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STOR 155 Introductory StatisticsPowerPoint Presentation

STOR 155 Introductory Statistics

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STOR 155 Introductory Statistics

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The UNIVERSITY of NORTH CAROLINA

at CHAPEL HILL

STOR 155 Introductory Statistics

Lecture 1: Overview

Lecture 1

- Contact Charlotte Rogers:
Hanes 321, 962-2307,

crogers@email.unc.edu

- Fill out some paperwork with her to be put on the waiting list.

Lecture 1

- Stay active/involved in class.
- Ask questions during class (especially if you do not understand something).
- Do not feel shy or stupid.

- Answer questions to help other students if you can.
- Keep pace with the lectures, review daily, do homework after each lecture to help understand the materials.
- Make effective use of office hours (Instructor and IA) and open tutorial sessions
- Help you to answer questions about homework and lectures
- Private time vs. public time

Lecture 1

- "Data! Data! Data!" he cried impatiently. "I can't make bricks without clay."
----Sherlock Holmes

The Adventures of the Copper Beeches

- "Data! Data! Data!" he yelled loudly. "I can't teach Statistics without Data."
----Instructor

Introductory Statistics

Lecture 1

Population

Inference about population (using statistical tools)

Sample of data

- Statistics: the science of collecting, organizing, and interpreting data.
- data = information

Lecture 1

- claims that it contains 1000 chips.Is this true?
- Among a group of randomly chosen people, how likely is it for two of them to have the same birthday?
- What is the relationship between Income and Years of Education?
- Design your own experiment, collect data, analyze data and draw conclusions.

Lecture 1

- Parents and teachers have been concerned about the trend of declining SAT scores and sought ways to halt the decline.
- One question: the effect of classroom atmosphere (strict or liberal).
- To answer the question, 50 students (24 males and 26 females) participated in a study on student performance, as measured by SAT scores at the end of the school year.
- The students were divided into two groups of 25 each (12 males and 13 females), with Group 1 to study under a strict atmosphere while Group 2 under a very permissive atmosphere.
- They were matched according to socio-economic background.

Lecture 1

- After nine months, all students were given the same standardized tests: the verbal test and the mathematics test.

Lecture 1

- This example involves data collection, data analysis,andstatistical inference.
- How?

- Questions:
- Does stricter classroom atmosphere increase the average score?
- Why “matched according to socio-economic background”?
- Why “12 males and 13 females per group”?
- Is the group size 50 large enough to make a confident conclusion?

Lecture 1

- Population: the entire group of individuals that we want information about.
- Students (who are about to take SAT)

- Sample: a part of the population that we actually examine in order to gather information.
- those students selected into the study

- Sample size: number of observations/individuals in a sample.
- 50

- Statistical inference: to make an inference about a population based on the information contained in a sample.
- Based on the data from the study, to infer whether a stricter classroom atmosphere increases SAT scores in general.

Lecture 1

- A parameter is a value that describes the population. It’s fixed but unknown in practice.
- the average SAT score of all the students, who are about to take SAT.

- A statistic is a value that describes a sample. It’s known once a sample is obtained.
- the average SAT score of all the students, who are selected into the study.
- asample analogy of the parameter.

Lecture 1

- Suppose you are interested in finding out the average SAT score of UNC unders,
- The SAT scores of all UNC unders in STOR155
- The SAT scores of all UNC unders

- Suppose you are interested in finding out the average SAT score of US unders,
- The SAT scores of all UNC unders
- The SAT scores of all US unders

Lecture 1

- Statistics is the science of data:
- Collecting
- Analyzing
- Decision making
= Information processing

- Fundamental concepts:
- Population, parameter,sample, statistic, sample size

- You can do a LOT with statistics … what ?

Lecture 1

- Interested in population, but it’s too large to become known completely
- Statisticians work on sample, which is a smaller and observable ``proxy’’
- There is uncertainty in this transition, hence errors are inevitable …
- That’s why statistical methods are needed …

Lecture 1