Chapter 3 Describing Data: Numerical Measures Chapter 3 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill...

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this needs to be done on excel, due tonight. lmk



Chapter 3 Describing Data: Numerical Measures Chapter 3 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education. 1 Learning Objectives LO3-1 Compute and interpret the mean, the median, and the mode. LO3-2 Compute a weighted mean. LO3-3 Compute and interpret the geometric mean. LO3-4 Compute and interpret the range, variance, and standard deviation. LO3-5 Explain and apply Chebyshev’s theorem and the Empirical Rule. LO3-6 Compute the mean and standard deviation of grouped data. 3-‹#› 2 Measures of Location The purpose of a measure of location is to pinpoint the center of a distribution of data. There are many measures of location. We will consider three: The arithmetic mean The median The mode LO3-1 Compute and interpret the mean, the median, and the mode. 3-‹#› 3 Characteristics of the Mean The arithmetic mean is the most widely used measure of location. It requires the interval scale. Major characteristics: All values are used. It is unique. The sum of the deviations from the mean is 0. It is calculated by summing the values and dividing by the number of values. LO3-1 3-‹#› 4 . Population Mean For ungrouped data, the population mean is the sum of all the population values divided by the total number of population values: LO3-1 3-‹#› 5 Example – Population Mean LO3-1 There are 42 exits on I-75 through the state of Kentucky. Listed below are the distances between exits (in miles). Why is this information a population? What is the mean number of miles between exits? 3-‹#› 6 Example – Population Mean LO3-1 There are 42 exits on I-75 through the state of Kentucky. Listed below are the distances between exits (in miles). Why is this information a population? This is a population because we are considering all of the exits in Kentucky. What is the mean number of miles between exits? 3-‹#› 7 Parameter versus Statistic PARAMETER A measurable characteristic of a population. STATISTIC A measurable characteristic of a sample. LO3-1 3-‹#› . 8 Properties of the Arithmetic Mean Every set of interval-level and ratio-level data has a mean. All the values are included in computing the mean. The mean is unique. The sum of the deviations of each value from the mean is zero. LO3-1 3-‹#› 9 Sample Mean For ungrouped data, the sample mean is the sum of all the sample values divided by the number of sample values: LO3-1 3-‹#› 10 . Example – Sample Mean LO3-1 3-‹#› 11 The Median Properties of the median: There is a unique median for each data set. It is not affected by extremely large or small values and is therefore a valuable measure of central tendency when such values occur. It can be computed for ratio-level, interval-level, and ordinal-level data. It can be computed for an open-ended frequency distribution if the median does not lie in an open-ended class. MEDIAN The midpoint of the values after they have been ordered from the minimum to the maximum values. LO3-1 3-‹#› 12 Examples - Median The ages for a sample of five college students are: 21, 25, 19, 20, 22 Arranging the data in ascending order gives: 19, 20, 21, 22, 25. Thus the median is 21. The heights of four basketball players, in inches, are: 76, 73, 80, 75 Arranging the data in ascending order gives: 73, 75, 76, 80. Thus the median is 75.5. LO3-1 3-‹#› 13 The Mode MODE The value of the observation that appears most frequently. LO3-1 3-‹#› 14 Example - Mode LO3-1 Using the data measuring the distance in miles between exits on I-75 through Kentucky, what is the modal distance? Organize the distances into a frequency table and select the distance with the highest frequency. 3-‹#› 15 The Relative Positions of the Mean, Median and the Mode LO3-1 16 Weighted Mean The weighted mean of a set of numbers X1, X2, ..., Xn, with corresponding weights w1, w2, ...,wn, is computed with the following formula: LO3-2 Compute a weighted mean. 3-‹#› 17 Example – Weighted Mean The Carter Construction Company pays its hourly employees $16.50, $19.00, or $25.00 per hour. There are 26 hourly employees: 14 are paid at the $16.50 rate, 10 at the $19.00 rate, and 2 at the $25.00 rate. What is the mean hourly rate paid for the 26 employees? LO3-2 3-‹#› 18 The Geometric Mean Useful in finding the average change of percentages, ratios, indexes, or growth rates over time. It has wide application in business and economics because we are often interested in finding the percentage changes in sales, salaries, or economic figures, such as the GDP. The geometric mean will always be less than or equal to the arithmetic mean. LO3-3 Compute and interpret the geometric mean. 3-‹#› 19 The Geometric Mean: Finding the average rate of return over time EXAMPLE: The return on investment earned by Atkins Construction Company for four successive years was: 30 percent, 20 percent, -40 