DR SUSANNE HANSEN SARAL
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DR SUSANNE HANSEN SARAL
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Displaying data – shape of distributions. Week 3 (1) презентация
Содержание
- 1. Displaying data – shape of distributions. Week 3 (1)
- 4. Bar Chart – categorical data DR
- 5. Describing distributions
- 6. Describing distributions – what to pay
- 7. Describing the shape of distributions We
- 10. Skewed distribution The thinner parts of a
- 11. Right skewed distributions Examples
- 12. Left skewed distributions Example
- 17. Graphs to Describe Time-Series
- 18. Graphs to Describe Time-Series
- 19. Line Chart (time series plot) One variable DR SUSANNE HANSEN SARAL, SUSANNE.SARAL@GMAIL.COM
- 20. Line Chart (time
- 21. Line Chart (time series
- 22. Presenting statistical charts and graphs When
- 23. Manipulation of data Data can
- 24. Manipulation of data DR SUSANNE HANSEN SARAL
- 25. Identical data - different graph
- 27. Manipulation of data DR SUSANNE HANSEN SARAL
- 28. Manipulation of data
- 30. Histogram with equal interval width
- 32. Contingency table
- 33. Contingency table
- 34. Contingency table
- 35. Contingency table in percent A) What percent
- 36. Contingency table A) What
- 39. In this situation-beverage marketshare Which of the
Histogram of employee completion times
Numerical data DR
Слайд 1BBA182 Applied Statistics Week 3 (1) Displaying data – shape of distributions
Слайд 2 Histogram of employee completion times
Numerical data
DR SUSANNE HANSEN SARAL, SUSANNE.SARAL@GMAIL.COM
Слайд 3 Numerical data
Employee completion time
Cumulative frequency
DR SUSANNE HANSEN SARAL, SUSANNE.SARAL@GMAIL.COM
Слайд 4 Bar Chart – categorical data
DR SUSANNE HANSEN SARAL, SUSANNE.SARAL@GMAIL.COM
Hospital Number
Unit of Patients
Cardiac Care 1,052
Emergency 2,245
Intensive Care 340
Maternity 552
Surgery 4,630
Unit of Patients
Cardiac Care 1,052
Emergency 2,245
Intensive Care 340
Maternity 552
Surgery 4,630
Слайд 5 Describing distributions
Once we have made a
picture of our numerical data, the histogram, what can we say about it’s shape?
DR SUSANNE HANSEN SARAL, SUSANNE.SARAL@GMAIL.COM
Слайд 6 Describing distributions –
what to pay attention to!
Pay attention to:
its’
shape
its’ center
Its’ spread
its’ center
Its’ spread
DR SUSANNE HANSEN SARAL, SUSANNE.SARAL@GMAIL.COM
Слайд 7 Describing the shape of distributions
We describe the shape of a distribution
in terms of:
Modes
Symmetry
Gaps or outlying values
Modes
Symmetry
Gaps or outlying values
DR SUSANNE HANSEN SARAL, SUSANNE.SARAL@GMAIL.COM
Слайд 8
Mode
Does the distribution have one peak (mode) or several peaks (several modes)?
Uni-modal: one mode
Bi-modal: Two modes
Multi-modal: More than two modes
DR SUSANNE HANSEN SARAL, SUSANNE.SARAL@GMAIL.COM
Слайд 9
Symmetry
If we can make a mirror image of the distribution, we have a symmetric distribution
DR SUSANNE HANSEN SARAL, SUSANNE.SARAL@GMAIL.COM
Слайд 10 Skewed distribution
The thinner parts of a distribution are called tails.
A
distribution is skewed, or asymmetric, if one tail stretches farther out on one side than on the other side of the center.
A right skewed distribution has a tail that extends farther to the right.
A left skewed distribution has a tail that extends farther to the left.
A right skewed distribution has a tail that extends farther to the right.
A left skewed distribution has a tail that extends farther to the left.
DR SUSANNE HANSEN SARAL, SUSANNE.SARAL@GMAIL.COM
Слайд 11 Right skewed distributions
Examples
Employee salaries in a company
Waiting times
in a line
DR SUSANNE HANSEN SARAL, SUSANNE.SARAL@GMAIL.COM
Слайд 12 Left skewed distributions
Example
Time to finish an exam
Employees going
home after work
Customers going shopping in a shopping center on a Saturday
Customers going shopping in a shopping center on a Saturday
DR SUSANNE HANSEN SARAL, SUSANNE.SARAL@GMAIL.COM
Слайд 13
Outliers
Outliers are extreme data points in a data set that are not close to the majority of the other data points
Example:
Age of 10 people in a restaurant:
24 19 21 65 20 21 23 20 24 25
DR SUSANNE HANSEN SARAL, SUSANNE.SARAL@GMAIL.COM
Слайд 14
Outliers
If you are studying the personal wealth of Americans in 2010 and you have Bill Gates (Founder of Microsoft) in your sample.
How would the personal wealth of Bill Gates affect the distribution of personal wealth of Americans in the sample?
DR SUSANNE HANSEN SARAL, SUSANNE.SARAL@GMAIL.COM
Слайд 15
Outliers
Outliers will affect the shape of a distribution:
DR SUSANNE HANSEN SARAL, SUSANNE.SARAL@GMAIL.COM
Слайд 16
Outliers
Outliers can affect almost every statistical method we use in Statistics.
Therefore we need to look out for them.
An outlier can be the most informative part in your data or it may just be an error.
No matter what it is, you need to look at it critically and judge if it is important for our analysis.
