The document discusses introductory concepts in statistics, including defining statistics, distinguishing between descriptive and inferential statistics, and summarizing sources of data and types of data scales. It covers topics like explaining types of samples, describing the survey process and potential errors, and outlining statistical methods like descriptive statistics and inferential statistics. Various key terms are also defined, such as population, parameter, sample, and statistic.
Five Pitfalls when Operationalizing Data Science and a Strategy for SuccessVMware Tanzu
Enterprise executives and IT teams alike know that data science is not optional, but struggle to benefit from it because the process takes too long and operationalizing models in applications can be hairy.
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Presenter : Guest Speakers Mike Gualtieri, Forrester, Dormain Drewitz and Jeff Kelly, Pivotal
In this advanced business analysis training session, you will learn Data Analytics Business Intelligence. Topics covered in this session are:
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While there is a lot of buzz about advanced IT analytics (AIA), the global research from leading IT analyst firm Enterprise Management Associates (EMA) highlights what is actually going on, who is successful and why.
These slides - based on the webinar featuring Dennis Drogseth, VP of research at EMA - cover why successful AIA deployments leverage more 3rd party sources (the average is 15), support more roles (the average is 11), and why 96% of respondents want to integrate service interdependency data into their AIA solutions.
These slides cover:
**What are the more popular types of advanced IT analytics (AIA), and how are they being used?
**How have advanced IT analytics adoption patterns and priorities changed over the last 2 years?
**Who is driving AIA organizationally in the real world?
The t Test for Related Samples
The t Test for Related Samples
Program Transcript
MATT JONES: As its name implies, the independent samples t-test has the
assumption of the independence of observations. But that's not always the case.
Sometimes we take multiple observations of the same unit of analysis, such as a
person, over time. In this case, we'll use a paired sample t-test, sometimes
referred to as the dependent sample t-test. Let's go to SPSS to see how we do
this.
To perform the paired sample t-test in SPSS, we once again go to Analyze,
Compare Means, and down to the Paired Sample T-test. SPSS doesn't require
much information here;; only the pair of variables of which we would like to test.
We have a simulated data set here for statistical anxiety of students. Students
were provided with an instrument that measures their anxiety around statistical
topics on a number of different constructs-- teachers, interpretation, asking for
help, worth, and self-conceptualization.
They were given the test at the beginning of the class and at the conclusion of a
class. Hence, why in the value labels we see pre-test and post-test. As a teacher,
I might have some interest in determining whether students felt more comfortable
with me or had lowering anxiety over time. This is perfect for a paired sample t-
test. To perform this paired sample t-test, we'll go to Analyze, Compare Means,
the Paired Sample T-test.
SPSS doesn't ask for much information;; only the pair of variables of which I
would like to test. In this case, teacher pre-test and teacher post-test. So this is a
classic before and after. The first piece of output I obtain from the paired sample
t-test are some descriptive statistics, specifically around the pairwise comparison
I'm looking at, which is the teacher subscale pre-test and post-test.
I see that there is mean on the pre-test of 17.32 and on the post-test, an 18.44.
So it appears, at least from a descriptive sense, that there is a higher mean on
the post-test than the pre-test. On the instrument, higher scores on an item or the
subscale indicate higher levels of anxiety for that specific attitude. Except for this
specific subscale, fear of statistics teachers, where higher scores actually
indicate lower levels of anxiety.
So if post scores are higher than pre scores, that means on average, students
feel lower levels of anxiety and more positive attitude about their statistics
teacher. I can see here, at least from a descriptive sense, that that appears to be
the case. But from the sample, I am performing a test of statistical significance.
Next to the mean, I'm provided with the sample size 25-- 25 observations pre-test
and 25 observations post-test, all the same person-- the standard deviation for
the pre-test and the post-test, and the standard error of the mean. ...
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
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This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Data
facts or information that is relevant or appropriate to a decision maker
Population
the totality of objects under consideration
Sample
a portion of the population that is selected for analysis
Parameter
a summary measure (e.g., mean) that is computed to describe a characteristic of the population
Statistic
a summary measure (e.g., mean) that is computed to describe a characteristic of the sample
Data
facts or information that is relevant or appropriate to a decision maker
Population
the totality of objects under consideration
Sample
a portion of the population that is selected for analysis
Parameter
a summary measure (e.g., mean) that is computed to describe a characteristic of the population
Statistic
a summary measure (e.g., mean) that is computed to describe a characteristic of the sample
1. Gender = categorical - nominal
2. Weight = numerical - continuous - ratio
3. Auto Speed = numerical - continuous - ratio
4. Temperature = numerical - continuous - interval
5. # Siblings = categorical - ordinal
6. Letter Grade = categorical - ordinal
Question Content
Related to research purpose
Based on respondent’s ability to answer accurately
Response Format
open-ended Vs. fixed alternative (closed-ended)
Question Wording
simple, clear words
avoid leading questions: ‘In view of the health crisis, it would be best to nationalize the health industry.’
