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Quantitative Methods. Varsha Varde. Course Coverage. Essential Basics for Business Executives Data Classification & Presentation Tools Preliminary Analysis & Interpretation of Data Correlation Model Regression Model Time Series Model Forecasting Uncertainty and Probability

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Quantitative Methods


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    1. Quantitative Methods Varsha Varde

    2. Course Coverage • Essential Basics for Business Executives • Data Classification & Presentation Tools • Preliminary Analysis & Interpretation of Data • Correlation Model • Regression Model • Time Series Model • Forecasting • Uncertainty and Probability • Sampling Techniques • Estimation and Testing of Hypothesis varsha Varde

    3. Quantitative Methods Preliminary Analysis of Data

    4. Preliminary Analysis of Data Central Tendency of the Data at Hand: • Need to Size Up the Data At A Glance • Find A Single Number to Summarize the Huge Mass of Data Meaningfully: Average • Tools: Mode Median Arithmetic Mean Weighted Average varsha Varde

    5. Mode, Median, and Mean • Mode: Most Frequently Occurring Score • Median: That Value of the Variable Above Which Exactly Half of the Observations Lie • Arithmetic Mean: Ratio of Sum of the Values of A Variable to the Total Number of Values • Mode by Mere Observation, Median needs Counting, Mean requires Computation varsha Varde

    6. Example: Number of Sales Orders Booked by 50 Sales Execs April 2006 0, 0, 0, 0, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6, 6, 7, 7, 7, 8, 8, 8, 8, 9, 9, 9, 9, 9, 10, 10, 11, 11, 11, 12, 14, 15, 16, 17, 19, 21, 24, 28, 30, 34, 43 • Mode:9 (Occurs 5 Times) Orders • Median:8 (24 Obs. Above & 24 Below) Total Number of Sales Orders: 491 Total Number of Sales Execs : 50 • Arithmetic Mean: 491 / 50 = 9.82 Orders varsha Varde

    7. This Group This Group of Participants: Mode of age is years Median is years, Arithmetic Mean is years varsha Varde

    8. Arithmetic Mean - Example varsha Varde

    9. Arithmetic Mean - Example • Arithmetic Mean: 65 / 4 = 16.25 % • Query: But, Are All Products of Equal Importance to the Company? • For Instance, What Are the Sales Volumes of Each Product? Are They Identical? • If Not, Arithmetic Mean Can Mislead. varsha Varde

    10. Weighted Average - Example varsha Varde

    11. A Comparison • Mode: Easiest, At A Glance, Crude • Median: Disregards Magnitude of Obs., Only Counts Number of Observations • Arithmetic Mean: Outliers Vitiate It. • Weighted Av. Useful for Averaging Ratios • Symmetrical Distn: Mode=Median=Mean • +ly Skewed Distribution: Mode < Mean • -ly Skewed Distribution: Mode > Mean varsha Varde

    12. Preliminary Analysis of Data Measure of Dispersion in the Data: • ‘Average’ is Insufficient to Summarize Huge Data Spread over a Wide Range • Need to Obtain another Number to Know How Widely the Numbers are Spread • Tools: Range & Mean Deviation Variance & Standard Deviation Coefficient of Variation varsha Varde

    13. Range and Mean Deviation • Range: Difference Between the Smallest and the Largest Observation • Mean Deviation: Arithmetic Mean of the Deviations of the Observations from an Average, Usually the Mean. varsha Varde

    14. Computing Mean Deviation • Select a Measure of Average, say, Mean. • Compute the Difference Between Each Value of the Variable and the Mean. • Multiply the Difference by the Concerned Frequency. • Sum Up the Products. • Divide by the Sum of All Frequencies. • Mean Deviation is the Weighted Average. varsha Varde

    15. Mean Deviation - Example varsha Varde 15

    16. Mean Deviation - Example varsha Varde 16

    17. Mean Deviation - Example varsha Varde 17

    18. Mean Deviation - Example varsha Varde 18

    19. Mean Deviation • Sum of the Products: 318.12 • Sum of All Frequencies: 50 • Mean Deviation: 318.12 / 50 = 6.36 • Let Us Compute With a Simpler Example varsha Varde

