The document covers key concepts in data analysis including variable types (quantitative and qualitative), analysis types (observational studies and experiments), and methods for exploring numerical and categorical variables. It explains various statistical measures such as mean, median, mode, and robust statistics, as well as data visualization techniques like histograms and contingency tables. Additionally, it introduces foundational concepts in probability, inference, and hypothesis testing.

1. Data Analysis
Spotlights - I
M.Elkharashy

2. Introduction
● Variables Types
● Analysis Types
● Random Samlping Vs Assignment
● Exploring Numerical Variables
o Mesures of Center
o Robust Statistics
● Exploring Categorical Variables

3. Variables Types
Quantitive/Numberical
● Continuous
● Discreate
Qualitative/Categorical
● Regural
● Ordinal
Notes:
It can be represented by numbers, but
without any arthimatic operations.
Ordinal: variables that have ordered levels.

4. Variables Types
Dependent/Associated
● +ve association
● -ve association
Independant

7. Analysis Types
Observational Study
● Merely “Observe”
● Retrospective/Prespective use
data (from past/ throughout the
study)
● Can only establish an
association between the
explooanatory and response
variables.
Experiment
● Randomly assign subjects to
various treatments.
● Can establish causal connections
between the explanatory and
response variables.

9. Random Sampling
Random
Assignment

10. ideal experiment
most experiments
most
opeservational
studies
bad observational
studies

11. Exploring Numerical
Variables

12. Dot Plot
Useful when individual values are of interest

13. Histogram

15. Box Plot
Useful for highlighting outliers, median, and the
interquartile range.

17. Intensity Map
Useful for highlighting the spacial distribution

18. Mesures of Center

19. Mesures of Center
Sample Statistics/Point Estimates
● mean: arithmetic average
● median: midpoint of the distn. (50th
percentile)
● mode: most frequent observation

20. Mesures of Center

21. Skewness Vs Mesures of Center

22. Robust Statistics

23. Robust Statistics
As a mesure on which extreme observation have little effect

24. Robust Statistics
As a mesure on which extreme observation have little effect

25. Exploring Categorical
Variables

26. Exploring Categorical Variables
● Exploring single categorical variable.
● Exploring the relationship between two
categorical variables
● Exploring the relationship between a
numberical variable and categorical
variable

27. 1- Single Categorical Variable
Frequency Table & Bar Plot

28. 1- Single Categorical Variable
Pie Chart

29. Contingency Table
2- Relationship between 2 categorical variables

30. Segmented Bar Plot
2- Relationship between 2 categorical variables

31. Relative Freq. Segmented Bar Plot
2- Relationship between 2 categorical variables

32. Mosaicplot
2- Relationship between 2 categorical variables

33. Side-by-Side Box Plots
3- Rel. bet. numberical & categorical variables

34. ● Disjoint events Vs independent process
● Conditional probability & probability trees
● Normal distribution
● Binomial distribution
● Centeral Limit Theorem (CLT)
● Confidence Interval
● Introductoin to Inference
In Next Session

35. ● Another introduction to inference
● Hypothesis testing(for mean)
o p-value
o test statistics (Z & T)
o ANOVA
● Hypothesis testing(for proportion)
o Chi-Square (GOF & Independence Test)
● Frequentist Vs Bayesian Inference
● Linear Regression
In 3rd Session

36. Questions?