scholarly journals AMSM- Univariate Descriptive Statistics-Chapter Two

2018 ◽  
Author(s):  
Mohammed R. Dahman
Keyword(s):  
Probability Distribution ◽  
Population Sample ◽  
Descriptive Statistics ◽  
Central Tendency ◽  
Hands On

In this chapter a brief introduction of the descriptive statistics is discussed. Followed with thorough explanation of terminologies such as population, sample, etc. after that, I have discussed definitions and classifications of probability distribution. I have discussed all what you have to know about univariate descriptive statistics (i.e. central tendency, dispersion, and shape). Finally, we have had hands on practice The act or process of providing evidence for or showing the truth of something

scholarly journals AMSM- Multivariate Descriptive Statistics- Chapter Six

2018 ◽  
Author(s):  
Mohammed R. Dahman
Keyword(s):  
Multivariate Analysis ◽  
Correlation Matrix ◽  
R Package ◽  
Descriptive Statistics ◽  
Central Tendency ◽  
Hands On ◽  
Mean Vector

This is the first chapter summary in the domain of multivariate analysis. We have introduced the concept of multivariate analysis through data-matric, and variable vector. The transformation from univariate domain and measurement of central tendency, as well as dispersion, to the multivariate dimension. Followed by the definition and the calculation of mean vector. Furthermore, we have discussed the steps of finding the covariance and correlation matrix between variables. Hands on practice was the last section of this chapter. We have done few examples using R package to cover what we have learned till this point.

The Greyhound Strike: Using a Labor Dispute to Teach Descriptive Statistics

1985 ◽  
Vol 12 (2) ◽  
pp. 85-86 ◽  
Author(s):  
Mark A. Shatz
Keyword(s):  
Small Groups ◽  
Descriptive Statistics ◽  
Labor Dispute ◽  
Data Sets ◽  
Central Tendency ◽  
Comparable Data ◽  
Labor Management ◽  
Simulation Exercise

A simulation exercise of a labor-management dispute is used to teach students some of the basics of descriptive statistics, such as measures of central tendency and the nature of distributions. Using comparable data sets generated by the instructor, students work in small groups to develop a statistical presentation that supports their particular position in the dispute.

15. Quantitative Analysis: Description and Inference

2020 ◽  
pp. 393-421
Author(s):  
Sandra Halperin ◽  
Oliver Heath
Keyword(s):  
Quantitative Analysis ◽  
Standard Deviation ◽  
Confidence Intervals ◽  
Quantitative Research ◽  
Building Blocks ◽  
Descriptive Statistics ◽  
Central Tendency ◽  
Margin Of Error ◽  
Univariate Statistics ◽  
The Mean

This chapter deals with quantitative analysis, and especially description and inference. It introduces the reader to the principles of quantitative research and offers a step-by-step guide on how to use and interpret a range of commonly used techniques. The first part of the chapter considers the building blocks of quantitative analysis, with particular emphasis on different ways of summarizing data, both graphically and with tables, and ways of describing the distribution of one variable using univariate statistics. Two important measures are discussed: the mean and the standard deviation. After elaborating on descriptive statistics, the chapter explores inferential statistics and explains how to make generalizations. It also presents the concept of confidence intervals, more commonly known as the margin of error, and measures of central tendency.

On the wind resource in Algeria: Probability distributions evaluation

2020 ◽  
pp. 095765092097588
Author(s):  
Houdayfa Ounis ◽  
Nawel Aries
Keyword(s):  
Wind Speed ◽  
Probability Distribution ◽  
Probability Distributions ◽  
Resource Assessment ◽  
Descriptive Statistics ◽  
The Other ◽  
Wind Speeds ◽  
Wind Resource Assessment ◽  
The Mean ◽  
Wind Resource

