ITDE 7003004: Research

1
ITDE 7003/004 Research Evaluation I
CLUSTER 22 GROUP 2 / UNIT 2
PRESENTATION OF DATA AND DATA ANALYSIS
2
GROUP 2 IS
Alisa Cooper
Cathi Dunkle
Sherlyn Jackson
Arizona Florida Michigan Pennsylvania Rhode Island
Norman LaPolice
Aretha Vernette
3
MODULE 1 PART 4
INTRODUCTION TO STATISTICS
DESCRIPTIVE and INFERENTIAL STATISTCS
ENCOMPASSES THE TWO MAIN CATEGORIES OF STATISTICS
4
DESCRIPTIVE STATISTICS
DESCRIBE DISTRIBUTIONS OF DATA

• Simple summaries about the samples measures
• Simple graphics analysis
• Form the basis for quantitative analysis of data

ENABLES COMPARISON ACROSS UNITS
5
DESCRIPTIVE STATISTIC EXAMPLE
BATTING AVERAGE
NUMBER OF HITS NUMBER OF TIMES AT BAT
.250 one in four
6
CENTRAL TENDENCY
A point of distribution that best describes its
center
THE 3 MEASURES OF CENTRAL TENDENCY

• MEAN arithmetic average
• (all data points divided by number of cases)
• MEDIAN- point at which half the cases fall
  above or
• below (less sensitive to outliers)
• MODE- the most common value in a distribution

7
DEVIATION or VARIABILITY
METHODS OF ASSESSING VARIABILITY
RANGE- computed by subtracting the lowest value
from the highest then adding 1 STANDARD
DEVIATION- a measure of the spread or
dispersion of a set of data VARIANCE - square
of the standard deviation Sum of a number of
different variables, each with its own variance
and weight
8
CORREALATION
A RELATIONSHIP BETWEEN 2 OR MORE VARIABLES
DEPENDING ON THE DATA TYPE, SOME OF THE
MEASUREMENT TESTS INCLUDE

• Pearson Product-Moment
• Point Biserial
• Thurstone Rank Order

9
INFERENTIAL STATISTICS
MAKE INFERENCES ABOUT HOW SAMPLES RELATE TO
POPULATIONS

• Data from samples extrapolated to
• populations
• Goal of IS is PREDICTION
• Extend beyond immediate data
• Forms a family of statistical
• models

10
SAMPLES
Any group selected from a POPULATION for study
RANDOM SAMPLE
When every member of a POPULATION has an equal
chance of being made a member of the SAMPLE
11
POPULATIONS
The group about which predictions are made
INFERENTIAL STATISTICS ARE PERFORMED ONSAMPLES-
THE RESULTS ARE INFERRED TOPOPULATIONS
EXAMPLES OF POPULATIONS

• All the people in North America
• All the women in Florida
• All the birds on an island

12
MODULE 1 PART 5
RAW DATA FREQUENCY DATA
DATA TYPES
Quantitative data (numerical values)

• Discrete (count) data
• Continuous (measuring) data

• Qualitative data
• Nominal data (names or labels)
• Ordinal Data (rank of values)

13
OTHER DATA TYPES
Categorical Data data that can be put in
categories Example characteristic of 'gender'
with categories 'male' and 'female Interval
Scale a scale of measurement where the distance
between any two adjacent units of measurement
(or 'intervals') is the same
14
FREQUENCY TABLES
A way of organizing the data by listing every
possible score
Shoe size example W width G gender
15
Relative Frequency Distribution
A tabular summary of a set of data
The first step in drawing a frequency
distribution is to construct a frequency table
16
MODULE 4
Introduction to Inferential Statistical Analysis
PART 4
SIGNIFICANCE LEVEL
17
A Priori Probability
A priori probability is the probability
estimate prior to receiving new information
18
TYPE I ERROR
In a test of significance, Type I error is the
error of rejecting the null hypothesis when it
is true of saying an effect or event is
statistically significant when it is not.
19
NULL HYPOTHESIS
In hypothesis testing, the null hypothesis is the
one you are hoping can be disproven by the
observed data
20
ALTERNATIVE HYPOTHESIS
In hypothesis testing, the hypothesis which
competes with the null hypothesis as an
explanation for observed data is called the
Alternative hypothesis.