Excel and research

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Excel and research

  1. 1. USING MICROSOFT EXCEL WITH BUSINESS RESEARCH METHODS www.drjayeshpatidar.blogspot.com
  2. 2. TITLE BAR MENU BAR STANDARD TOOLBAR FORMATTING TOOLBAR FORMULA BAR ACTIVE CELL
  3. 3. PASTE FUNCTION TOOLS MENU
  4. 4. The Paste Function Provides Numerous Statistical Operations
  5. 5. The Statistical Function Category
  6. 6. Data Analysis Dialog Box • Click on “Tools” • Select “Data Analysis” • Select statistical operation o such as Histogram
  7. 7. Functions • Functions are predefined formulas for mathematical operations • They perform calculations by using specific values, called arguments • Arguments indicate data or a range of cells • Arguments are performed, in a particular order, called the syntax.
  8. 8. Functions • Functions are predefined formulas for mathematical operations • They perform calculations by using specific values, called arguments • Arguments are performed, in a particular order, called the syntax. • For example, the SUM function adds values or ranges of cells
  9. 9. Easy to Use Paste Functions • • • • • AVERAGE (MEAN) MEDIAN MODE SUM STANDARD DEVIATION
  10. 10. Functions • The syntax of a function begins with the function name • followed by an opening parenthesis • the arguments for the function • separated by commas • a closing parenthesis. • If the function starts a formula, an equal sign (=) is typed before the function name.
  11. 11. The Equal Sign Then The Function Name And Arguments • =FUNCTION (Argument1) • =FUNCTION (Argument1,Argument2)
  12. 12. Arguments • Typical arguments are numbers, text, arrays, and cell references. • Arguments can also be constants, formulas, or other functions.
  13. 13. The AVERAGE Function Located in the Statistical Category
  14. 14. Data Array • • • • The data appear in cells A2 through 14 A2:A14 Sometimes written with dollars signs $A$2:$A$14
  15. 15. Sum, Average, and Standard Deviation • • • • =FUNCTION (Argument1) =SUM(A2:A9) =AVERAGE(A2:A9) =STDEVA(A2:A9)
  16. 16. SUM Function Sales Call Example
  17. 17. AVERAGE (Mean) Function Sales Call Example
  18. 18. Standard Deviation Function Sales Call Example Variance s2: (algebraic, scalable computation) s 2 n n n 1 1 1 2 2   ( xi  x )  n  1 [ xi  n ( xi ) 2 ] n  1 i 1 i 1 i 1 Standard deviation s is the square root of variance s2
  19. 19. • Variance • Standard deviation: the square root of the variance – Measures spread about the mean – It is zero if and only if all the values are equal – Both the deviation and the variance are algebraic www.drjayeshpatidar.blogspot.com 26
  20. 20. Data Dispersion Characteristics • Motivation – • Data dispersion characteristics – • To better understand the data: central tendency, variation and spread median, max, min, quantiles, outliers, variance, etc. Numerical dimensions correspond to sorted intervals – – • Data dispersion: analyzed with multiple granularities of precision Boxplot or quantile analysis on sorted intervals Dispersion analysis on computed measures – Folding measures into numerical dimensions – Boxplot or quantile analysis on the transformed cube www.drjayeshpatidar.blogspot.com 27
  21. 21. Measuring the Central Tendency • Mean – • 1 n x   xi n i 1 n Weighted arithmetic mean x  Median: A holistic measure – w x i 1 n i i w i 1 i Middle value if odd number of values, or average of the middle two values otherwise – • estimated by interpolation Mode – Value that occurs most frequently in the data – Unimodal, bimodal, trimodal – Empirical formula: mean  mode  3  (mean  median) www.drjayeshpatidar.blogspot.com 28
  22. 22. Measuring the Dispersion of Data • Quartiles, outliers and boxplots – – Inter-quartile range: IQR = Q3 – Q1 – Five number summary: min, Q1, M, Q3, max – Boxplot: ends of the box are the quartiles, median is marked, whiskers, and plot outlier individually – • Quartiles: Q1 (25th percentile), Q3 (75th percentile) Outlier: usually, a value higher/lower than 1.5 x IQR Variance and standard deviation – Variance s2: (algebraic, scalable computation) s – 2 n n n 1 1 1 2 2   ( xi  x )  n  1 [ xi  n ( xi ) 2 ] n  1 i 1 i 1 i 1 Standard deviation s is the square root of variance s2 www.drjayeshpatidar.blogspot.com 29