The present study aims to present a contribution to the wind resource assessment in Algeria using ERA-Interim reanalysis. Firstly, the ERA-Interim reanalysis 10 m wind speed data are considered for the elaboration of the mean annual 10 m wind speed map for a period starting from 01-01-2000 to 31-12-2017. Moreover, the present study intends to highlight the importance of the descriptive statistics other than the mean in wind resource assessment. On the other hand, this study aims also to select the proper probability distribution for the wind resource assessment in Algeria. Therefore, nine probability distributions were considered, namely: Weibull, Gamma, Inverse Gaussian, Log Normal, Gumbel, Generalized Extreme Value (GEV), Nakagami, Generalized Logistic and Pearson III. Furthermore, in combination with the distribution, three parameter estimation methods were considered, namely, Method of Moment, Maximum Likelihood Method and L-Moment Method. The study showed that Algeria has several wind behaviours due to the diversified topographic, geographic and climatic properties. Moreover, the annual mean 10 m wind speed map showed that the wind speed varies from 2.3 to 5.3 m/s, where 73% of the wind speeds are above 3 m/s. The map also showed that the Algerian Sahara is windiest region, while, the northern fringe envelopes the lowest wind speeds. In addition, it has been shown that the study of the mean wind speeds for the evaluation of the wind potential alone is not enough, and other descriptive statistics must be considered. On the other hand, among the nine considered distribution, it appears that the GEV is the most appropriate probability distribution. Whereas, the Weibull distribution showed its performance only in regions with high wind speeds, which, implies that this probability distribution should not be generalized in the study of the wind speed in Algeria.

scholarly journals Profiling students’ environmental care attitudes taught using Sasak Tribe local wisdom-integrated model

2019 ◽  
Vol 5 (3) ◽  
Author(s):  
H. Hunaepi ◽  
Ika Nuraini Dewi ◽  
S. Sumarjan
Keyword(s):  
Sampling Technique ◽  
Integrated Model ◽  
Descriptive Statistics ◽  
Central Tendency ◽  
Mean Values ◽  
Environmental Care ◽  
Descriptive Research ◽  
Analysis Technique ◽  
The Mean

Sasak Tribe possesses unique local wisdom which is potential to be utilized in term of improving students' care attitudes toward the environment. This study aimed at profiling students' evironmental attitudes who were taught using Sasak Tribe local wisdom-integrated model. This descriptive research was designed with a one-shot case study. The sample used in this study was 140 VII graders chosen using random sampling technique. The sample comprised of 3 classes of SMPN 2 Gunung Sari and 3 classes of SMPN 3 Lingsar placed in West Nusa Tenggara-Indonesia. The data analysis technique used was descriptive statistics in term of central tendency. The measured indicators were environmental awareness (EA), developing empathy (DE), and effect handling (EH). The results showed that the students live in suburb area tended to have better environmental care attitudes, in which the mean values were 86.66 (EA), 75.69 (DE), and 42.66 (EH) compared to those who live in urban area in which the mean values were 68.73 (EA), 57.07 (DE), and 30.62 (EH). Based on this findings, further evaluation in several aspects should be done.

Descriptive Statistics: Measures of Central Tendency

2021 ◽  
pp. 61-76
Author(s):  
Alese Wooditch ◽  
Nicole J. Johnson ◽  
Reka Solymosi ◽  
Juanjo Medina Ariza ◽  
Samuel Langton
Keyword(s):  
Descriptive Statistics ◽  
Central Tendency

Popular Measures of Central Tendency

1990 ◽  
Vol 83 (9) ◽  
pp. 744-746
Author(s):  
Robert G. Mogull
Keyword(s):  
Location Parameter ◽  
Arithmetic Mean ◽  
Descriptive Statistics ◽  
Applied Statistics ◽  
Central Tendency ◽  
Introductory Textbooks

Introductory textbooks in applied statistics typically present a discussion of descriptive statistics as well as of inferential techniques. The presentations include measures of central tendency, such as the arithmetic mean, the median, and the mode, with definitions and examples given for each location parameter.

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