Chapter 1 Section 1.1
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Transcript of Chapter 1 Section 1.1
Displaying Distributions with Graphs
Chapter 1Section 1.1
the science of collecting, analyzing, and drawing conclusions from data
Statistics
Descriptive
Inferential
Main Types of Statistics
the methods of organizing & summarizing data
Descriptive Statistics
involves making generalizations from a sample to a population
Inferential Statistics
the entire collection of individuals or objects about which information is desired
Population
a subset of the population, selected for study in some prescribed manner
Sample
any characteristic whose value may change from one individual to another
Variable
observations on single variable or simultaneously on two or more variables
Data
Who?
What?
Why?
When you see a set of data, ask:
What individuals do the data describe?
How many individuals appear in the data?
Who?
How many variables?
What are the definitions of these variables?
What units?
What?
What is the reason data were gathered?
Why?
Types of Variables
Also called “qualitative”
Identify basic differentiating characteristics of the population
Categorical Variables
Also called “numerical”Observations or measurements that take on numerical values
Makes sense to average these values
Two types-discrete & continuous
Quantitative Variables
Listable set of values
Usually counts of items
Discrete (numerical)
Data can take on any value in the domain of the variable
Usually measurements of something
Continuous (numerical)
Univariate-data that describes a single characteristic of the population
Bivariate-data that describes two characteristics of the population
Multivariate-data that describes more than two characteristics
Classifications by the number of variables
Which category of variables would the following be?
GenderAgeHair colorSmokerSystolic blood pressureNumber of girls in class
Categorical or Quantitative
Types of Distributions4 Common Types
Tells us what values the variable takes and how often it takes these values
One variable may take values that are very close together while others might be spread out
Distribution
Refers to data in which both sides are (more or less) the same when the graph is folded vertically down the middle
Bell-shaped is a special typeHas a center mound with sloping tails
Symmetrical
Refers to data in which every class has equal or approximately equal frequency
Uniform
Refers to data in which one side (tail) is longer than the other side
The direction of the skewness is on the side of the longer tail
Skewed (left or right)
Skewed right Skewed left
Refers to data in which two or more classes have the largest frequency and are separated by at least one other class
Bimodal (multi-modal)
How to describe a graph
Normal, SymmetricalSkewedUniformBimodal
Shape of Graphs
Where the middle of the data falls
3 types of central tendencyMean, median, mode
Center of Graphs
Shows how spread out the data is
Refers to the variability of the data
Range, standard deviation, IQR
Spread
Outliers-value that lies away from the rest of the data
Clusters
Gaps
Unusual Occurrences
1. Bar2. Pie Chart3. Dotplot4. Stem-and-Leaf5. Histogram6. Relative cumulative frequency graph7. Time Plot8. Box & Whisker9. Scatter
Types of Graphs
Must label the axes and title the graph
Scale your axesDraw vertical bar above each category name to a height that corresponds to the count in that category
1. Bar Graphs
Bar Graph (example)The graph below shows the proportion of the female labor force aged 25 and older in the United States that falls into various educational categories. The coding used in the plot is as follows:
1. none-8th Grade 6. bachelor’s degree2. 9th grade-11th grade 7. master’s degree3. high school graduated 8. professional degree4. some college, no degree 9. doctorate degree5. associate degree
Proportion
Educational Attainment (women)
Must include all categories that make up a whole.
2. Pie Chart
3. Dotplot
0 100 200 300 400
Age (months)
Tally marks are made for each set of data.
The data below is graphed on the dotplot on the right.
195 194204 199204 204192 204192 192193 214209 222209
A stem and leaf plot displays data like a bar graph, but we break the data apart into stem and leaf plots.
The data point 30 is broken up into 3 0
stem leaf
The data point for 304 is broken up into 30 4
stem leaf
4. Stem and Leaf Plot
The data set {30, 27, 34, 28, 45, 31, 34, 40, 29}
is represented by:
2: 7 8 93: 0 1 4 44: 0 5
The 2 and 9 represents the data point 29. The stems are the tens place and the leaves are the ones digits. Notice numbers are listed in order from smallest to largest.
Stem and Leaf Plot
Draw a bar graph that represents the count in each class. Leave no horizontal space (unlike a bar graph).
The data below is graphed on the dotplot on the next slide.
195 194 204 199 204 204 192204 192 192 193 214 209 222 209
5. Histogram
Histogram (example)
192 198 204 210 216 222
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15 Students
Freq
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Age (Months)
Also called an Ogive.
A relative frequency histogram has the same shape and the same horizontal scale as the corresponding frequency histogram. The difference is that the vertical scale measures the relative frequencies (measured as a percentage), not frequencies.
6. Relative Cumulative Frequency Graph
Relative Cumulative Frequency Graph
192 198 204 210 216 222
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15 Students
Freq
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The frequency needs to be changed to percentages.
Place the time on the x axis. Time is the explanatory variable.
Place the observations on the y axis. The observations represent the response variable.
7. Time Plot
Time Plot (example)
These will be discussed later.Box and Whisker-Section 1.2Scatter Plot-Section 3.1
8. Box & Whisker/9. Scatter Plot
The distribution of a variable tells us what values it takes and how often it takes these values.
To describe a distribution, begin with a graph. Bar graphs and pie charts display the
distributions of categorical variables. These graphs use the counts or percents of the categories.
Stem & Leaf plots and histograms display the distributions of qualitative variables. Stem & Leaf Plots separate each observation into a stem and a one-digit leaf. Histograms plot the frequencies (counts) or percents of equal-width classes of values.
Summary
When examining a distribution, look for the shape, center and spread, and for clear deviations from the overall shape. Some distributions have simple shapes, such as symmetric or skewed. Others may be bimodal (more than one major peak).
Outliers are observations that lie outside the overall pattern of a distribution. Always look for outliers and try to explain them.
Summary continued…
A relative frequency graph (ogive) is a good way to see the relative standing of an observation.
When observations on a variable are taken over time, make a time plot that graphs horizontally and the values of the variable vertically. A time plot can reveal trends (patterns) or other changes over time.
Summary continued
On page 64 in your textbook, complete exercises 1.13 and 1.16.
Assignment