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4. Use data analysis to generate further questions of interest. In the case of the
teen’s data, we made no distinction between male and female teens, or the actual
ages of the teens. It is logical to believe that a 13 year old female web visitor
may behave quite differently than a 19 year old male. This data may be available
for analysis and it may be of critical importance for understanding behavior.
Often our data is in qualitative form rather than quantitative, or is a combination
of both. In the next chapter, we perform similar analyses on qualitative data. It is
important to value both data types equally, because they can both serve our goal of
gaining insight. In some cases, we will see similar techniques applied to both types
of data, but in others, the techniques will be quite different. Developing good skills
for both types of analyses is important for anyone performing data analysis.
Key Terms
Add-in
Series
Treatment
Time Series Data
Cross-sectional Data
Cyclicality
Seasonality
Leading
Trend
Linear Trend
E-tailer
Page-views
Frequency Distribution
Central Tendency
Variation
Descriptive Statistics
Mean
Standard Deviation
Population
Range
Median
Mode
Standard Error
Sample Variance
Kurtosis
Skewness
Systematic Behavior
Linear Regression
Dependent Variable
Independent Variable
Simple Linear Regression
Beta
Alpha
R-square
Residuals
Significance F
Covariance
Correlation
Perfectly Positively Correlated
Perfectly Negatively Correlated
Winters’ 3-factor Exponential
Smoothing
Exponential Smoothing
Level of Confidence
t-Test
t-Test: Paired Two Sample for Mean
Type 1 Error
t-Stat
Critical Value
Test of Hypothesis
Null Hypothesis
One-tail Test
Two-tail Test
ANOVA
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