1 rolls a Die 2 rolls a 1 2 3 4 5 6 7 Die 2 rolls a 2 3 4 5 6 7 8 Die 2 rolls a 3 4 5 6 7 8 9 Die 2 rolls a 4 5 6 7 8 9 10 Die 2 rolls a 5 6 7 8 9 10 11 Die 2 rolls a 6 7 8 9 10 11 12
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S S' sum 0.055 0.275 0.33 0.295 0.375 0.67 0.35 0.65 1 0.33 0.157143 0.423077 0.945 0.375
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a 6
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Discrete Probability Variable Distributon A: xi Distributon A: P(X=xi) mean(u) / xi tmes p(x=xi) 0 0.56 0 1 0.19 0.19 2 0.13 0.26 3 0.08 0.24 4 0.04 0.16 0.85 st deviation A 1.161 st deviation B 1.161 A distribution is symmetric when the lower and upper portions are mirror i Distribution A: xi Distribution A: P(X=xi) 0 0.56 1 0.19 2 0.13 3 0.08 4 0.04 a b (a-mean)square Accidents_Daily_(X) P(X=xi) [ x- E(x) ]^2 0 0.32 1.8769 1 0.33 0.1369 2 0.18 0.3969 3 0.07 2.6569 4 0.05 6.9169 5 0.03 13.1769 6 0.02 21.4369 mean 1.37 st dev 1.440 If most of the values are in the lower portion, a distribution is right-skewed A distribution is left-skewed if most of the values are in the upper portion.
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Outcome Prize Probability( x= xI) Win a car 20,000 3.133421069E-05 Win a gas card 100 3.133421069E-05 Win a gift card 5 0.99993733157862 mean 5.63 st dev 111.925 Outcome Prize variables xi Probability( x= xI) Win a car 22,000 3.15079715167938E-05 Win a gas card 75 3.15079715167938E-05 Win a gift card 5 0.999936984056966 mean 5.70 123.461 Die 1 rolls a 1 Die 1 rolls a 2 Die 2 rolls a 1 2 3 Die 2 rolls a 2 3 4 Die 2 rolls a 3 4 5 Die 2 rolls a 4 5 6 Die 2 rolls a 5 6 7 Die 2 rolls a 6 7 8 probability("<7",wins 5) x 15 T 36 x 21 p = x/t t 36 wins (p is ">7", wins x 15 t 36 loses 7 or under x 21 t 36 win if 7 x 6 t 36 loses otherwise 30 30/36 Frequency Arrivals x Frequency xf 0 17 0
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1 35 35 2 47 94 3 40 120 4 26 104 5 21 105 6 9 54 7 4 28 8 1 8 36 200 548 Mean of frequency xf/frequency mean, u 2.74 Explanaton Finding the mean average from a data collecton th Find the sum of the values and divide it by the tot Arrivals Frequency fx 0.00 27.00 0 1.00 56 56 2.00 34 68 3.00 30 90 4.00 22 88 5.00 17 85 6.00 9 54 7.00 4 28 8.00 1 8 36 200 477 mean 2.385 standard deviation 1.851 Probability of Occurrence p= x/t p(x>2) 0.415 Number_Approved x Frequency f fx 0 12 0 1 24 24 2 32 64 3 17 51 4 9 36 5 7 35
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6 2 12 7 1 7 28 104 229 expected numebr of mortgage mean=? mean 2.202 deviation 1.522 Probability more than 1 commercial mortagge 68 0.654 Probability x=xi Economic_condition Stock_X x 0.1 Recession -50 0.3 Slow_growth 30 0.4 Moderate_growth 90 0.2 Fast_growth 170 expected return for stock x is mean stock x 74 mean stock y 101 st deviation x 63.75 st deviation y 97.51 Probability x = xi Economic_Condition Corporate_Bond_Fund 0.02 Extreme_recession -250 0.08 Recession -90 0.15 Stagnation 30 0.3 Slow_growth 70 0.3 Moderate_growth 90 0.15 High_growth 110 mean Corporate_Bond_Fund 56.800 Common_Stock_Fund 65.300 deviation 67.24 common stock deviation 236.37 between 30 and 40 gallons
