Excel and research

USING MICROSOFT
EXCEL WITH BUSINESS
RESEARCH METHODS
www.drjayeshpatidar.blogspot.com

TITLE BAR
MENU BAR
STANDARD TOOLBAR
FORMATTING TOOLBAR

FORMULA BAR

ACTIVE CELL

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)

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

• 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


26

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


27

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)

28

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

29

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

30

A Boxplot
A boxplot


31

Visualization of Data Dispersion:
Boxplot Analysis


32

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


33

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


34

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


35

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


36

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


37

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


38

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
39

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

Text Labels Can Be Included
or Excluded From Input Range

The Descriptive Statistics
Function

SEVERAL ROWS OF DATA ARE HIDDEN

Correlation Coefficient, r = .75

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