percent, and 200 percent. What is the geometric mean rate of return on investment? LO3-3 3-‹#› The Geometric Mean: Finding an Average Percent Change Over Time EXAMPLE: During the decade of the 1990s, and into the 2000s, Las Vegas, Nevada, was the fastest-growing city in the United States. The population increased from 258,295 in 1990 to 584,539 in 2011. This is an increase of 326,244 people, or a 126.3 percent increase over the period. What is the average annual increase? LO3-3 3-‹#› 21 Dispersion A measure of location, such as the mean or the median, only describes the center of the data but it does not tell us anything about the spread of the data. For example, if your nature guide told you that the river ahead averaged 3 feet in depth, would you want to wade across on foot without additional information? Probably not. You would want to know something about the variation in the depth. A second reason for studying the dispersion in a set of data is to compare the spread in two or more distributions. LO3-4 Compute and interpret the range, variance, and standard deviation. 3-‹#› 22 Measures of Dispersion Range Variance Standard Deviation LO3-4 3-‹#› 23 Example – Range The number of cappuccinos sold at the Starbucks location in the Orange County Airport between 4 and 7 p.m. for a sample of 5 days last year were 20, 40, 50, 60, and 80. Determine the range for the number of cappuccinos sold. Range = Maximum value – Minimum value = 80 – 20 = 60 LO3-4 3-‹#› 24 Variance and Standard Deviation The variance and standard deviations are nonnegative and are zero only if all observations are the same. For populations whose values are near the mean, the variance and standard deviation will be small. For populations whose values are dispersed from the mean, the population variance and standard deviation will be large. The variance overcomes the weakness of the range by using all the values in the population. VARIANCE The arithmetic mean of the squared deviations from the mean. STANDARD DEVIATION The square root of the variance. LO3-4 3-‹#› 25 Computing the Variance Steps in computing the variance: Step 1: Find the mean. Step 2:Find the difference between each observation and the mean, and square that difference. Step 3:Sum all the squared differences found in Step 2. Step 4:Divide the sum of the squared differences by the number of items in the population. LO3-4 3-‹#› 26 Example – Variance and Standard Deviation The number of traffic citations issued during the last twelve months in Beaufort County, South Carolina, is reported below: What is the population variance? Step 1: Find the mean. LO3-4 3-‹#› 27 Example – Variance and Standard Deviation Continued What is the population variance? Step 2: Find the difference between each observation and the mean of 29, and square that difference. Step 3: Sum all the squared differences found in Step 2. Step 4: Divide the sum of the squared differences by the number of items in the population. LO3-4 3-‹#› 28 Sample Variance LO3-4 3-‹#› 29 Example – Sample Variance The hourly wages for a sample of part-time employees at Home Depot are: $12, $20, $16, $18, and $19. The sample mean is $17. What is the sample variance? LO3-4 3-‹#› 30 Sample Standard Deviation LO3-4 3-‹#› 31 Chebyshev’s Theorem The arithmetic mean biweekly amount contributed by the Dupree Paint employees to the company’s profit-sharing plan is $51.54, and the standard deviation is $7.51. At least what percent of the contributions lie within plus 3.5 standard deviations and minus 3.5 standard deviations of the mean? LO3-5 Explain and apply Chebyshev’s theorem and the Empirical Rule. 3-‹#› 32 The Empirical Rule LO3-5 3-‹#› 33 The Arithmetic Mean of Grouped Data LO3-6 Compute the mean and standard deviation of grouped data. 3-‹#› 34 Example - The Arithmetic Mean of Grouped Data Recall in Chapter 2, we constructed a frequency distribution for Applewood Auto Group profit data for 180 vehicles sold. The information is repeated in the table. Determine the arithmetic mean profit per vehicle. LO3-6 3-‹#› 35 Example - The Arithmetic Mean of Grouped Data LO3-6 3-‹#› 36 Example - Standard Deviation of Grouped Data Refer to the frequency distribution for the Applewood Auto Group data used earlier. Compute the standard deviation of the vehicle profits. LO3-6 3-‹#› 37 29 12 348 12 10 34 ... 17 19 = = + + + + = = å N x m 124 12 488 , 1 ) ( 2 2 = = - = å N X m s sample the in ns observatio of number the is n sample the of mean the is x sample the in n observatio each of value the is x variance sample the is s : where 2 Nickel, which started trading on the London Metal Exchange (LME) in 1979, is a key component in the making of stainless steel – 78% of the world’s nickel is consumed by the stainless steel industry. It also plays a crucial role in battery technology and will play a big part
Answered Same DayFeb 06, 2021

Answer To: Chapter 3 Describing Data: Numerical Measures Chapter 3 Copyright © 2015 McGraw-Hill Education. All...