    20. Machine Downtime Data in Minutes per Day for 100 Working DaysFrequency Distribution varsha Varde

    21. Machine Downtime Data in Minutes per Day for 100 Working DaysFrequency Distribution varsha Varde

    22. Arithmetic Mean varsha Varde

    23. Arithmetic Mean • Arithmetic Mean is the Average of the Observed Downtimes. • Arithmetic Mean= Total Observed Downtime/ total number of days • Arithmetic Mean= 2000 / 100 = 20 Minutes • Average Machine Downtime is 20 Minutes. varsha Varde

    24. Mean Deviation varsha Varde

    25. Mean Deviation varsha Varde

    26. Mean Deviation • Definition: Mean Deviation is mean of Deviations (Disregard negative Sign) of the Observed Values from the Average. • In this Example, Mean Deviation is the Weighted Average(weights as frequencies) of the Deviations of the Observed Downtimes from the Average Downtime. • Mean Deviation = 1000 / 100 = 10 Minutes varsha Varde

    27. Variance • Definition: Variance is the average of the Squares of the Deviations of the Observed Values from the mean. varsha Varde

    28. Standard Deviation • Definition: Standard Deviation is the Average Amount by which the Values Differ from the Mean, Ignoring the Sign of Difference. • Formula: Positive Square Root of the Variance. varsha Varde

    29. Variance varsha Varde

    30. Variance & Standard Deviation • Variance = 14500 / 100 = 145 Mts Square • Standard Deviation = Sq. Root of 145 = 12.04 Minutes • Exercise: This Group of 65: Compute the Variance & Standard Deviation of age varsha Varde

    31. Simpler Formula for Variance • Logical Definition: Variance is the Average of the Squares of the Deviations of the Observed Values from the mean. • Simpler Formula: Variance is the Mean of the Squares of Values Minus the Square of the Mean of Values.. varsha Varde

    32. Variance (by Simpler Formula) varsha Varde

    33. Variance (by Simpler Formula) • Mean of the Squares of Values = 54500/100 = 545 • Square of the Mean of Values=20x20=400 • Variance = Mean of Squares of Values Minus Square of Mean of Values = 545 – 400 = 145 • Standard Deviation = Sq.Root 145 = 12.04 varsha Varde

    34. Significance of Std. Deviation In a Normal Frequency Distribution • 68 % of Values Lie in the Span of Mean Plus / Minus One Standard Deviation. • 95 % of Values Lie in the Span of Mean Plus / Minus Two Standard Deviation. • 99 % of Values Lie in the Span of Mean Plus / Minus Three Standard Deviation. Roughly Valid for Marginally Skewed Distns. varsha Varde

    35. Machine Downtime Data in Minutes per Day for 100 Working DaysFrequency Distribution varsha Varde

    36. Interpretation from Mean & Std Dev Machine Downtime Data • Mean = 20 and Standard Deviation = 12 • Span of One Std. Dev. = 20–12 to 20+12 = 8 to 32: 60% Values • Span of Two Std. Dev. = 20–24 to 20+24 = -4 to 44: 95% Values • Span of Three Std. Dev. = 20–36 to 20+36 = -16 to 56: 100% Values varsha Varde

    37. Earlier Example varsha Varde

    38. Interpretation from Mean & Std Dev Sales Orders Data • Mean = 9.82 & Standard Deviation = 6.36 • Round Off To: Mean 10 and Std. Dev 6 • Span of One Std. Dev. = 10–6 to 10+6 = 4 to 16: 31 Values (62%) • Span of Two Std. Dev. = 10–12 to 10+12 = -2 to 22: 45 Values (90%) • Span of Three Std. Dev. = 10–18 to 10+18 = -8 to 28: 47 Values (94%) varsha Varde

