Excel and research
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Excel and research

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Excel and research Excel and research Presentation Transcript

  • USING MICROSOFT EXCEL WITH BUSINESS RESEARCH METHODS www.drjayeshpatidar.blogspot.com
  • TITLE BAR MENU BAR FORMULA BAR STANDARD TOOLBAR FORMATTING TOOLBAR ACTIVE CELL
  • PASTE FUNCTION TOOLS MENU
  • The Paste Function Provides Numerous Statistical Operations
  • The Statistical Function Category
  • Data Analysis Dialog Box • Click on “Tools” • Select “Data Analysis” • Select statistical operation o such as Histogram
  • 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.
  • 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
  • Easy to Use Paste Functions • AVERAGE (MEAN) • MEDIAN • MODE • SUM • STANDARD DEVIATION
  • 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.
  • The Equal Sign Then The Function Name And Arguments • =FUNCTION (Argument1) • =FUNCTION (Argument1,Argument2)
  • Arguments • Typical arguments are numbers, text, arrays, and cell references. • Arguments can also be constants, formulas, or other functions.
  • The AVERAGE Function Located in the Statistical Category
  • Data Array • The data appear in cells A2 through 14 • A2:A14 • Sometimes written with dollars signs • $A$2:$A$14
  • Sum, Average, and Standard Deviation • =FUNCTION (Argument1) • =SUM(A2:A9) • =AVERAGE(A2:A9) • =STDEVA(A2:A9)
  • SUM Function Sales Call Example
  • AVERAGE (Mean) Function Sales Call Example
  • Standard Deviation Function Sales Call Example Variance s2: (algebraic, scalable computation) Standard deviation s is the square root of variance s2          n i n i ii n i i x n x n xx n s 1 1 22 1 22 ])( 1 [ 1 1 )( 1 1
  • • 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 26www.drjayeshpatidar.blogspot.com
  • 27 Data Dispersion Characteristics • Motivation – To better understand the data: central tendency, variation and spread • Data dispersion characteristics – 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
  • 28 Measuring the Central Tendency • Mean – Weighted arithmetic mean • Median: A holistic measure – 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:   n i ix n x 1 1      n i i n i ii w xw x 1 1 )(3 medianmeanmodemean  www.drjayeshpatidar.blogspot.com
  • 29 Measuring the Dispersion of Data • Quartiles, outliers and boxplots – Quartiles: Q1 (25th percentile), Q3 (75th percentile) – 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 – Outlier: usually, a value higher/lower than 1.5 x IQR • Variance and standard deviation – Variance s2: (algebraic, scalable computation) – Standard deviation s is the square root of variance s2          n i n i ii n i i x n x n xx n s 1 1 22 1 22 ])( 1 [ 1 1 )( 1 1 www.drjayeshpatidar.blogspot.com
  • 30 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
  • 31 A Boxplot A boxplot www.drjayeshpatidar.blogspot.com
  • 32 Visualization of Data Dispersion: Boxplot Analysis www.drjayeshpatidar.blogspot.com
  • 33 Mining Descriptive Statistical Measures in Large Databases • 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               22 1 22 1 1 1 )( 1 1 ii n i i x n x n xx n s www.drjayeshpatidar.blogspot.com
  • 34 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
  • 35 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
  • 36 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
  • 37 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
  • 38 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
  • 39 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
  • Proportion • =COUNT • =COUNTIF • DIVIDE COUNTIF BY COUNT • =D3/D2
  • Frequency Distributions • There are alternative ways of constructing frequency distributions • COUNTIF function • HISTOGRAM function
  • =COUNTIF(A6:A134,1) =D4/D9*100
  • Histogram Function • Tools -Data Analysis-Histogram • Bins
  • The bins are the frequency categories
  • Insert Input and Bin Ranges
  • Text Labels Can Be Included or Excluded From Input Range
  • The Chart Wizard
  • The Descriptive Statistics Function
  • SEVERAL ROWS OF DATA ARE HIDDEN
  • SEVERAL ROWS OF DATA ARE HIDDEN
  • Correlation
  • Correlation Coefficient, r = .75
  • Regression Analysis