Chapter 1 statistics
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CHAPTER 1 STATISTICS. Statistics is a way of reasoning, along with a collection of tools and methods, designed to help us understand the world. READ THE BOOK. Think Show Tell For Example Step-by-Step What can go wrong* What have we learned?. CHAPTER 2 DATA.

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Chapter 1 statistics


Statistics is a way of reasoning, along with a collection of tools and methods, designed to help us understand the world

Read the book


  • Think

  • Show

  • Tell

  • For Example

  • Step-by-Step

  • What can go wrong*

  • What have we learned?

Chapter 2 data


  • Information together with its context

    • Numerical

    • Names

    • Labels

  • Five W’s

    • Who, What, When, Where, Why

    • How

Chapter 1 statistics


  • Respondents: Individuals who answer a survey

  • Subjects or Participants: People on whom we experiment. (Experimental Units)

  • Records or Cases: Rows in a database or data table. Individuals about whom or about which, we have the data.

Chapter 1 statistics


  • Variables

    • Characteristics recorded about each individual. These are usually columns in a data table, and they should have a name that identifies what has been measured.

      • Categorical (or Qualitative)

      • Quantitative (Numerical values with measurement units)

      • Ordinal

More w s

…more W’s

  • Where and When?

    • Country? Year?

  • How?

    • How the data was collected?

  • Why?

    • Reason for the study



  • Investments. According to an article in Fortune (Dec.28, 1992), 401(K) plans permit employees to shift part of their before-tax salaries into investments such as mutual funds. Employers typically match 50% of the employees’ contribution up to about 6% of salary. One company, concerned with what it believed was a low employee participation rate in its 401(k) plan, sampled 30 other companies with similar plans and asked for their 401(k) participation rates.

Identify the w s

Identify the W’s

  • Who ?

    • 30 Companies

  • What ?

    • Participation Rates

      • Quantitative (Units : Percent)

  • When ?

    • Sometime after 1992

Identify the w s cont

Identify the W’s (cont.)

  • Where ?

    • USA

  • Why ?

    • The company was concerned with its participation rate compared with other companies

  • How ?

    • Companies were sampled using an unspecified method



  • Flowers. In a study appearing in the journal Science a research team reports that plants in southern England are flowering earlier in the spring. Records of the first flowering dates for 385 species over a period of 47 years indicate that flowering has advanced an average of 15 days per decade, an indication of climate warming according to the authors.

Identify the w s1

Identify the W’s

  • Who ?

    • 385 species of flowers over 47 years

  • What ?

    • First flowering date

      • Quantitative (Units : days)

  • When ?

    • Not specified

Identify the w s cont1

Identify the W’s (cont.)

  • Where ?

    • Southern England

  • Why ?

    • Researchers associate this behavior with climate warming

  • How ?

    • Observation. ( Method not specified)

Chapter 3 displaying and describing categorical data

Chapter 3. Displaying and Describing Categorical Data

  • Make a picture

  • First Make piles

    • Organize the counts by categories in a frequency table (counts) or a relative frequency table (percentages)

    • Both types of tables describe the distribution of the categorical variable because they name the possible categories and tell how frequently each occurs

The area principle

The Area Principle

  • The area occupied by a part of the graph. It should correspond to the magnitude of the value it represents

Bar charts

Bar Charts

  • A bar chart displays the distribution of a categorical variable, showing the counts for each category next to each other for easy comparison.

Pie charts

Pie Charts

  • Relative proportion (percentages instead of counts).

  • Pie charts show the whole group of cases as a circle, each of the pieces has a size proportional to the fraction of the whole in each category.

Contingency tables

Contingency Tables

  • Two categorical variables

Marginal and conditional distributions

Marginal and Conditional distributions

  • Marginal Distribution

    • Distribution of either variable alone (at the margin of the table)

  • Conditional Distribution

    • A distribution in one variable for only those individuals satisfying some condition on another variable.

  • Note : If the distribution of one variable is the same for all categories of another we say that the variables are independent.



  • Step-by-Step page 31

  • What can go wrong

    • Check the charts on pages 34

    • Simpson’s Paradox (page 35)

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