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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