Kushal answered on Feb 07 2021
143 Votes
Nickel Futures Historical Data
    Date    Nickel Price    Nickel Change %    Cobalt price    Cobalt change    Lithium price    LithiumChange %    Year    Cobalt price    Nickel Price    Lithium Price
    Jan-20    12777.5    -0.0907    34637.5    0.0702    49000    -0.0101    2020    34637.5    49000    49000        Nickel            Cobalt            Lithium
    Dec-19    14052.5    0.0282    32366    -0.0853    49500    -0.0833    2019    32366    49500    49500
    Nov-19    13667.5    -0.1811    35384    -0.006    54000    -0.0769    2019    35384    54000    54000
    Oct-19    16690    -0.0245    35598    0.0013    58500    -0.0085    2019    35598    58500    58500        Arithmatic Return (2009-12)    1.69%        Arithmatic Return (2009-12)    -1.50%        Arithmatic Return (2009-12)    0.70%
    Sep-19    17110    -0.0441    35552    0.1029    59000    -0.0407    2019    35552    59000    59000        Arithmatic Return (2016-18)    0.87%        Arithmatic Return (2016-18)    2.70%        Arithmatic Return (2016-18)    -1.10%
    Aug-19    17900    0.23    32235    0.2559    61500    -0.0821    2019    32235    61500    61500        Arithmatic Return (2018-)    0.42%        Arithmatic Return (2018-)    -2.27%        Arithmatic Return (2018-)    -4.63%
    Jul-19    14552.5    0.1481    25666    -0.1042    67000    -0.0884    2019    25666    67000    67000        Arithmatic Return overall    -0.61%        Arithmatic Return overall    -
0.17%        Arithmatic Return overall    -0.53%
    Jun-19    12675    0.0571    28650    -0.1047    73500    -0.0455    2019    28650    73500    73500
    May-19    11990    -0.02    32000    -0.0725    77000    0.0065    2019    32000    77000    77000        Geometric Return (2009-12)    0.87%        Geometric Return (2009-12)    -1.57%        Geometric Return (2009-12)    0.73%
    Apr-19    12235    -0.0585    34500    0.15    76500    0    2019    34500    76500    76500        Geometric Return (2016-18)    0.60%        Geometric Return (2016-18)    2.60%        Geometric Return (2016-18)    -1.80%
    Mar-19    12995    -0.0052    30000    -0.0909    76500    -0.0255    2019    30000    76500    76500        Geometric Return (2018-)    -0.22%        Geometric Return (2018-)    -3.29%        Geometric Return (2018-)    -4.49%
    Feb-19    13062.5    0.0477    33000    -0.0294    78500    0.0129    2019    33000    78500    78500        Geometric Return overall    0.09%        Geometric Return overall    -0.16%        Geometric Return overall    0.25%
    Jan-19    12467.5    0.1676    34000    -0.3818    77500    -0.0252    2019    34000    77500    77500
    Dec-18    10677.5    -0.0378    55000    0    79500    -0.0063    2018    55000    79500    79500        Volatility (2009-12)    10.89%        Volatility (2009-12)    6.24%        Volatility (2009-12)    0.93%
    Nov-18    11097.5    -0.0386    55000    -0.0795    80000    0.0256    2018    55000    80000    80000        Volatility (2016-18)    9.07%        Volatility (2016-18)    8.48%        Volatility (2016-18)    11.92%
    Oct-18    11542.5    -0.0801    59750    -0.0355    78000    -0.0311    2018    59750    78000    78000        Volatility (2018-)    8.88%        Volatility (2018-)    10.66%        Volatility (2018-)    6.16%
    Sep-18    12547.5    -0.0209    61951    -0.0366    80500    -0.0417    2018    61951    80500    80500        Volatility overall    -8.96%        Volatility overall    -8.46%        Volatility overall    -8.34%
    Aug-18    12815    -0.0895    64306    -0.0694    84000    -0.1845    2018    64306    84000    84000
    Jul-18    14075    -0.0585    69100    -0.109    103000    -0.1626    2018    69100    103000    103000
    Jun-18    14950    -0.0224    77550    -0.1383    123000    -0.0752    2018    77550    123000    123000
