Microsoft Office Tutorials and References
In Depth Information
PivotTables and PivotCharts allow the analyst to view the interaction of several
variables in a data set. To do so, it is often necessary to convert data elements that are
collected in surveys into values that permit easier manipulation—e.g. we converted
Income and Age into categorical data. This does not suggest that we have made an
error in how we collected data; on the contrary, it is often advantageous to collect
data in its purest form (e.g. 23 years of age) versus providing a category value (e.g.
the 19-24 years of age category). This allows detailed uses of the data that may not
be anticipated.
In the next chapter we will begin to apply more sophisticated statistical tech-
niques to qualitative data. These techniques will permit us to not only study the
interaction between variables, but they also allow us to quantify how confident we
are that the conclusions we reach are indeed applicable to a population of interest.
Among the techniques we will introduce are Analysis of Variance (ANOVA), tests
of hypothesis with t-tests and z-tests, and chi-square tests. These are powerful sta-
tistical techniques that can be used to study the effect of independent variables on
dependent variables, and determine similarity or dissimilarity in data samples. When
used in conjunction with the techniques we have learned in this chapter, we are
capable of uncovering the complex data interactions that are essential to successful
decision making.
Key Terms
Data Errors
Error Checking
EXACT (text1, text2)
TRUE/FALSE
OR, AND, NOT, TRUE, FALSE
MOD (number, divisor)
Cross-tabulation
PivotTable / PivotChart
Data Scrubbing
Mutually Exclusive
Collectively Exhaustive
Data Area
Count
Page, Column, Row, and Data
Sum, Count, Average, Min, Max
COUNTIF (range, criteria)
Grand Totals
Sampling
Market Segmentation
Problems and Exercises
1. Data errors are of little consequence in data analysis—T or F.
2. What does the term data scrubbing mean?
3. Write a logical if function for a cell (A1) that tests whether or not a cell contains
a value larger than or equal to 15 or less than 15. Return phrases that say “15 or
more” or “less than 15.”
4. For Exhibit 5.1, write a logical IF function in the cells H2:I4 that calculates the
difference between Original Data Entry and Secondary Data Entry for each
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