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-0.38 0.45 z<-0.38 z<0.45 0.3520 0.6736 to find the probability between -0.38 0.3216 QN 7 chapter 6 probability that someone consumed le so gallons is z and consumption is the value inside the z x<30, 30 mean, u 34.6 st deviation lets say is a 12 z=(x-u)/st dev -0.38 z< -0.38 0.352 95% people consumed less than ? Gall 0.9500 then, z based on 0.9500 is 1.65 x= za + u 54.400 qn 8 mean, u 60 st deviation, a 11 a. between 47 to 60 thousand miles a year (Z<z<z) z= (x-u)/a , 47 miles -1.18 z= (x-u)/a , 60 miles 0.00 z< -1.18 0.119 z<0 0.5 between, subtract and find 0.3810 either less than 50 or greater than 80 50 -0.91 to find z with x present 80 1.82 to find z with x present z<-0.91 z<1.82 0.1814 0.9656 between 0.7842 greather than or less than, compleme 21.58% miles ? Meaning now we have to find the x Given trucks 0.85 first, we can find z miles travelled ba 1.04 now we can find x? by formula x= za + 71 to find Z value in excel we use =norm. -1.04 x 48.6 miles 48599 if st dev 7 mean
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between 47 to 60 x 47 x1 60 z -1.86 z1 0.00 z < -1.86 0.0314 z1 < 0 0.5 between 0.4686 either less or greater 50 and 80 -1.43 2.86 Z 0.0764 0.9979 \between 0.9215 greater or less 7.85% number of miles travelled by 85% truc 85.00% so we use 15% while using Z -1.04 miles now to find x 52.7449662735435 1000 52745 no 6 mean 21 st deviaton 5 p(x>25), greater than, X= 25 z 0.8000 p(x<25) 0.7881 p(x>25), greater than 0.2119 x p(9<x<20) ? 9 p(x<9), Z=?, since we have x -2.400 0.0082 p(x<20) z=?, since we have x -0.200 0.4207 between = 0.4125 between which two Xs will the middle 95% the area of left shaded part 0.025 The are of right shaded part 0.025 To find value of Z with an area of 0.025 Z -1.96 There fore the shaded part falls on -1 -1.96 1.96 now convert z to x x 11.20 x1 30.80 mean 78 st deviaton 7 p(x<87), below than, X= 87
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Z 1.29 1. p(x<87) 0.9015 Between 71 and 97, p(71<x<97) x 71 p(x<71), Z=?, since we have x -1.00 0.1587 p(x<97) z=?, since we have x 2.71 0.9966 between = 0.8379 probability that 5% student taking tes 0.05 since higher is the area is right of Z, t 0.95 Z 1.64 X 90 z for exam result with exam 1 exam 2 grade 92 64 mean 78 61 deviation 7 3 Z 2.00 1.00 percentages of people who got score 0.9772 0.8413 percentages of people who got score 98% 84% -1.64485362695147 112.102927460971 0.0401 between 95-170 minutes / probability = ? 95 170 0.0401 0.9772 therefore, subtract eachother 0.9372 p( x> 170) = ? 170 p(x<170) => 0.9772 p(x>170) => 0.0228 P(5%< ?)=> X=? Now we solve for Z -1.64485362695147 X 97.10 no.12 mean 18.49