    May-18    15292.5    0.1183    90000    0.0169    133000    -0.0732    2018    90000    133000    133000
    Apr-18    13675    0.022    88500    -0.054    143500    -0.0712    2018    88500    143500    143500
    Mar-18    13380    -0.026    93550    0.1549    154500    0    2018    93550    154500    154500
    Feb-18    13737.5    0.0185    81000    0.0131    154500    0    2018    81000    154500    154500
    Jan-18    13487.5    0.066    79956    0.0632    154500    -0.0693    2018    79956    154500    154500
    Dec-17    12652.5    0.1355    75205    0.1195    166000    -0.0292    2017    75205    166000    166000
    Nov-17    11142.5    -0.103    67180    0.1123    171000    0    2017    67180    171000    171000
    Oct-17    12422.5    0.1803    60400    0.023    171000    0.0491    2017    60400    171000    171000
    Sep-17    10525    -0.1065    59040    -0.0281    163000    0.0449    2017    59040    163000    163000
    Aug-17    11780    0.1524    60750    0.0688    156000    0.0505    2017    60750    156000    156000
    Jul-17    10222.5    0.0907    56840    -0.0487    148500    0.0206    2017    56840    148500    148500
    Jun-17    9372.5    0.0469    59750    0.0603    145500    0.0319    2017    59750    145500    145500
    May-17    8952.5    -0.0526    56350    0.0245    141000    0.0522    2017    56350    141000    141000
    Apr-17    9450    -0.0571    55000    0.0092    134000    0.0152    2017    55000    134000    134000
    Mar-17    10022.5    -0.086    54500    0.0846    132000    0.0313    2017    54500    132000    132000
    Feb-17    10965    0.1006    50250    0.3581    128000    0.0159    2017    50250    128000    128000
    Jan-17    9962.5    -0.0094    37000    0.1298    126000    0    2017    37000    126000    126000
    Dec-16    10057.5    -0.0994    32750    0.1008    126000    0.012    2016    32750    126000    126000
    Nov-16    11167.5    0.072    29750    0.0425    124500    0.0081    2016    29750    124500    124500
    Oct-16    10417.5    -0.0088    28537    0.0377    123500    -0.0276    2016    28537    123500    123500
    Sep-16    10510    0.0749    27500    0.0476    127000    -0.0155    2016    27500    127000    127000
    Aug-16    9777.5    -0.0808    26250    0    129000    -0.1457    2016    26250    129000    129000
    Jul-16    10637.5    0.1298    26250    0.1079    151000    -0.0563    2016    26250    151000    151000
    Jun-16    9415    0.1155    23693    0.0003    160000    -0.0476    2016    23693    160000    160000
    May-16    8440    -0.105    23686.5    -0.0016    168000    -0.0118    2016    23686.5    168000    168000
    Apr-16    9430    0.1101    23725.5    0.0794    170000    -0.0087    2016    23725.5    170000    170000
    Mar-16    8495    -0.0012    21981    -0.0278    171500    0.0178    2016    21981    171500    171500
    Feb-16    8505    -0.011    22609.5    0.0362    168500    0.1013    2016    22609.5    168500    168500
    Jan-16    8600    -0.0244    21820    -0.0895    153000    0.186    2016    21820    153000    153000
    Dec-15    8815    -0.0045    23966    -0.0282    129000    0.5732    2015    23966    129000    129000
    Nov-15    8855    -0.1167    24660.8    -0.0844    82000    0.2424    2015    24660.8    82000    82000
    Oct-15    10025    -0.0291    26933    -0.0277    66000    0.2692    2015    26933    66000    66000
    Sep-15    10325    0.0279    27700    -0.016    52000    0.0097    2015    27700    52000    52000
    Aug-15    10045    -0.0914    28151    -0.083    51500    0.0098    2015    28151    51500    51500
    Jul-15    11055    -0.0784    30700    -0.0767    51000    0.0099    2015    30700    51000    51000
    Jun-15    11995    -0.0465    33250    0.0956    50500    0.0306    2015    33250    50500    50500
    May-15    12580    -0.0956    30350    0.0144    49000    0    2015    30350    49000    49000
