Review of Modern Data Analysis: A First Course in Applied Statistics.

1991 ◽  
Vol 36 (2) ◽  
pp. 168-168
Author(s):  
Lawrence J. Stricker
Keyword(s):  
Data Analysis ◽  
Applied Statistics

Why Data Analysis?

1990 ◽  
Vol 83 (2) ◽  
pp. 90-93
Author(s):  
Richard L. Scheaffer
Keyword(s):  
Data Analysis ◽  
Empirical Data ◽  
Exploratory Data Analysis ◽  
Applied Statistics ◽  
Statistical Education ◽  
The Past ◽  
Classical Statistics ◽  
Exploratory Data ◽  
To Come ◽  
Oriented Approach

Recent years have witnessed a strong movement away from what might be termed classical statistics to a more empirical, data-oriented approach to statistics, sometimes termed exploratory data analysis, or EDA. This movement has been active among professional statisticians for twenty or twenty-five years but has begun permeating the area of statistical education for nonstatisticians only in the past five to ten years. At this point, there seems to be little doubt that EDA approaches to applied statistics will gain support over classical approaches in the years to come. That is not to say that classical statistics will disappear. The two approaches begin with different assumptions and have different objectives, but both are important. These differences will be outlined in this article.

Improving the Teaching of Applied Statistics: Putting the Data Back into Data Analysis

1990 ◽  
Vol 44 (3) ◽  
pp. 223 ◽  
Author(s):  
Judith D. Singer ◽  
John B. Willett
Keyword(s):  
Data Analysis ◽  
Applied Statistics

Modern Data Analysis: A First Course in Applied Statistics.

Biometrics ◽  
1993 ◽  
Vol 49 (2) ◽  
pp. 673 ◽  
Author(s):  
L. C. Hamilton
Keyword(s):  
Data Analysis ◽  
Applied Statistics

scholarly journals Engaging Underrepresented High School students in Data Driven Storytelling: An Examination of Learning Experiences and Outcomes for a Cohort of Rising Seniors Enrolled in the Gaining Early Awareness and Readiness for Undergraduate Program (GEAR UP)

2017 ◽  
Vol 5 (4) ◽  
pp. 54 ◽  
Author(s):  
Lisa Dierker ◽  
Nadia Ward ◽  
Jalen Alexander ◽  
Emmanuel Donate
Keyword(s):  
High School ◽  
High School Students ◽  
Data Analysis ◽  
Real World ◽  
Research Question ◽  
Learning Experiences ◽  
Project Based Learning ◽  
Student Interest ◽  
Applied Statistics ◽  
School Students

Background: Upward trends in data-oriented careers threaten to further increase the underrepresentation of both females and individuals from racial minority groups in programs focused on data analysis and applied statistics. To begin to develop the necessary skills for a data-oriented career, project-based learning seems the most promising given its focus on real-world activities that are aimed at engaging student interest and enthusiasm. Method: Using pre and post survey data, the present study examines student background characteristics, learning experiences and course outcomes for a cohort of 33 rising high school seniors involved in a two-week, accelerated version of a project-based data analysis and applied statistics curriculum. Results: On average, students rated the experience as rewarding and the vast majority (78.1%) felt that they had accomplished more than they had expected. Based on responses to both the pre and post course surveys, roughly half of the students reported increases in confidence in applied skills (i.e. developing a research question, managing data, choosing the correct statistical test, effectively presenting research results, and conducting a statistical analysis of data), while more than 80% reported increased confidence in writing code to run statistical analyses. Fully 84.4% of students reported interest in one or more follow-up courses with interest in computer programming being endorsed by the largest number of students (53.1%). Conclusions: These findings support previous research showing that real-world, project-based experiences afford the best hope for achieving the kind of analytic and statistical literacy necessary for meaningful engagement in research, problem solving and professional development.

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