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Statistics Week 1

by: Danielle Adams

Statistics Week 1 sta2023

Marketplace > University of Florida > sta2023 > Statistics Week 1
Danielle Adams

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About this Document

These notes are for the first lesson. They cover 1.1-1.2 from the WorkBook.
Maria Ripoli
Class Notes
intro to statistics, Math, Lecture Notes
25 ?





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This 2 page Class Notes was uploaded by Danielle Adams on Wednesday May 11, 2016. The Class Notes belongs to sta2023 at University of Florida taught by Maria Ripoli in Spring 2016. Since its upload, it has received 28 views.


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Date Created: 05/11/16
Chapter 1 Statistics: The Art and Science of Learning From Data 1.1 Using Data to Answer Statistical Questions What is statistics? The art and science of learning from data — designing studies, analyzing the data, and translating data into knowledge and understanding of the world around us. Who uses statistics? It seems that nearly everyone in every field uses statistics in some way shape or form. Some popular examples are sports, medical studies, and marketing. In sports, players are analyzed and give “stats” which represent how good or bad they are at something. Baseball relies on players batting averages to determine how well they perform when hitting. Medical studies utilize statistics almost every day. They use them when determining how effective a treatment is compared to a previously relied on treatment and how effective it would be to replace the current treatment with the new one. Marketing is the final top relier on statistics. Everything from what color package sells better to what gender is going to respond better to this advertisement. Why use statical methods? Design: The design is how the experimenters plan to obtain the data for their study. Description: The description is the summary of the data obtained in the study with graphs and numerical summaries. Inference: Inference is using data from a random and representative sample to draw conclusions about the population of interest. Inference can only be applied to the population the random sample represents — meaning the inference cannot apply to the entirety of something unrelated. 1.2 Sample vs. Population Data sets consist of many different aspects (key vocabulary terms for the course): Subjects: whomever the objects in the experiment/study are Variables: What we are measuring. This could be a characteristic or an ability or anything Population: all the subjects of interest Sample: Subjects that we have received data from Random Sampling: We can use random samples to represent the population as a whole because everyone in said population had an equal chance of being included in the study. Parameter: the numerical study of the population Statistics: The numerical summary for the sample Example Provided in Work Book: Internet sites report that about 13% of Americans are left-handed. Is this true for students at your university? During the statistics exam, the instructor walks around the room and counts 15 left-handed students out of 98 students in the class. Identify the following: Variable: dominant hand — left or right Population: UF — all students Sample: 98 students in statistics exam Parameter: 13% is assumed to be the percent at UF who are left handed Statistic: 15% (15/98) of students in the class are left handed Was random sampling used? No, students opted to be in the statistics class. Each student at UF did not have the same chance/opportunity of being selected for this study.


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