    Apr-15    13910    0.1263    29919    0.1181    49000    0.0538    2015    29919    49000    49000
    Mar-15    12350    -0.1219    26758    -0.0683    46500    0.0403    2015    26758    46500    46500
    Feb-15    14065    -0.0719    28720    -0.0246    44700    0    2015    28720    44700    44700
    Jan-15    15155    0.0005    29444    -0.0621    44700    0.0395    2015    29444    44700    44700
    Dec-14    15148    -0.064    31394    0.0329    43000    0.0361    2014    31394    43000    43000
    Nov-14    16184    0.031    30393    0.0144    41500    0.0122    2014    30393    41500    41500
    Oct-14    15697    -0.0349    29962    -0.0689    41000    0    2014    29962    41000    41000
    Sep-14    16265    -0.1339    32180.5    -0.0078    41000    0.038    2014    32180.5    41000    41000
    Aug-14    18780    0.015    32434    0.0165    39500    0    2014    32434    39500    39500
    Jul-14    18502    -0.0277    31907    0.0451    39500    0.0128    2014    31907    39500    39500
    Jun-14    19030    -0.0122    30531    0.0017    39000    -0.025    2014    30531    39000    39000
    May-14    19266    0.0532    30478    0.0195    40000    0    2014    30478    40000    40000
    Apr-14    18292    0.1513    29895    -0.0477    40000    -0.0244    2014    29895    40000    40000
    Mar-14    15888    0.0834    31393    0.0024    41000    0    2014    31393    41000    41000
    Feb-14    14665    0.0464    31318.5    0.0828    41000    0    2014    31318.5    41000    41000
    Jan-14    14015    0.0045    28924    -0.0012    41000    0.025    2014    28924    41000    41000
    Dec-13    13952    0.035    28960    0.1386    40000    0    2013    28960    40000    40000
    Nov-13    13480    -0.0761    25435    -0.0534    40000    -0.0244    2013    25435    40000    40000
    Oct-13    14591    0.0495    26870    0.0118    41000    -0.012    2013    26870    41000    41000
    Sep-13    13903    0.0083    26556    -0.0487    41500    -0.0119    2013    26556    41500    41500
    Aug-13    13789    -0.0044    27916    0.058    42000    -0.0118    2013    27916    42000    42000
    Jul-13    13850    0.0087    26385    -0.1776    42500    -0.0116    2013    26385    42500    42500
    Jun-13    13731    -0.0734    32084    0.0876    43000    0    2013    32084    43000    43000
    May-13    14819    -0.0346    29500    0.0724    43000    -0.0115    2013    29500    43000    43000
    Apr-13    15350    -0.08    27508.8    0.0211    43500    0    2013    27508.8    43500    43500
    Mar-13    16685    0.0048    26940    0.0588    43500    0    2013    26940    43500    43500
    Feb-13    16605    -0.0967    25444    -0.0285    43500    0.0116    2013    25444    43500    43500
    Jan-13    18383    0.0748    26190    0.0305    43000    0    2013    26190    43000    43000
    Dec-12    17104    -0.0235    25415    0.112    43000    0    2012    25415    43000    43000
    Nov-12    17515    0.0843    22855    -0.1055    43000    0    2012    22855    43000    43000
    Oct-12    16153    -0.1221    25550    -0.1412    43000    0    2012    25550    43000    43000
    Sep-12    18400    0.1521    29750    0.0085    43000    0    2012    29750    43000    43000
    Aug-12    15971    0.0102    29500    -0.0087    43000    0    2012    29500    43000    43000
    Jul-12    15810    -0.0567    29760    0.0629    43000    0    2012    29760    43000    43000
    Jun-12    16761    0.0267    28000    -0.0744    43000    0.0361    2012    28000    43000    43000
    May-12    16325    -0.087    30250    -0.0082    41500    0.0375    2012    30250    41500    41500
    Apr-12    17880    0.0044    30500    -0.0686    40000    0.0256    2012    30500    40000    40000
    Mar-12    17801    -0.0871    32745    0.0445    39000    0    2012    32745    39000    39000
    Feb-12    19500    -0.0592    31350    -0.0468    39000    0    2012    31350    39000    39000