DR SUSANNE HANSEN SARAL, SUSANNE.SARAL@GMAIL.COM
Слайд 17 Graphs to Describe
Time-Series Data
A histogram can provide information
about the distribution of a variable, but it cannot show any pattern of the data over time.
Sometimes we need to analyze data over time.
A graph of values against time is called a times series plot
Sometimes we need to analyze data over time.
A graph of values against time is called a times series plot
DR SUSANNE HANSEN SARAL, SUSANNE.SARAL@GMAIL.COM
Слайд 18 Graphs to Describe
Time-Series Data
A time-series plot is used
to show the values of a variable ordered over time.
Time is measured on the horizontal axis
The variable of interest is measured on the vertical axis
Used to monitor the evolution of a certain item of interest, such as evolution of the price of gas, annual interest rates, daily closing prices for shares of common stock, evolution of home prices in a certain region, exchange rates (Euro-TL, TL-$), etc.
Time is measured on the horizontal axis
The variable of interest is measured on the vertical axis
Used to monitor the evolution of a certain item of interest, such as evolution of the price of gas, annual interest rates, daily closing prices for shares of common stock, evolution of home prices in a certain region, exchange rates (Euro-TL, TL-$), etc.
DR SUSANNE HANSEN SARAL, SUSANNE.SARAL@GMAIL.COM
Слайд 20 Line Chart (time series plot)
Two variables
DR SUSANNE HANSEN SARAL, SUSANNE.SARAL@GMAIL.COM
Слайд 21 Line Chart (time series plot)
Two variables
DR SUSANNE HANSEN SARAL, SUSANNE.SARAL@GMAIL.COM
Слайд 22 Presenting statistical charts and graphs
When presenting data for an audience or
a manager your charts and graphs MUST give as clear and accurate picture of the data as possible.
The graphs and charts must be:
Convincing
Clear
Truthful
The graphs and charts must be:
Convincing
Clear
Truthful
DR SUSANNE HANSEN SARAL
Слайд 23 Manipulation of data
Data can be manipulated in graphical techniques in such
a way that they look more/less favorable than they are in reality. This gives misleading information about the data.
You need to be critical whenever you are presented a graph, pie-chart, histogram, etc.
You should also be careful not to construct misleading information with graphical techniques.
You need to be critical whenever you are presented a graph, pie-chart, histogram, etc.
You should also be careful not to construct misleading information with graphical techniques.
2/15/2017
Слайд 31 Data
Presentation Errors
Do not make a histogram of categorical data
Unequal histogram interval widths
Label the x-axis and y-axis clearly (identify
variables clearly)
Compressing or distorting the vertical axis
Do not calculate numerical summaries of categorical data, such as code, telephone numbers, etc.
DR SUSANNE HANSEN SARAL
(continued)
Слайд 32 Contingency table
Class exercise
A survey of the entering MBA students at a university in the US reported the following data on the gender of their students in their two MBA programs:
What are the two variables under study?
Слайд 33 Contingency table
How many students are surveyed?
A) How many of all MBA students are women?
B) How many of Two-year MBAs are women?
C) How many of Evening MBAs are men?
D) How many of all MBAs are men?
Слайд 35 Contingency table in percent
A) What percent of all MBA students are
women?
B) What percent of Two-year MBAs are women?
C) What percent of Evening MBAs are men?
D) What percent of all MBAs are men?
B) What percent of Two-year MBAs are women?
C) What percent of Evening MBAs are men?
D) What percent of all MBAs are men?
Слайд 36 Contingency table
A) What percent of all MBA students
are women?
B) What percent of Two-year MBAs are women?
C) What percent of Evening MBAs are men?
D) What percent of all MBAs are men?
B) What percent of Two-year MBAs are women?
C) What percent of Evening MBAs are men?
D) What percent of all MBAs are men?
Слайд 37 Displaying categorical data -exercise
Softdrink
market share
A local survey company conducted a survey on the consumption of soft drinks its region of operations. The results of the survey were summarized in the following pie-chart:
A) Which soft-drink brand has the highest consumption?
B) Is this a good method to display this data?
A local survey company conducted a survey on the consumption of soft drinks its region of operations. The results of the survey were summarized in the following pie-chart:
A) Which soft-drink brand has the highest consumption?
B) Is this a good method to display this data?
Слайд 38 Displaying categorical data -exercise
Softdrink
market share ( same data as in the preceding slide)
A local survey company conducted a survey on the consumption of soft drinks its region of operations. The results of the survey were summarized in the following pie-chart:
A) Compared to the pie chart in the preceding slide, which
chart is better for displaying the relative proportion (per-
cent) of market share
B) Which chart gives the best visual picture of the data?
A local survey company conducted a survey on the consumption of soft drinks its region of operations. The results of the survey were summarized in the following pie-chart:
A) Compared to the pie chart in the preceding slide, which
chart is better for displaying the relative proportion (per-
cent) of market share
B) Which chart gives the best visual picture of the data?
Слайд 39In this situation-beverage marketshare Which of the two graphs gives the best
picture of the data?
Слайд 40 Are there any
outliers in the
following data sets?
If yes, explain:
A) 15 21 20 54 18 17 22 22
B) 345 340 339 344 338 341 343
C) – 21 -23 -25 -18 -20 -63 -19 -22
Слайд 41 How would the
outliers affect the mean of the data set?
Would the outlier increase or decrease the mean of the respective datasets?
A) 15 21 20 54 18 17 22 22
B) 345 340 339 344 338 341 343
C) – 21 -23 -25 -18 -20 -63 -19 -22