    39. BIENAYME_CHEBYSHEV RULE • For any distribution percentage of observations lying within +/- k standard deviation of the mean is at least ( 1- 1/k square ) x100 for k>1 • For k=2, at least (1-1/4)100 =75% of observations are contained within 2 standard deviations of the mean varsha Varde

    40. Coefficient of Variation • Std. Deviation and Dispersion have Units of Measurement. • To Compare Dispersion in Many Sets of Data (Absenteeism, Production, Profit), We Must Eliminate Unit of Measurement. • Otherwise it’s Apple vs. Orange vs. Mango • Coefficient of Variation is the Ratio of Standard Deviation to Arithmetic Mean. • CoV is Free of Unit of Measurement. varsha Varde

    41. Coefficient of Variation • In Our Machine Downtime Example, Coefficient of Variation is 12.04 / 20 = 0.6 or 60% • In Our Sales Orders Example, Coefficient of Variation is 6.36 / 9.82 = 0.65 or 65% • The series for which CV is greater is said to be more variable or less consistent , less uniform, less stable or less homogeneous.

    42. Coefficient of Variation • In Our Machine Downtime Example, Coefficient of Variation is 12.04 / 20 = 0.6 • In Our Sales Orders Example, Coefficient of Variation is 6.36 / 9.82 = 0.65 • The series for which CV is greater is said to be more variable or less consistent , less uniform, less stable or less homogeneous.

    43. Example • Mean and SD of dividends on equity stocks of TOMCO & Tinplate for the past six years is as follows • Tomco:Mean=15.42%,SD=4.01% • Tinplate:Mean=13.83%, SD=3.19% • CV:Tomco=26.01%,Tinplate=23.01% • Since CV of dividend of Tinplates is less it implies that return on stocks of Tinplate is more stable • For investor seeking stable returns it is better to invest in scrips of Tinplate

    44. Exercise • List Ratios Commonly used in Cricket. • Study Individual Scores of Indian Batsmen at the Last One Day Cricket Match. • Are they Nominal, Ordinal or Cardinal Numbers? Discrete or Continuous? • Find Median & Arithmetic Mean. • Compute Range, Mean Deviation, Variance, Standard Deviation & CoV. .. varsha Varde

    45. Steps in Constructing a Frequency Distribution (Histogram) 1. Determine the number of classes 2. Determine the class width 3. Locate class boundaries 4. Use Tally Marks for Obtaining Frequencies for each class varsha Varde

    46. Rule of thumb • Not too few to lose information content and not too many to lose pattern • The number of classes chosen is usually between 6 and15. • Subject to above the number of classes may be equal to the square root of the number of data points. • The more data one has the larger is the number of classes. varsha Varde

    47. Rule of thumb • Every item of data should be included in one and only one class • Adjacent classes should not have interval in between • Classes should not overlap • Class intervals should be of the same width to the extent possible varsha Varde

    48. Illustration Frequency and relative frequency distributions (Histograms): Example Weight Loss Data 20.5 19.5 15.6 24.1 9.9 15.4 12.7 5.4 17.0 28.6 16.9 7.8 23.3 11.8 18.4 13.4 14.3 19.2 9.2 16.8 8.8 22.1 20.8 12.6 15.9 • Objective: Provide a useful summary of the available information varsha Varde

    49. Illustration • Method: Construct a statistical graph called a “histogram” (or frequency distribution) Weight Loss Data class boundaries - tally class rel . freq, f freq, f/n 1 5.0-9.0 3 3/25 (.12) 2 9.0-13.0 5 5/25 (.20) 3 13.0-17.0 7 7/25 (.28) 4 17.0-21.0 6 6/25 (.24) 5 21.0-25.0 3 3/25 (.12) 6 25.0-29.0 1 1/25 (.04) Totals 25 1.00 Let • k = # of classes • max = largest measurement • min = smallest measurement • n = sample size • w = class width varsha Varde

    50. Formulas • k = Square Root of n • w =(max− min)/k • Square Root of 25 = 5. But we used k=6 • w = (28.6−5.4)/6 w = 4.0 varsha Varde