1. USING MICROSOFT
EXCEL WITH BUSINESS
RESEARCH METHODS
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2. TITLE BAR
MENU BAR
FORMULA BAR
STANDARD TOOLBAR
FORMATTING TOOLBAR
ACTIVE CELL

3. PASTE FUNCTION
TOOLS MENU

4. The Paste Function Provides
Numerous Statistical
Operations

5. The Statistical Function
Category

6. Data Analysis
Dialog Box
• Click on “Tools”
• Select “Data Analysis”
• Select statistical operation
o such as Histogram

10. 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.

11. 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

12. Easy to Use Paste Functions
• AVERAGE (MEAN)
• MEDIAN
• MODE
• SUM
• STANDARD DEVIATION

13. 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.

14. The Equal Sign Then The
Function Name And
Arguments
• =FUNCTION (Argument1)
• =FUNCTION (Argument1,Argument2)

15. Arguments
• Typical arguments are numbers, text,
arrays, and cell references.
• Arguments can also be constants,
formulas, or other functions.

16. The AVERAGE Function
Located in the Statistical Category

18. Data Array
• The data appear in cells A2 through 14
• A2:A14
• Sometimes written with dollars signs
• $A$2:$A$14

20. Sum, Average, and Standard
Deviation
• =FUNCTION (Argument1)
• =SUM(A2:A9)
• =AVERAGE(A2:A9)
• =STDEVA(A2:A9)

23. SUM Function
Sales Call Example

24. AVERAGE (Mean) Function
Sales Call Example

25. Standard Deviation Function
Sales Call Example
Variance s2: (algebraic, scalable computation)
Standard deviation s is the square root of variance s2
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26. • 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. 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. 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:
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www.drjayeshpatidar.blogspot.com

29. 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
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www.drjayeshpatidar.blogspot.com

30. 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
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31. 31
A Boxplot
A boxplot
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32. 32
Visualization of Data Dispersion:
Boxplot Analysis
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33. 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
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www.drjayeshpatidar.blogspot.com

34. 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. 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. 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. 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. 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
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39. 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

40. Proportion
• =COUNT
• =COUNTIF
• DIVIDE COUNTIF BY COUNT
• =D3/D2

43. Frequency Distributions
• There are alternative ways of
constructing frequency distributions
• COUNTIF function
• HISTOGRAM function

45. =COUNTIF(A6:A134,1)
=D4/D9*100

49. Histogram Function
• Tools -Data Analysis-Histogram
• Bins

51. The bins are the
frequency
categories

52. Insert Input and Bin Ranges

53. Text Labels Can Be Included
or Excluded From Input Range

54. The Chart Wizard

56. The Descriptive Statistics
Function

64. SEVERAL ROWS OF DATA ARE HIDDEN

65. SEVERAL ROWS OF DATA ARE HIDDEN

71. Correlation

74. Correlation Coefficient, r = .75

75. Regression Analysis