This chapter discusses numerical descriptive measures used to describe data, including measures of central tendency (mean, median, mode), variation (range, variance, standard deviation, coefficient of variation), and shape. It provides definitions and formulas for calculating these measures, as well as examples of interpreting and comparing them. The mean is the most common measure of central tendency, while the standard deviation is generally the best measure of variation. Measures of central tendency and variation are useful for summarizing and understanding the key properties of numerical data.
Descriptive Statistics are utilized to portray the fundamental highlights of the information in an examination. They give straightforward synopses about the example and the measures. Along with straightforward designs examination, they structure the premise of essentially every quantitative investigation of information.
Descriptive Statistics regularly recognized from inferential insights. With clear measurements, you are just depicting what is or what the information shows.
Basic Business Statistics Chapter 3Numerical Descriptive Measures
Chapters Objectives:
Learn about Measures of Center.
How to calculate mean, median and midrange
Learn about Measures of Spread
Learn how to calculate Standard Deviation, IQR and Range
Learn about 5 number summaries
Coefficient of Correlation
Descriptive Statistics are utilized to portray the fundamental highlights of the information in an examination. They give straightforward synopses about the example and the measures. Along with straightforward designs examination, they structure the premise of essentially every quantitative investigation of information.
Descriptive Statistics regularly recognized from inferential insights. With clear measurements, you are just depicting what is or what the information shows.
Basic Business Statistics Chapter 3Numerical Descriptive Measures
Chapters Objectives:
Learn about Measures of Center.
How to calculate mean, median and midrange
Learn about Measures of Spread
Learn how to calculate Standard Deviation, IQR and Range
Learn about 5 number summaries
Coefficient of Correlation
Measure of dispersion has two types Absolute measure and Graphical measure. There are other different types in there.
In this slide the discussed points are:
1. Dispersion & it's types
2. Definition
3. Use
4. Merits
5. Demerits
6. Formula & math
7. Graph and pictures
8. Real life application.
We know that frequency distributions serve useful purposes but there are many situations that require other type of data summarizations. What we need in many instances is the ability to summarize the data by means of a single number called a descriptive measure. Descriptive measures may be computed from the data of a sample or the data of a population.
Topic: Coefficient of Variance
Student Name: Shakeela
Class: B.Ed. 2.5
Project Name: “Young Teachers' Professional Development (TPD)"
"Project Founder: Prof. Dr. Amjad Ali Arain
Faculty of Education, University of Sindh, Pakistan
Measure of dispersion has two types Absolute measure and Graphical measure. There are other different types in there.
In this slide the discussed points are:
1. Dispersion & it's types
2. Definition
3. Use
4. Merits
5. Demerits
6. Formula & math
7. Graph and pictures
8. Real life application.
We know that frequency distributions serve useful purposes but there are many situations that require other type of data summarizations. What we need in many instances is the ability to summarize the data by means of a single number called a descriptive measure. Descriptive measures may be computed from the data of a sample or the data of a population.
Topic: Coefficient of Variance
Student Name: Shakeela
Class: B.Ed. 2.5
Project Name: “Young Teachers' Professional Development (TPD)"
"Project Founder: Prof. Dr. Amjad Ali Arain
Faculty of Education, University of Sindh, Pakistan
Frequencies, Proportion, Graphs
February 8th, 2016
Frequency, Percentages, and Proportions
Frequency number of participants or cases.
Denoted by the symbol f.
N can also mean frequency.
f=50 or N=50 had a score of 80. Both mean that 50 people had a score of 80.
Percentage, number per 100 who have a certain characteristic.
64% of registered voters are Democrats; each 100 registered voters 64 are Democrats
To determine how many are Democrats
Multiply the total number of registered voters by .64; if we have 2,200 registered voters; .64 X 2200=1408 are democrats.
2
Percentage and Proportions
Percentages
32 of 96 children reported that a dog is their favorite animal ; 32/96=.33*100=33% of these students like dogs.
Interpretation: Based on this sample, out of 100 participants from the same population, we can expect about 33 of them to report that dogs are their favorite animal.
Proportions is part of numeral 1.
Proportion of children who like dogs is .33
Meaning that 33 hundredths of the children like dogs.
Percentages are easier to interpret.
Percentages Cont’d
Good to report the sample size with the frequency
Percentages can help us understand differences between groups of individuals
College ACollege BNumber of Education MajorsN=500N=800Early Childhood EducationN= 400 (80%)N=600
(75%)
Shapes of Distributions
Frequency distribution
Number of participants have each score
Remember that we are describing our data
X (Score)f2522442352110207194181171N=34
Frequency Polygon
Histogram
Shapes of Distributions Cont’d
Normal Distribution
Most Important shape (shape found in nature)
Heights of 10 year old boys in a large population
Bell-shaped curve
Used for inferential Statistics
Skewed Distributions
Skew: most frequent scores are clustered at one end of the distribution
The symmetry of the distribution.
Positive skew (scores bunched at low values with the tail pointing to high values).
Negative skew (scores bunched at high values with the tail pointing to low values).
Consider how groups differ depending on their standard deviation.
68% of the cases lie within one standard deviation unit of the mean in a normal distribution.
95% of the cases lie within two standard deviation unit of the mean in a normal distribution
99% of the cases lie within three standard deviation unit of the mean in a normal distribution
Standard Deviation and the Normal Distribution
February 1, 2016
Descriptive Statistics
Number of Children in families
Order of finish in the Boston Marathon
Grading System (A, B, C, D, F)
Level of Blood Sugar
Time required to complete a maze
Political Party Affiliation
Amount of gasoline consumed
Majors in College
IQ scores
Number of Fatal Accidents
Level of Measurement Examples
2
Types of Statistics
We use descriptive statistics to summarize data
Think about measures of central tendency and variability
We use correlational statistics to describe the relationship between two variables
Considered as a ...
Biostatistics Survey Project on Menstrual cup v/s Sanitary PadsCheshta Rawat
Hey, this is a project survey conducted in Miranda House with random girls regarding whether menstrual cups are profitable or sanitary napkins.
Do read the conclusion.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
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.
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/
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.