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st deviation 4 X 13 P(x>13), first find p(x<13) 0.0850 P(x>13), 0.9150 P(6<x<8) between ? x 6 x 8 p(x<6) 0.000896610527529 p(x<8) 0.00436436339046 now, p(6< X < 8) 0.0035 p(x<8) 0.0044 p(97%< x), X=?, Z= 1.88 X 26.01 no.13 shafts mean 21.003 st deviation 0.005 Given, x 20.992 x1 21 P(20.992< x < 21) Zx 0.0139 Zx1 0.2743 now to find between 0.2603 en diameter of 20.992 and 21.008mm x 20.992 x1 21.008 Zx 0.0139 Zx1 0.8413 now to find between 0.8274 find the right of probability, X>1% ? Z 2.33 X 21.0146 mean 9 deviation 1.4 x%<7yrs = ?, x= 7 z 0.0766 in percentge 7.66% x%>10.5 yrs, Z 0.858 x>10.5 0.1420 in percentge 14.20% x 7.8
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x1 12 mean 9 dev 1.4 78.89% zx 0.1974 zx1 0.9839 en zx and zx1 the middle shaded part 0.7866 78.66% give the 17th percentile = ? x such that 17% OF DISTRIBUTION IS SMALL chapter 7 qn 1 probability that x bar <93 ? xbar 93 mean 100 st deviation 15 sample, n 9 To find the Z -1.40 based on cumulative table, Z 0.0808 qn no 4 1 1 2 2 5 5 7 7 9 9 10 10 population size N 6 sample size n 2 To find possible number of sample 30 mean 2.1 st dev 0.045 x 2.06 sample 4 then new st dev a. p(x<2.06) 0.187 Assuming that you sample without replacement, select all possible sampl and construct the sampling distribution of the mean. Compute the mean o
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b. p(xbar<2.06) given the sample 0.038 sample 25 then new st dev c. p(xbar<2.06) 0.00 explain the difference in standard eror in part a and c a. p(x<2.06), st deviation= ? stdev/1 c. p(xbar<2.06) stdev/sqrt(25) stdev/5 b. p(xbar<2.06) , st deviation stdev/2 larger sample size than c mean 42.25 st dev 0.045 x 42.215 p(x<42.215) 0.218 n 4.000 new st dev/ std error p(xbar<42.215) 0.060 n 25.000 new st dev/ std error p(xbar<42.215) 0.000 no 7, p(xbar<2.55)= ? xbar 2.55 mean 2.57 st dev 0.04 n 11 therefore new st dev p(xbar<2.55)= ? 0.0486 p(2.56<Xbar<2.58)= ? xbar 2.56 p(xbar<2.56) 0.2035 xbar 2.58 p(Xbar<2.58)= ? 0.7965 p(2.56<Xbar<2.58)= ? 0.5930 between two values, 65% 0.65 find lower bound and upper bound =? since they are in the interval to find the lower and upper bound we need to subtract the midd 0.350 now to find two of them divide now to find z value -0.3800 Xbar 42.24 Xbar 42.25 Die 1 rolls a 1 Die 1 rolls a 2 Die 2 rolls a 1 2 3 Die 2 rolls a 2 3 4
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Die 2 rolls a 3 4 5 Die 2 rolls a 4 5 6 Die 2 rolls a 5 6 7 Die 2 rolls a 6 7 8 probability("<7",wins 5) x 15 T 36 x 21 p = x/t t 36 wins (p is ">7", wins x 15 t 36 loses 7 or under x 21 t 36 win if 7 x 6 t 36 loses otherwise 30 30/36 20 0.166666666666667 -5 0.833333333333333 mean -0.83
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[ xi- E(x) ]^2 [ xi- E(x) ]^2 tmes P(X=x Distributon B: xi Distributon B: P(X=xi) 0.7225 0.4046 0 0.04 0.0225 0.004275 1 0.08 1.3225 0.171925 2 0.13 4.6225 0.3698 3 0.19 9.9225 0.3969 4 0.56 1.3475 0.85 images of each other. Distribution B: xi Distribution B: P(X=xi) 0 0.04 1 0.08 2 0.13 3 0.19 4 0.56 (a-mean)square tmes b [ x- E(x) ]^2 tmes P(X=xi) 0.600608 0.045177 0.071442 0.185983 0.345845 0.395307 0.428738 2.0731 variance d.