Question Sequence
use simple & interesting opening questions
general questions first
Layout
More important for Mail Survey than telephone survey
Pretest
Shows where you have asked ambiguous questions
Pragmatic Reasons
If Chrysler wished to census past purchasers’ reactions, millions of car buyers would have to be contacted
Accurate & Reliable Results
Reasonable accuracy though not perfect - sampling error!
May be more accurate than census since less chance of nonsampling errors (e.g., data entry)
Bureau of the Census uses samples to check the accuracy of the US Census. If the sample shows possible source of error, the census is redone.
Destruction of Test Units
e.g., Mean Life of Light Bulbs
Probability Samples
Selection is based on chance
Subjects are chosen based on some known probabilities
Eliminates or reduces bias
Random refers to procedure not the data:
The outcome cannot be predicted because it is dependent upon chance
Non Probability Samples
Do not have above characteristics
Done for time and convenience
Probability Samples
Selection is based on chance
Subjects are chosen based on some known probabilities
Eliminates or reduces bias
Random refers to procedure not the data:
The outcome cannot be predicted because it is dependent upon chance
Non Probability Samples
Do not have above characteristics
Done for time and convenience
Simple Random
Use random number table
Number of digits is determined by population size
Columns are 01, 02 etc. (aligned vertically)
Example
Population size is 50. Sample size is 10.
Since population size (50) has 2-digits, divide table into 2 digit numbers.
Begin top left (for convenience only).
1-49, 2-28, 3-08, 4-89 (skip) 4-24, 5-35, 6-77 (skip),
6-90 (skip) 6-02, 7-83 (skip) 7-61(skip) 7-87 (skip)
7-04, 8-16, 9-57 (skip) 9-07, 10-46.
Example
Population size is 100. Use 3 digit numbers.
Probability Samples
Selection is based on chance
Subjects are chosen based on some known probabilities
Eliminates or reduces bias
Random refers to procedure not the data:
The outcome cannot be predicted because it is dependent upon chance
Non Probability Samples
Do not have above characteristics
Done for time and convenience
Systematic
Requires all population elements
Bias may occur due to periodicity
In the telephone book example, unlisted numbers will not be found
Example:
Sampling frame is 100 individuals. You want to select 20. Select first name by random number, then every 5th person.
Probability Samples
Selection is based on chance
Subjects are chosen based on some known probabilities
Eliminates or reduces bias
Random refers to procedure not the data:
The outcome cannot be predicted because it is dependent upon chance
Non Probability Samples
Do not have above characteristics
Done for time and convenience
Stratified
Assures
1. Sample reflects population in terms of criterion used for stratifying.
2. More efficient sample - sampling error is reduced.
Example: College has 70% on-campus students and 30% commuters. A 100 student survey would get close to 70 on-campus students and 30 commuters. A simple random survey might get 60 on-campus and 40 commuting students.
Similar to Quota sampling except that a simple random sample is drawn from each strata.
Probability Samples
Selection is based on chance
Subjects are chosen based on some known probabilities
Eliminates or reduces bias
Random refers to procedure not the data:
The outcome cannot be predicted because it is dependent upon chance
Non Probability Samples
Do not have above characteristics
Done for time and convenience
Cluster
Idea is to sample economically yet retain characteristics of probability sample.
Ideally, cluster is as heterogeneous as the population.
Often, characteristics of elements in cluster may be similar.
Probability Samples
Selection is based on chance
Subjects are chosen based on some known probabilities
Eliminates or reduces bias
Random refers to procedure not the data:
The outcome cannot be predicted because it is dependent upon chance
Non Probability Samples
Do not have above characteristics
Done for time and convenience
Judgment
A fashion manufacturer selects key accounts to predict what will sell next season
Quota
Advantages are speed of data collection, lower costs, and convenience.
Often used in laboratory experiments
It is difficult to find a sample of the general population willing to visit a laboratory
Chunk (Convenience)
Street interviews at election time. Views represent supposedly the entire population.
Need impressions of text book in an hour. Use this class to represent all students.
Frame Error
The sampling frame is also called the ‘working population.’
Frame error is the discrepancy between population and sampling frame.
e.g., Not all students may be in phone book
Sampling Error
Sampling units may not perfectly represent the population.
All samples vary.
Sampling error is a function of sample size
Systematic (Nonresponse & Measurement) Error
Nonresponse, badly worded questions, interview error.