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    Descriptive Statistics & Regression Analysis

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    I could really use your help, I have attached the work that I have completed for Case #2 in the word document attached. In case #2 I need help with part "a".

    The excel spreadsheet provides all of the work that I have done for Case #2 and #3. In Case #3 I need help with "discussing the predictability of productivity from cumulative hours of training".

    In Case #1 I am just completely lost. I would really appreciate your help with this. I have attached the complete assignment in the word document titled assignment 6 so that it made sense.

    Thank you

    Case # 1

    Shown below are sales figures for 2009 for personal soaps sold throughout Europe. You will note that each of these soaps is produced by 4 manufacturers - Unilever, Proctor & Gamble, Dial, and Colgate-Palmolive.

    Soap Manufacturer Sales ($ millions)
    Dove Unilever 271
    Dial Dial 193
    Lever 2000 Unilever 138
    Irish Spring Colgate-Palmolive 121
    Zest Proctor & Gamble 115
    Ivory Proctor & Gamble 94
    Caress Unilever 93
    Olay Proctor & Gamble 69
    Safeguard Proctor & Gamble 48
    Coast Dial 44

    In 1983, the market share for soap were:
    Proctor & Gamble = 37.1%
    Unilever = 24%
    Dial = 15%
    Colgate-Palmolive = 6.5%
    All others = 17.4%

    By 1991, the market shares were:
    Unilever = 31.5%
    Proctor & Gamble = 30.5%
    Dial = 19%
    Colgate ââ?¬" Palmolive = 8%
    All others = 11%

    Question:

    Produce an Executive Summary to the CEO of Proctor&-Gamble either in Power Point or in a Word Doc that includes:

    a. A pie chart showing the market shares for 1983 and 1991.

    b. A third pie chart which shows the market share for 2009. Assume that the ââ?¬Ë?All othersââ?¬â?¢ total is $119 M.

    c. Indicate to the CEO of Proctor & ââ?¬" Gamble your observations about the market shares of the various companies by studying the charts. Also, relate to the CEO specifically how Proctor &-Gamble is performing relative to previous years.

    Case # 2

    Coca-Cola is the number one seller of soft drinks in the world. It continues to try to grow internationally with operations throughout Europe, Asia and Russia. However, many of these nations desire different bottle sizes which is a manufacturing concern for the Coke bottlers in these nations.

    Due to the variability of the new bottling machinery, it is likely that every 200 milliliter bottle of Coke does not contain exactly 200 milliliters. Some may contain more and some less. Coke brings in a production engineer who will test some of the bottles from the first production run to determine how close they are to the 200 milliliter level.

    Below are the fill measurements from a run of 50 random sample bottles:

    200.1 199.9 200.2 200.2 200.0 200.1 200.9 200.1 200.3 200.5
    199.7 200.4 200.3 199.8 199.3 200.1 199.4 199.6 199.2 200.2
    200.4 199.8 199.9 200.2 199.6 199.6 200.4 200.4 200.6 200.6
    200.1 200.8 199.9 200.0 199.9 200.3 200.5 199.9 201.1 199.7
    200.2 200.5 200.2 199.7 200.9 200.2 199.5 200.6 200.3 199.8

    Question:

    In Excel use the Descriptive Statistics and comment on the mean, the skewness, the standard deviation and kurtosis.

    a. Based on your analysis, how is the new bottling process working?

    Case # 3

    The Bekaert Company of Belgium is a major supplier of bead wire for tire re-enforcement and other specialized wire processes in the USA and throughout the world. The company has always prided itself on its dedication to employee training and education.

    Recently they have increased production from 70,000 to 90,000 pounds per week during a time when it implemented a basic skills training program over an 18 month period.

    The following data date measures the number of total cumulative basic skills hours of training and the per week productivity figures taken once a month over this time period.

    Question:

    In Excel use the regression tool to analyze the data in a brief report (Power Point or Word Doc) to the CEO of Bekaert.

    Discuss the predictability of productivity from cumulative hours of training.

    Cumulative Hours of Training Productivity (lbs. /week)
    0 70,000
    100 70,350
    250 70,500
    375 72,600
    525 74,000
    750 76,500
    875 77,000
    1,100 77,400
    1,300 77,900
    1,450 77,200
    1,660 78,900
    1,900 81,000
    2,300 82,500
    2,600 84,000
    2,850 86,500
    3,150 87,000
    3,500 88,600
    4,000 90,000

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    https://brainmass.com/statistics/regression-analysis/descriptive-statistics-regression-analysis-441569

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    Solution Summary

    The solution provides step by step method for the calculation of descriptive statistics and regression analysis. Formula for the calculation and Interpretations of the results are also included.

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