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[ xi- E(x) ]^2 [ xi- E(x) ]^2 tmes P(X=xi) 399774851.5196080000000 12526.6294260789 8905.7904600298100 0.279055914635566 0.3962756546059 0.396250820636206 12527.3047328141 [ xi- E(x) ]^2 [ xi- E(x) ]^2 tmes P(X=xi) 483749442.6063430000000 15241.9636589055 4803.1520607515900 0.15133757832099 0.4833355641139 0.483305106267491 15242.5983015901 Die 1 rolls a 3 Die 1 rolls a 4 Die 1 rolls a 5 Die 1 rolls a 6 4 5 6 7 5 6 7 8 6 7 8 9 7 8 9 10 8 9 10 11 9 10 11 12 method 1 method 2 method 3 x2 x2f 0 0
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1 35 4 188 9 360 16 416 25 525 36 324 49 196 64 64 204 2108 hat has been structured into a frequency table is referred to as "mean from a frequency table." tal number of values to determine the mean. x2 x2F 0.00 0 1.00 56 4.00 136 9.00 270 16.00 352 25.00 425 36.00 324 49.00 196 64.00 64 204 1823 x2 x2F 0.00 0 1.00 24 4.00 128 9.00 153 16.00 144 25.00 175
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36.00 72 49.00 49 140.00 745 X stock Y stock Stock_Y x (x- mean)^2 (x- mean)^2 times probability (x- mean)^2 -140 15376 1537.6 58081 50 1936 580.8 2601 150 256 102.4 2401 200 9216 1843.2 9801 4064 Corportate bond common stock Common_Stock_Fund (x- mean)^2 (x- mean)^2 times probability (x- mean)^2 -985 94126.24 1882.5248 1103130.09 -400 21550.24 1724.0192 216504.09 -50 718.24 107.736 13294.09 90 174.24 52.272 610.09 150 1102.24 330.672 7174.09 350 2830.24 424.536 81054.09 4521.76
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table 60
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g excel Z formula x1 20
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x1 97
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LER AND 83% IS LARGER 0.0225 les of n = 2 of all the sample means and also compute the population mean. Are they equal? What is
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0.009 0.0225 0.009 0.012060453783111 dle part 65% Die 1 rolls a 3 Die 1 rolls a 4 Die 1 rolls a 5 Die 1 rolls a 6 4 5 6 7 5 6 7 8
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6 7 8 9 7 8 9 10 8 9 10 11 9 10 11 12 method 1 method 2 method 3
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mean(u) / xi tmes p(x=x [ xi- E(x) ]^2 [ xi- E(x) ]^2 tmes P(X=xi) 0 9.9225 0.3969 0.08 4.6225 0.3698 0.26 1.3225 0.171925 0.57 0.0225 0.004275 2.24 0.7225 0.4046 3.15 1.3475
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(x- mean)^2 times probability 5808.1 780.3 960.4 1960.2 9509 (x- mean)^2 times probability 22062.6018 17320.3272 1994.1135 183.027 2152.227 12158.1135 55870.41 2
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s this property called?
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chapter 1 Ino to business statistics chapter 2 Defining and collecting data chapter 3 Descriptives statistics- summary measures section 1-4 &section 2.5 chapter 12 Simple Linear Regression & Correlation section 1-3 Exam 1 91 chapter 4 Basic Probability section 1&2 kinda not there yet chapter 5 Discrete Probability Distribution section 5.1 kina chapter 6 The Normal Distributions section 1&2 chapter 7 Sampling Distributions & Central Limit Theorem Exam 2 chapter 8 Statistical Inference: Estimating U and P chapter 9 Hypothesis Testing (one-sample tests) chapter 10 Two- sample Tests Sections 1-3(testing only) Exam 3 chapter 11 Chi-square (X2) Tests Final
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