MSF502 0130HW-CH3

Name: Tanvi Vikas Patil I have used excel functions to calculate the formulas Question 25 A) Calculate and interpret the 25th, 50th and 75th percentiles (took only 10 company) Company P/E Ratio Chevron corp (CVX) 7 Hewlwtt-Packard (HPQ) 8 Exxonmobil (XOM) 10 caterpillar (CAT) 11 Intel (INTC) 11 Boeing (BA) 12 Cisco System (CSCO) 12 American experess (AXP) 13 DuPont (DD) 13 3M (MMM) 14 General Electric (GE) 15 merck (MRK) 17 McDonald's (MCD) 17 Johnson & Johnson 17 Home Depot (HD) 19 coca-cola (KO) 20 Plizer (PFE) 21 Alcoa (AA) 24 Tesla, INC 42.11 AT&T (T) 49 Super micro computer, INC 51.82 Using the PERCENTILE.INC function we get the follwing data For 25th percentiles 12 For 50th percentiles 15 For 75th percentiles 20 Therefore, 25th, 50th and 75th percentile is 17, 19 and 49 respectively B) Construct an boxplot yes there are outliers The following distribution is not Symmetric and its positively skewness 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.

Question 34 Year Annual Return 1 17.3 2 19.6 3 6.8 4 8.2 A) calculate arithmetic mean return 12.975 Therefore the arithmetic mean return is 12.975 by using AVEGRAGE function. The arithmetic mean of the data sets is the simple average of the datasets in which the data base are added and then the sum is divided by total number of datasets. B) calculate geometric return 11.7261713181829 Thus the geometric mean of the return is 11.726 by using GEOMEAN function The geometric mean is the central value of the set of data points which is found by multiplying the datasets and taking its root by the number of dataset Question 65 Average return is 8 % standard deviation is 12% A) z = (x - average return)/standard deviation z = (20 - 8)/12 z = 1 thus, P(x > 12) = P(z > 1) = 0.1587 thus, 16% B) z = (-16 - 8)/12 = -2 P(x < -16) = P(z < -2) 0.0228 thus, 2.3% Question 73 Year Vanguard Group Fidelity Investment 2007 56.17 -11.9 2008 52.57 20.95 2009 -17.55 2.76 2010 7.29 18.98 2011 -2.72 5.6 2012 16.57 -2.83 2013 59.66 -0.09 2014 33.22 21.72 2015 17.93 -0.87 2016 21.72 21.72 2017 -32.67 -1.56 2018 -31.9 -14.64 2019 58.89 31.34 2020 23.09 -11.22 2021 31.78 -11.49 2022 -12.72 18.69 2023 7.34 23.09 A) Calculate the sample covariance between the returns 89.5033529411765 Thus, 89.50 is the covariance by using COVARAINCE.S function B) Calculate Interpret the correlation coefficient 0.199919771589507 Thus, 0.19 is the correlation coefficient by using CORREL function

Question 44 Date GOOG TESLA 3/1/2019 58.665501 18.657333 4/1/2019 59.424 15.912667 5/1/2019 55.181499 12.344 6/1/2019 54.045502 14.897333 7/1/2019 60.834 16.107332 8/1/2019 59.404999 15.040667 9/1/2019 60.950001 16.058001 10/1/2019 63.005501 20.994667 11/1/2019 65.248001 21.996 12/1/2019 66.850998 27.888666 1/1/2020 71.711502 43.371334 2/1/2020 66.966499 44.532665 3/1/2020 58.140499 34.933334 4/1/2020 67.432999 52.125332 5/1/2020 71.445999 55.666668 6/1/2020 70.680496 71.987335 7/1/2020 74.148003 95.384003 8/1/2020 81.709 166.106674 9/1/2020 73.480003 143.003326 10/1/2020 81.050499 129.346664 11/1/2020 88.037003 189.199997 12/1/2020 87.594002 235.223328 1/1/2021 91.787003 264.51001 2/1/2021 101.843002 225.166672 3/1/2021 103.431503 222.643326 4/1/2021 120.505997 236.479996 5/1/2021 120.578003 208.406662 6/1/2021 125.316002 226.566666 7/1/2021 135.220993 229.066666 8/1/2021 145.462006 245.240005 9/1/2021 133.265503 258.493347 10/1/2021 148.270493 371.333344 11/1/2021 142.451996 381.58667 12/1/2021 144.679504 352.26001 1/1/2022 135.698502 312.23999 2/1/2022 134.891006 290.143341 3/1/2022 139.649506 359.200012 4/1/2022 114.966499 290.253326 5/1/2022 114.039001 252.753326 6/1/2022 109.372498 224.473328 7/1/2022 116.639999 297.149994 8/1/2022 109.150002 275.609985 9/1/2022 96.150002 265.25 10/1/2022 94.660004 227.539993 11/1/2022 101.449997 194.699997 12/1/2022 88.730003 123.18 1/1/2023 99.870003 173.220001 2/1/2023 90.300003 205.710007 3/1/2023 104 207.460007 4/1/2023 108.220001 164.309998 5/1/2023 123.370003 203.929993 6/1/2023 120.970001 261.769989 7/1/2023 133.110001 267.429993 8/1/2023 137.350006 258.079987 9/1/2023 131.850006 250.220001 10/1/2023 125.300003 200.839996 11/1/2023 133.919998 240.080002 12/1/2023 140.929993 248.479996 Mean 100.231130137931 180.285413137931 A) calculate the sample variance and sample standard deviation for each firm variance for GOOG 876.25978087264 variance for TESLA 11734.9659090946 Standard deviation for GOOG 29.6016854397286 Standard deviation for TESLA 108.328047656618 B) which firms stock price had greater variability as measured by the standard deviation C) Which stock price had the greater relative dispersion? Calculate coefficient of variation for GOOG 0.295334247942659 Calculate coefficient of variation for TESLA 0.600869730784818 Tesla had greater variability measured by standard deviation Thus, TESLA had a great relative dispersion as compare to GOOG

Sure, let's create two datasets—one cross-sectional and one tim Cross-Sectional Dataset: This dataset represents information about different companies a Company Industry Employees Company A Technology 500 1000 Company B Finance 300 500 Company C Healthcare 700 1200 Company D 400 800 Company E Retail 200 300 In this cross-sectional dataset: Qualitative variables: Company (categorical), Industry (catego Quantitative variables: Revenue, Employees, Profit Margin. Time Series Dataset: This dataset represents the stock prices of a single company ove Date 1/1/2023 50 2 Positive 2/1/2023 55 2.5 Neutral 3/1/2023 48 1.8 Negative 4/1/2023 60 3 Positive Revenue (in millions) Manufacturi ng Stock Price ($) Trading Volume (millions) News Sentiment (Positive/Negative)

Nigeria Africa 2,230 8.1 Russia Europe 10,230 5.8 Australia Oceania 53,800 5.2 Mexico North America 9,224 3.6 Saudi Arabia Asia 20,028 5.7 South Africa Africa 5,410 28.5 France Europe 41,760 8.5

me series—related to economics or finance. For simplicity, I'll provide fictional d at a specific point in time. Profit Margin 15% 8% 20% 12% 5% orical). er a period of months.

0.539 0.824 0.944 0.779 0.857 0.709 0.901

datasets with a mix of qualitative and quantitative variables.