08 Aug Statistics Excel File
MGMT 650 Summer 2019 Week 11 Homework Answers (Last updated 3/18/2019)
Chi Square
| Saeko has a yarn shop and wants to test her theory on what types of colors she is selling. | ||
| She believes that Black, White, the Primary Colors, and Tertiary colors sell in equal amounts. | ||
| Test Saeko’s theory using the 5 step hypothesis testing analysis and Chi Square at the .10 level of significance. | ||
| (optional) | Use the “Pivot Table Data” tab to create a pivot table that shows Saeko the number of yards that were sold in the various yarn types during the busiest weekend of her shop last year. | |
| The pivot table should contain Color Type, Sum of Yards and Count of Color Type as Column Titles. | ||
| Here is the pivot table that you should have created. It is optional so that you can practice your pivot table skills. | ||
| Color Type | Sum of Yards | Count of Color Type |
| Black | 19,762.00 | 23 |
| Blue | 8,127.00 | 20 |
| Brown | 8,027.00 | 13 |
| Green | 6,533.00 | 12 |
| Purple | 7,243.00 | 12 |
| Red | 5,194.00 | 10 |
| White | 17,649.00 | 26 |
| Yellow | 7,229.00 | 14 |
| Grand Total | 79,764.00 | 130 |
| 1) | Using the pivot table that you just created, fill in the blanks in the following table: | |
| Primary Colors consists of the sum of Blue, Red, and Yellow yarn sold | ||
| Tertiary Colors consists of the sum of Brown, Green, and Purple Colors Sold. | ||
| The Total in this chart must equal the Grand Total, Cell D16 in the above table. | ||
| Color Type | Sum of Yards | |
| Black | ||
| White | ||
| Primary Colors | ||
| Tertiary Colors | ||
| Total | ||
| This table represents the observed data in the Chi Square analysis. | ||
| Find the Expected values for each of the colors. Saeko expects that the colors sell in equal amounts. | ||
| Color Type | Sum of Yards | |
| Black | ||
| White | ||
| Primary Colors | ||
| Tertiary Colors | ||
| Total | ||
| Subtract the Expected values from the observed values | ||
| Color Type | Sum of Yards | |
| Black | ||
| White | ||
| Primary Colors | ||
| Tertiary Colors | ||
| Square the values just found | ||
| Color Type | Sum of Yards | |
| Black | ||
| White | ||
| Primary Colors | ||
| Tertiary Colors | ||
| Divide each square by the expected value and add together | ||
| Color Type | Sum of Yards | |
| Black | ||
| White | ||
| Primary Colors | ||
| Tertiary Colors | ||
| Total | ||
| This total is your Chi Square test statistic | ||
| 2) | Use the 5 step hypothesis testing procedure to determine if Saeko’s hypothesis that the colors sell in equal amounts is true. | |
| What is the null hypothesis? | ||
| What is the alternative hypothesis? | ||
| What is the level of significance? | ||
| What is the Chi Square test statistic? | – 0 | |
| 3) | What is the Chi Square critical Value? | Use =CHISQ.INV() |
| 4) | What is your answer to Saeko? |
Pivot Table Data
| Customer | Color Name | Color Type | Yards | Meters |
| 1 | Coriander | White | 1,067.00 | 975.66 |
| 2 | Black | Black | 917.00 | 838.50 |
| 3 | Daffodil | Yellow | 762.00 | 696.77 |
| 4 | Black | Black | 735.00 | 672.08 |
| 5 | Opal | Blue | 551.00 | 503.83 |
| 6 | Toffee | Brown | 709.00 | 648.31 |
| 7 | Ruby | Red | 792.00 | 724.20 |
