1. STAT 4030 – Programming in R STATISTICS MODULE: Basic Data Analysis
Jennifer Lewis Priestley, Ph.D.
Kennesaw State University 1

2. STATISTICS MODULE
Basic Descriptive Statistics and Confidence Intervals
Basic Visualizations
Histograms
Pie Charts
Bar Charts
Scatterplots
Ttests
One Sample
Paired
Independent Two Sample
ANOVA
Chi Square and Odds
Regression Basics 2 2 2

3. Statistics Module: Descriptive Statistics
Center, or where do we find most of the data
Distribution or shape, such as a bell shaped curve
Variation or dispersion, how far spread out is the data, on average, how far are observations from the center?
Outliers…do we have points that are so unusual that we need to address them separately? 3

4. Statistics Module: Descriptive Statistics
The “center” of a data set can be described using three different measures:
Mean – the commonly known “average”
Median – the midpoint
Mode – the most frequently occurring value
Without any additional information, the “center” of the data is the expected value of any observation pulled at random. 4

5. Statistics Module: Descriptive Statistics5

6. Statistics Module: Descriptive Statistics
In a symmetric, bell shaped distribution we typically describe the entire distribution using only two numbers: the mean and the standard deviation.
The standard deviation is roughly the average distance that observations are from their mean: 6

7. Statistics Module: Descriptive Statistics
The Empirical Rule
For any normal curve, approximately
68% of the values fall within 1 standard deviation of the mean
95% of the values fall within 2 standard deviations of the mean
99.7% of the values fall within 3 standard deviations of the mean
Using this logic, what is the definition of an outlier? 7

8. Statistics Module: Descriptive Statistics
Boxplots are helpful to visualize all of these at the same time:
Minimum
Lower Quartile
Median
Upper Quartile
Maximum + *
Mean
Outlier
Inter-Quartile Range 8

9. Statistics Module: Descriptive Statistics
When developing a visual representation of a single variable, the most common tools are – Histograms, Pie Charts, Bar Charts, Box Plots and Stem and Leaf Plots.
Visualizations are as much a part of the data discovery process as descriptive statistics. These visualizations will be addressed in a separate set of notes. 9