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DESCRIBE THE ROLE OF
STATISTICAL ANALYSIS IN
INTERPRETING THE RESULTS
OF RESEARCH PROJECT
Ammara Mehwish2007-ag-1680
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What is Statistics?
Statistics derived from the Latin word status meaningpolitical state
The word statistics can either be singular or plural
Singular form- statistic Plural form-statistics
Mathematical science pertaining to the collection,analysis, interpretation or explanation, and presentationof data
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Statistics concerns with
Designing experiments and other data collectionSummarizing information to aid understanding
Drawing conclusions from data
Estimating the present or predicting the future.
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Uses of Statistics
Summarize or describe a collection of data
Useful in research, when communicatingthe results of experiments
Provides a prediction (based in data) for
where a theory logically leads
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Draw backs of statistics
if statistical conclusions are based on
incomplete information
Interpret only the data that are favorable to
the presenter
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Types of statistics
There are two types:
1. Descriptive statistics
(summarize collection of data)
2. Inferential statistics
(draw inferences about the populationbeing studied)
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Well known statistical tests
ANOVA (Analysis of variance)
Chi-square test
Regression
Correlation
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ANOVA;
A statistical model meant to analyze data
Chi-square test;The Greek letter chi (lower case)
A statistical test comparing observed
frequencies with the frequencies expectedfrom a given hypothesis
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Regression;
Effect of one or more function on a
single character
Correlation;
Degree of association between two
variables
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Data analysis
It is a process of
Gathering,
Modeling,
Transforming data
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Initial data analysis must be able to
answer
What is the quality of the data?
What is the quality of the
measurements?
Did the implementation of the study fulfill
the intentions of the research design?
What are the characteristics of the datasample?
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Analyzing and interpreting data
Process of assigning meaning to the
collected information and determining the
conclusions, significance, and
implications of the findings.
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There are several different classifications
that are used in interpreting data
Variable:Types;
Qualitative
Quantitative
1.Discrete
2.Continuous
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Qualitative Data
Describes the quality of something in a
non-numerical format
Interpretation of qualitative data
Frequency tables
Graphs
Bar Charts Pie Charts
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Bar chart
Color Preference of Customers
0
5
10
15
Red Blue Yellow Green
Color
N
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Pie charts
Pie Chart of Color Preferences
Red
44%
Blue
32%
Yellow
16%
Green
8%
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Quantitative Data
It arise when the observations arefrequencies or measurements.
Interpretation of quantitative data;
Frequency tables Graphs
Bar Charts Dot Plots
Pie Charts
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Method for increasing breast
meat yields in poultry
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Figure 2
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Figure 1 Explanation
It is a graphical representation of feed
conversion data from a feeding trial using
a control diet and diets supplemented with
alpha.-galactosidase at three differentconcentrations.
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Figure 2 Explanation
It is a graphical representation of feed
conversion data from a second feeding
trial using a control diet and diets
supplemented with .alpha.-galactosidaseat three different concentrations
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Line Charts
Frequency Table/Frequency Distribution Histograms
Stem and Leaf
Box plots Line Graphs
XY Scatter Charts
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XY-charts
Worker-Hours by Lot Size
0
20
40
60
80
100
120
140
160
180
0 20 40 60 80 100
Lot Size
Hours
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Histogram
150145140135130125120115110
5
4
3
2
1
0
Time
Frequency
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Different packages used in
statistics for analyzing data
Dfremel
SAS
MSTAT C SPSS
Minitab
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SAS
Access raw data files analyze data using
descriptive statistics
Manage data using tools for data
entry,editing,formating
Present data using reports and business
and statistical graphics
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SPSS
Research design
Sample size and selection
Mean,variance,standard deviation Analysis of variance
Corelation and Regression
Chisquare test
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MSTAT C
Used to simplify and enhance field and lab
research
Helps to generate the design of
experiments design
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Minitab
Anova
Regression of analysis
Design of experiments
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Dfreml
Estimation of covariance components and
the resulting genetic parameters for
continuous traits
Developed with the analysis of data from
animal breeding programs
Accommodate numerous traits, random
regression models, covariance structures
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