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STATISTICS ELEMENTARY MARIO F. TRIOLA EIGHTH EDITION

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**STATISTICS ELEMENTARY MARIO F. TRIOLA**

Chapter Introduction to Statistics MARIO F. TRIOLA EIGHTH EDITION

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**Introduction to Statistics**

Chapter 1 Introduction to Statistics 1-1 Overview 1-2 The Nature of Data 1-3 Uses and Abuses of Statistics 1-4 Design of Experiments

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**Statistics Two Meanings Specific numbers 1-1 Overview**

Method of analysis

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**Statistics Specific number**

numerical measurement determined by a set of data Example: Twenty-three percent of people polled believed that there are too many polls.

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**Statistics Method of analysis**

a collection of methods for planning experiments, obtaining data, and then then organizing, summarizing, presenting, analyzing, interpreting, and drawing conclusions based on the data page 4 of text

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**Definitions Population**

the complete collection of all elements (scores, people, measurements, and so on) to be studied. The collection is complete in the sense that it includes all subjects to be studied.

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**Definitions Census Sample**

the collection of data from every element in a population Sample a subcollection of elements drawn from a population Emphasize that a population is determined by the researcher, and a sample is a subcollection of that pre-determined group. For example, if I collect the ages from a section of elementary statistics students, that data would be a sample if I am interested in studying ages of all elementary statistics students. However, if I am studying only the ages of the specific section of elementary statistics, the data would be a population.

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The Nature of Data

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**Definitions Parameter**

a numerical measurement describing some characteristic of a population page 5 of text

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**Definitions Parameter population parameter**

a numerical measurement describing some characteristic of a population population parameter

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**Definitions Statistic**

a numerical measurement describing some characteristic of a sample

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**Definitions Statistic sample statistic**

a numerical measurement describing some characteristic of a sample sample statistic

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**Definitions Quantitative data**

numbers representing counts or measurements page 6 of text

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**Definitions Quantitative data**

numbers representing counts or measurements Qualitative (or categorical or attribute) data can be separated into different categories that are distinguished by some nonnumeric characteristics

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Definitions Quantitative data the incomes of college graduates

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**Definitions Quantitative data**

the incomes of college graduates Qualitative (or categorical or attribute) data the genders (male/female) of college graduates

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Definitions Discrete data result when the number of possible values is either a finite number or a ‘countable’ number of possible values 0, 1, 2, 3, . . .

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**Definitions Discrete Continuous**

data result when the number of possible values is either a finite number or a ‘countable’ number of possible values 0, 1, 2, 3, . . . Continuous (numerical) data result from infinitely many possible values that correspond to some continuous scale that covers a range of values without gaps, interruptions, or jumps Understanding the difference between discrete versus continuous data will be important in Chapters 4 and 5. When measuring data that is continuous, the result will be only as precise as the measuring device being used to measure. 2 3

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Definitions Discrete The number of eggs that hens lay; for example, 3 eggs a day.

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Definitions Discrete The number of eggs that hens lay; for example, 3 eggs a day. Continuous The amounts of milk that cows produce; for example, gallons a day.

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**Definitions nominal level of measurement**

characterized by data that consist of names, labels, or categories only. The data cannot be arranged in an ordering scheme (such as low to high) Example: survey responses yes, no, undecided page 7 of text

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**Definitions ordinal level of measurement**

involves data that may be arranged in some order, but differences between data values either cannot be determined or are meaningless Example: Course grades A, B, C, D, or F Understanding the differences between the levels of data will help students later in determining what type of statistical tests to use. Nominal and ordinal data should not be used for calculations (even when assigned ‘numbers’ for computerization) as differences and magnitudes of differences are meaningless.

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**Definitions interval level of measurement**

like the ordinal level, with the additional property that the difference between any two data values is meaningful. However, there is no natural zero starting point (where none of the quantity is present) Example: Years 1000, 2000, 1776, and 1492 Students usually have some difficulty understanding the difference between interval and ratio data. Fortunately, interval data occurs in very few instances.

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**Definitions ratio level of measurement**

the interval level modified to include the natural zero starting point (where zero indicates that none of the quantity is present). For values at this level, differences and ratios are meaningful. Example: Prices of college textbooks

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**Levels of Measurement Nominal - categories only**

Ordinal - categories with some order Interval - differences but no natural starting point Ratio - differences and a natural starting point review of four levels of measurement

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**Levels of Measurement Nominal - categories only**

Ordinal - categories with some order Interval - differences but no natural starting point Ratio - differences and a natural starting point

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Section 1-3 Types of Data.

Section 1-3 Types of Data.

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