| 8 | Ash | Blue | 830.00 | 758.95 |
| 9 | Black | Black | 670.00 | 612.65 |
| 10 | Ash | Blue | 292.00 | 267.00 |
| 11 | Black | Black | 744.00 | 680.31 |
| 12 | Whirlpool | Blue | 943.00 | 862.28 |
| 13 | Verde | Green | 947.00 | 865.94 |
| 14 | Regal | Purple | 945.00 | 864.11 |
| 15 | Lynx | Brown | 774.00 | 707.75 |
| 16 | Yellow Rose | Yellow | 801.00 | 732.43 |
| 17 | Chocolate | Brown | 750.00 | 685.80 |
| 18 | Mist | White | 629.00 | 575.16 |
| 19 | Whirlpool | Blue | 113.00 | 103.33 |
| 20 | Alfalfa | Green | 344.00 | 314.55 |
| 21 | Ruby | Red | 162.00 | 148.13 |
| 22 | Verde | Green | 964.00 | 881.48 |
| 23 | Sky | White | 517.00 | 472.74 |
| 24 | Black | Black | 1,223.00 | 1,118.31 |
| 25 | Whirlpool | Blue | 200.00 | 182.88 |
| 26 | Black | Black | 879.00 | 803.76 |
| 27 | Mist | White | 999.00 | 913.49 |
| 28 | Alfalfa | Green | 598.00 | 546.81 |
| 29 | Jade | Green | 662.00 | 605.33 |
| 30 | Yellow Rose | Yellow | 368.00 | 336.50 |
| 31 | Cream | White | 529.00 | 483.72 |
| 32 | Black | Black | 1,100.00 | 1,005.84 |
| 33 | Ruby | Red | 870.00 | 795.53 |
| 34 | Mist | White | 342.00 | 312.72 |
| 35 | Yellow Rose | Yellow | 747.00 | 683.06 |
| 36 | Black | Black | 1,160.00 | 1,060.70 |
| 37 | Sky | White | 628.00 | 574.24 |
| 38 | Periwinkle | Purple | 185.00 | 169.16 |
| 39 | Coriander | White | 978.00 | 894.28 |
| 40 | Black | Black | 607.00 | 555.04 |
| 41 | Yellow Rose | Yellow | 387.00 | 353.87 |
| 42 | Black | Black | 255.00 | 233.17 |
| 43 | Periwinkle | Purple | 742.00 | 678.48 |
| 44 | Black | Black | 414.00 | 378.56 |
| 45 | Blush | Red | 345.00 | 315.47 |
| 46 | Black | Black | 892.00 | 815.64 |
| 47 | Mist | White | 727.00 | 664.77 |
| 48 | Coriander | White | 584.00 | 534.01 |
| 49 | Cream | White | 321.00 | 293.52 |
| 50 | Verde | Green | 478.00 | 437.08 |
| 51 | Black | Black | 931.00 | 851.31 |
| 52 | Daffodil | Yellow | 539.00 | 492.86 |
| 53 | Chocolate | Brown | 767.00 | 701.34 |
| 54 | Daffodil | Yellow | 369.00 | 337.41 |
| 55 | Regal | Purple | 378.00 | 345.64 |
| 56 | Daffodil | Yellow | 376.00 | 343.81 |
| 57 | Coriander | White | 957.00 | 875.08 |
| 58 | Black | Black | 929.00 | 849.48 |
| 59 | Black | Black | 959.00 | 876.91 |
| 60 | Lynx | Brown | 994.00 | 908.91 |
| 61 | Periwinkle | Purple | 714.00 | 652.88 |
| 62 | Daffodil | Yellow | 912.00 | 833.93 |
| 63 | Coriander | White | 776.00 | 709.57 |
| 64 | Verde | Green | 895.00 | 818.39 |
| 65 | Lynx | Brown | 706.00 | 645.57 |
| 66 | Alfalfa | Green | 105.00 | 96.01 |
| 67 | Cream | White | 165.00 | 150.88 |
| 68 | Daffodil | Yellow | 505.00 | 461.77 |
| 69 | Periwinkle | Purple | 661.00 | 604.42 |
| 70 | Cream | White | 226.00 | 206.65 |
| 71 | Black | Black | 472.00 | 431.60 |
| 72 | Opal | Blue | 184.00 | 168.25 |
| 73 | Cream | White | 191.00 | 174.65 |
| 74 | Cream | White | 238.00 | 217.63 |
| 75 | Coriander | White | 894.00 | 817.47 |
| 76 | Mist | White | 488.00 | 446.23 |
| 77 | Verde | Green | 400.00 | 365.76 |
| 78 | Black | Black | 1,618.00 | 1,479.50 |
| 79 | Blush | Red | 374.00 | 341.99 |
| 80 | Opal | Blue | 359.00 | 328.27 |
| 81 | Daffodil | Yellow | 553.00 | 505.66 |
| 82 | Mist | White | 439.00 | 401.42 |
| 83 | Alfalfa | Green | 448.00 | 409.65 |