    Jan-12    20727    0.1132    32890    0.0974    39000    0.0263    2012    32890    39000    39000
    Dec-11    18620    0.0686    29970    -0.0448    38000    0    2011    29970    38000    38000
    Nov-11    17425    -0.1089    31374    0.1041    38000    0    2011    31374    38000    38000
    Oct-11    19555    0.1095    28415    -0.1269    38000    0    2011    28415    38000    38000
    Sep-11    17625    -0.2077    32545    -0.0856    38000    0    2011    32545    38000    38000
    Aug-11    22245    -0.1014    35590    -0.0139    38000    0    2011    35590    38000    38000
    Jul-11    24756    0.0568    36090    0.0311    38000    0    2011    36090    38000    38000
    Jun-11    Feb-64    -0.0011    35000    -0.0565            2011    35000
    May-11    Mar-64    -0.1266    37095    0.0026            2011    37095
    Apr-11    Jul-73    0.0274    37000    0.0027            2011    37000
    Mar-11    Jul-71    -0.098    36900    -0.0754            2011    36900
    Feb-11    Apr-79    0.0614    39910    0.022            2011    39910
    Jan-11    Sep-74    0.0942    39050    0.013            2011    39050
    Dec-10    Apr-68    0.0921    38550    0.0649            2010    38550
    Nov-10    Jul-62    -0.0072    36200    -0.0573            2010    36200
    Oct-10    Dec-62    -0.0133    38400    -0.0098            2010    38400
    Sep-10    Nov-63    0.1258    38780    -0.0689            2010    38780
    Aug-10    Sep-56    -0.0159    41650    0.0932            2010    41650
    Jul-10    Aug-57    0.0712    38100    -0.009            2010    38100
    Jun-10    Oct-53    -0.0665    38445    -0.0174            2010    38445
    May-10    Aug-57    -0.2042    39125    -0.0869            2010    39125
    Apr-10    May-72    0.0559    42850    -0.0481            2010    42850
    Mar-10    Jul-68    0.1841                    2010
    Feb-10    Dec-57    0.1374                    2010
    Jan-10    Dec-50    -0.0132                    2010
    Dec-09    Aug-51    0.1524                    2009
    Nov-09    Oct-44    -0.1014                    2009
    Oct-09    Oct-49    0.0225                    2009
    Sep-09    Sep-48    -0.069                    2009
    Aug-09    May-52    0.0711                    2009
    Jul-09    Nov-48    0.1583                    2009
    Jun-09    Mar-42    0.1249                    2009
    May-09    Jul-37    0.178                    2009
    Apr-09    Nov-31    0.1867                    2009
    Mar-09    Oct-26    -0.0126                    2009
    Feb-09    Mar-27    -0.1217                    2009
    Jan-09    Dec-30    0.1472                    2009
Sheet1
    Date    Nickel Price    Nickel Change %    Cobalt price    Cobalt change    Lithium price    LithiumChange %
    Jan-20    12777.5    0.0248646481    34637.5    0.01875    49000    -0.3677419355
    Jan-19    12467.5    -0.0756255792    34000    -0.5747661214    77500    -0.498381877
    Jan-18    13487.5    0.3538268507    79956    1.160972973    154500    0.2261904762
    Jan-17    9962.5    0.1584302326    37000    0.6956920257    126000    -0.1764705882
    Jan-16    8600    -0.432530518    21820    -0.2589322103    153000    2.4228187919
    Jan-15    15155    0.0813414199    29444    0.0179781496    44700    0.0902439024
    Jan-14    14015    -0.2376108361    28924    0.1043909889    41000    -0.0465116279
    Jan-13    18383    -0.1130892073    26190    -0.2037093341    43000    0.1025641026
    Jan-12    20727    -0.2407692308    32890    -0.1577464789    39000
    Jan-11    27300    0.4677419355    39050
    Jan-10    18600    0.6460176991
    Jan-09    11300
        Nickel    Cobalt    Lithium
    Average Annual return    0.0575088559    0.0891811103    0.2190889056
Sheet2
    Date    Nickel Change %    Cobalt change    LithiumChange...
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