what is statistics l.
Download
Skip this Video
Loading SlideShow in 5 Seconds..
What is Statistics? PowerPoint Presentation
Download Presentation
What is Statistics?

Loading in 2 Seconds...

play fullscreen
1 / 30

What is Statistics? - PowerPoint PPT Presentation


  • 481 Views
  • Uploaded on

What is Statistics? Definition of Statistics Statistics is the science of collecting, organizing, analyzing, and interpreting data in order to make a decision. Branches of Statistics

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about 'What is Statistics?' - Anita


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
what is statistics
What is Statistics?

Definition of Statistics

    • Statistics is the science of collecting, organizing, analyzing, and interpreting data in order to make a decision.
  • Branches of Statistics
    • The study of statistics has two major branches – descriptive(exploratory) statistics and inferential statistics.
      • Descriptive statistics is the branch of statistics that involves the organization, summarization, and display of data. In this course, from chapter 1 through Chapter 5, they are talking about Descriptive statistics.
      • Inferential statistics is the branch of statistics that involves using a sample to draw conclusions about population. A basic tool in the study of inferential statistics is probability. In this course, starting from Chapter 9, they are talking about inferential statistics.
chapter outline
Chapter outline
  • Individuals and variables
  • Categorical variables:
    • Pie Charts and bar graphs
  • Quantitative variables:
    • Histograms
  • Interpreting histograms
  • Quantitative variables: Stemplots
  • Time plots
examining distributions introduction
Examining Distributions- Introduction
  • Definitions:
    • Individuals: the objects described by a set of data
    • Variable: any characteristic of an individual
examples
Examples
  • College student data: every currently enrolled student – date of birth, gender, major, GPA and so on
  • Employee data: every employee – age, gender, salary, job type
variables
Variables
  • Categorical variable: categories, groups
  • Quantitative variable: numerical values
  • Distribution of a variable: what values it takes and how often it takes these values
examples7
Examples
  • College student data: every currently enrolled student – DOB, gender, major, GPA, and so on
  • Employee data: every employee – age, gender, salary, job type
  • We can see distributions easily using graphs. It is possible to see distributions using numbers which describe the data.
slide9
Exploratory data analysis describes the main feature of data.
    • 1. Examine each variable
    • 2. Study the relationships among the variables
    • 3. Start with graphs and add some numerical

summeries.

categorical variables bar graphs and pie charts
Categorical variables--- bar graphs and pie charts
  • Distribution of categorical variables categories by relevant count or percent of individuals.
  • Graphs: bar graph, pie chart
    • Pie chart: figure 1.1 (P. 7)/ must include all categories
    • Bar graph: figure 1.2 (P. 8)/heightindividual’s weight

[gaps between bars and order is not important.]

    • Note: It’s only for single variable now (for example: college major, tire model, final exam grade).
quantitative variables histograms
Quantitative variables: histograms
  • How to make histograms
    • Step 1. Choose the classes. Divide the range of the data into classes of equal width.
    • Step 2. Count the individuals in each class.
    • Step 3. Draw the histogram.
  • Example 1.3
interpreting histograms
Interpreting histograms
  • Interpretation: What do we see?

Overall pattern and striking deviations.

    • Overall pattern

Shape, center, spread: symmetric, skewed to the right/left, clustered.

    • striking deviations

Outlier

quantitative variables stemplots
Quantitative variables: stemplots
  • Another way to display a distribution of quantitative variables.
  • How to make stemplots
    • 1. Sort data in increasing order first
    • 2. Separate each observation into a stem consisting of all but the final digit, and a leaf, the final digit.
    • 3. Write the stems in a vertical column with the smallest at the top, and draw a vertical line at the right of this column
    • 4. Write each leaf in the row to the right of its stem, in increasing order out from the stem.
quantitative variables stemplots22
Quantitative variables: stemplots
  • Data: 80, 52, 86, 94, 76, 48, 92, 69, 79, 45
    • Step 1. Sort data in increasing order first
    • Step 2. Decide stem
    • Step 3. Fill in leaves
examples and exercises
Examples and Exercises
  • Example 1.7 (P. 16) using Table 1.1 (P. 10)
  • Example 1.8 (P. 16)
slide24
Tips
  • 1. Rounding
  • 2. Splitting stems
quantitative variables stemplots25
Quantitative variables: stemplots
  • For small data sets, it is quicker to make and presents more detailed information
  • You keep data values
time plots
Time plots
  • It is for variables which are measured at intervals over time.
    • Example 1. The cost of raw materials for a manufacturing process each month.
    • Example 2. The price of a stock at the end of each day.
time plots27
Time plots
  • To display change over time, make a time plot. Plot each observation against the time at which it was measured
    • 1. Put time on the horizontal scale
    • 2. Put the variable on the vertical scale
    • 3. Connect the data points by lines
  • Special case: time series (for regularly measured variable)
  • You can see: 1 )seasonal variation, 2) trend
free tutoring
Free tutoring

The Math Assistance Complex (MAC) 122 Kell Hall

  • MAC website:(online tutoring available) www.gsu.edu/~wwwclc/mathlab.htm