| 84 | Jade | Green | 290.00 | 265.18 |
| 85 | Ruby | Red | 907.00 | 829.36 |
| 86 | Verde | Green | 402.00 | 367.59 |
| 87 | Cream | White | 123.00 | 112.47 |
| 88 | Black | Black | 981.00 | 897.03 |
| 89 | Chocolate | Brown | 247.00 | 225.86 |
| 90 | Regal | Purple | 805.00 | 736.09 |
| 91 | Sky | White | 358.00 | 327.36 |
| 92 | Regal | Purple | 461.00 | 421.54 |
| 93 | Yellow Rose | Yellow | 540.00 | 493.78 |
| 94 | Lynx | Brown | 964.00 | 881.48 |
| 95 | Regal | Purple | 317.00 | 289.86 |
| 96 | Ash | Blue | 190.00 | 173.74 |
| 97 | Periwinkle | Purple | 423.00 | 386.79 |
| 98 | Black | Black | 1,761.00 | 1,610.26 |
| 99 | Black | Black | 912.00 | 833.93 |
| 100 | Blush | Red | 258.00 | 235.92 |
| 101 | Regal | Purple | 968.00 | 885.14 |
| 102 | Sapphire | Blue | 517.00 | 472.74 |
| 103 | Whirlpool | Blue | 362.00 | 331.01 |
| 104 | Yellow Rose | Yellow | 125.00 | 114.30 |
| 105 | Whirlpool | Blue | 504.00 | 460.86 |
| 106 | Sapphire | Blue | 427.00 | 390.45 |
| 107 | Chocolate | Brown | 557.00 | 509.32 |
| 108 | Toffee | Brown | 498.00 | 455.37 |
| 109 | Sapphire | Blue | 216.00 | 197.51 |
| 110 | Whirlpool | Blue | 174.00 | 159.11 |
| 111 | Black | Black | 470.00 | 429.77 |
| 112 | Regal | Purple | 644.00 | 588.87 |
| 113 | Opal | Blue | 436.00 | 398.68 |
| 114 | Blush | Red | 768.00 | 702.26 |
| 115 | Coriander | White | 1,456.00 | 1,331.37 |
| 116 | Whirlpool | Blue | 385.00 | 352.04 |
| 117 | Ash | Blue | 869.00 | 794.61 |
| 118 | Blush | Red | 416.00 | 380.39 |
| 119 | Black | Black | 634.00 | 579.73 |
| 120 | Ruby | Red | 302.00 | 276.15 |
| 121 | Mist | White | 892.00 | 815.64 |
| 122 | Chocolate | Brown | 240.00 | 219.46 |
| 123 | Opal | Blue | 523.00 | 478.23 |
| 124 | Toffee | Brown | 710.00 | 649.22 |
| 125 | Yellow Rose | Yellow | 245.00 | 224.03 |
| 126 | Sky | White | 736.00 | 673.00 |
| 127 | White | White | 2,389.00 | 2,184.50 |
| 128 | Black | Black | 499.00 | 456.29 |
| 129 | Chocolate | Brown | 111.00 | 101.50 |
| 130 | Ash | Blue | 52.00 | 47.55 |
ANOVA
| Saeko owns a yarn shop and want to expands her color selection. | |||
| Before she expands her colors, she wants to find out if her customers prefer one brand | |||
| over another brand. Specifically, she is interested in three different types of bison yarn. | |||
| As an experiment, she randomly selected 21 different days and recorded the sales of each brand. | |||
| At the .01 significance level, can she conclude that there is a difference in preference between the brands? | |||
| Misa’s Bison | Yak-et-ty-Yaks | Buffalo Yarns | |
| 343 | 365 | 360 | |
| 308 | 368 | 346 | |
| 349 | 351 | 381 | |
| 304 | 339 | 306 | |
| 348 | 366 | 314 | |
| 346 | 331 | 307 | |
| Total | 1,998.00 | 2,120.00 | 2,014.00 |
| 5) | What is the null hypothesis? | ||
| What is the alternative hypothesis? | |||
| What is the level of significance? | |||
| 6) | Use Tools – Data Analysis – ANOVA:Single Factor | ||
| to find the F statistic: | |||
| 7) | From the ANOVA ooutput: What is the F value? | ||
| 8) | What is the F critical value? | ||
| 9) | What is your decision? |
Regression