  23. 23. Boxplot Analysis • Five-number summary of a distribution: Minimum, Q1, M, Q3, Maximum • Boxplot – Data is represented with a box – The ends of the box are at the first and third quartiles, i.e., the height of the box is IRQ – The median is marked by a line within the box – Whiskers: two lines outside the box extend to Minimum and Maximum www.drjayeshpatidar.blogspot.com 30
  24. 24. A Boxplot A boxplot www.drjayeshpatidar.blogspot.com 31
  25. 25. Visualization of Data Dispersion: Boxplot Analysis www.drjayeshpatidar.blogspot.com 32
  26. 26. Mining Descriptive Statistical Measures in Large Databases • Variance 1 n 1  1 2 2 2 s   ( xi  x )   xi  n  xi   n  1 i 1 n 1   2 • Standard deviation: the square root of the variance – Measures spread about the mean – It is zero if and only if all the values are equal – Both the deviation and the variance are algebraic www.drjayeshpatidar.blogspot.com 33
  27. 27. Histogram Analysis • Graph displays of basic statistical class descriptions – Frequency histograms • A univariate graphical method • Consists of a set of rectangles that reflect the counts or frequencies of the classes present in the given data www.drjayeshpatidar.blogspot.com 34
  28. 28. Quantile Plot • Displays all of the data (allowing the user to assess both the overall behavior and unusual occurrences) • Plots quantile information – For a data xi data sorted in increasing order, fi indicates that approximately 100 fi% of the data are below or equal to the value xi www.drjayeshpatidar.blogspot.com 35
  29. 29. Quantile-Quantile (Q-Q) Plot • Graphs the quantiles of one univariate distribution against the corresponding quantiles of another • Allows the user to view whether there is a shift in going from one distribution to another www.drjayeshpatidar.blogspot.com 36
  30. 30. Scatter plot • Provides a first look at bivariate data to see clusters of points, outliers, etc • Each pair of values is treated as a pair of coordinates and plotted as points in the plane www.drjayeshpatidar.blogspot.com 37
  31. 31. Loess Curve • Adds a smooth curve to a scatter plot in order to provide better perception of the pattern of dependence • Loess curve is fitted by setting two parameters: a smoothing parameter, and the degree of the polynomials that are fitted by the regression www.drjayeshpatidar.blogspot.com 38
  32. 32. Graphic Displays of Basic Statistical Descriptions • • • • • • Histogram: (shown before) Boxplot: (covered before) Quantile plot: each value xi is paired with fi indicating that approximately 100 fi % of data are  xi Quantile-quantile (q-q) plot: graphs the quantiles of one univariant distribution against the corresponding quantiles of another Scatter plot: each pair of values is a pair of coordinates and plotted as points in the plane Loess (local regression) curve: add a smooth curve to a scatter plot to provide better perception of the pattern of dependence www.drjayeshpatidar.blogspot.com 39
  33. 33. Proportion • • • • =COUNT =COUNTIF DIVIDE COUNTIF BY COUNT =D3/D2
  34. 34. Frequency Distributions • There are alternative ways of constructing frequency distributions • COUNTIF function • HISTOGRAM function
  35. 35. =COUNTIF(A6:A134,1) =D4/D9*100
  36. 36. Histogram Function • Tools -Data Analysis-Histogram • Bins
  37. 37. The bins are the frequency categories
  38. 38. Insert Input and Bin Ranges
  39. 39. Text Labels Can Be Included or Excluded From Input Range
  40. 40. The Chart Wizard
  41. 41. The Descriptive Statistics Function
  42. 42. SEVERAL ROWS OF DATA ARE HIDDEN
  43. 43. SEVERAL ROWS OF DATA ARE HIDDEN
  44. 44. Correlation
  45. 45. Correlation Coefficient, r = .75
  46. 46. Regression Analysis

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