| Studies have shown that the frequency with which shoppers browse Internet retailers is related to the frequency with which they actually purchase products and/or services online. The following data show respondents age and answer to the question “How many minutes do you browse online retailers per week?” | |
| Age (X) | Time (Y) |
| 13 | 5662 |
| 19 | 4549 |
| 16 | 3772 |
| 44 | 1872 |
| 32 | 2799 |
| 52 | 1355 |
| 39 | 1966 |
| 15 | 5682 |
| 40 | 1602 |
| 53 | 1186 |
| 48 | 1832 |
| 37 | 2253 |
| 36 | 2241 |
| 42 | 1001 |
| 30 | 2474 |
| 42 | 1943 |
| 28 | 3021 |
| 11 | 5682 |
| 32 | 2192 |
| 39 | 1784 |
| 23 | 2707 |
| 37 | 1801 |
| 17 | 4827 |
| 11 | 2693 |
| 18 | 4340 |
| 50 | 1399 |
| 52 | 1593 |
| 9 | 9154 |
| 41 | 1504 |
| 26 | 2627 |
| 30 | 2575 |
| 32 | 2711 |
| 53 | 2368 |
| 10) | Use Data > Data Analysis > Correlation to compute the correlation checking the Labels checkbox. |
| 11) | Use the Excel function =CORREL to compute the correlation. If answers for #1 and 2 do not agree, there is an error. |
| The strength of the correlation motivates further examination. | |
| 12) | a) Insert Scatter (X, Y) plot linked to the data on this sheet with Age on the horizontal (X) axis. |
| b) Add to your chart: the chart name, vertical axis label, and horizontal axis label. | |
| c) Complete the chart by adding Trendline and checking boxes | |
| Read directly from the chart: | |
| 13) | a) Intercept = |
| b) Slope = | |
| c) R2 = | |
| Perform Data > Data Analysis > Regression. | |
| 14) | Highlight the Y-intercept with yellow. Highlight the X variable in blue. Highlight the total standard error in orange |
| SUMMARY OUTPUT | |
| Use Excel to predict the number of minutes spent by a 37-year old shopper. Enter = followed by the regression formula. | |
| 15) | Enter the intercept and slope into the formula by clicking on the cells in the regression output with the results. |
| 16) | Is it appropriate to use this data to predict the amount of time that a 68-year-old will be on the Internet? |
| If yes, what is the amount of time, if no, why? |
Cleaning Data with Outlier
| 17) | On this worksheet, make an XY scatter plot linked to the following data: |
| X | Y |
| 92 | 22 |
| 87 | 23 |
| 102 | 23 |
| 80 | 25 |
| 91 | 27 |
| 100 | 20 |
| 95 | 21 |
| 109 | 19 |
| 77 | 28 |
| 100 | 221 |
| 98 | 25 |
| 89 | 27 |
| 97 | 23 |
| 93 | 22 |
| 89 | 27 |
| 91 | 22 |
| 97 | 21 |
| 105 | 21 |
| 88 | 22 |
| 83 | 24 |
| 86 | 27 |
| 89 | 26 |
| 79 | 30 |
| 88 | 22 |
| 94 | 24 |
| 18) | Add trendline, regression equation and r squared to the plot. |
| Add this title. (“Scatterplot of X and Y Data”) | |
| 19) | The scatterplot reveals a point outside the point pattern. Copy the data to a new location in the worksheet. You now have 2 sets of data. |
| Data that are more tha 1.5 IQR below Q1 or more than 1.5 IQR above Q3 are considered outliers and must be investigated. | |
| It was determined that the outlying point resulted from data entry error. Remove the outlier in the copy of the data. | |
| Make a new scatterplot linked to the cleaned data without the outlier, and add title (“Scatterplot without Outlier,”) trendline, and regression equation label. | |
| Compare the regression equations of the two plots. How did removal of the outlier affect the slope and R2? | |
| 20) |
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