Download Lesson 1.2 Random vs Biased Samples Notes

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Lesson 1.2 Random vs Biased Samples Notes
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VOCABULARY…
Population: data from every individual of interest. (Every person or thing is
talked to or counted or included.)
Census: measurements or observation from the entire population are used.
Sample: measurements or observations from part of the population are used.
Random Sample: a sample in which every person or object has an equal
chance of being selected
Bias Sample: a sample in which every person or object does not have an
equal chance of being selected. (A biased sample, a non-random sample of
a population (or non-human factors) is a sample in which all individuals, or
instances, were not equally likely to have been selected.)
For Examples 1 – 3, determine which would be better to use: Census or
Sample
Example 1: Find the average number of hours of television a person in
Phoenix watches each day.
Example 2: Find the average number of hours of homework each student in
your math class gets each week.
Example 3: Find the average age of everyone who lives in Phoenix.
For Examples 4 – 7, identify whether each situation is a random or biased
sample:
Example 4: Determine the average annual snow fall in Colorado by
measuring the snow fall in Denver.
Example 5: Determine the average annual rain fall in Texas by measuring
the rain fall in twenty different cities across the state.
Example 6: Determine America’s favorite NBA basketball team by
conducting a survey in Los Angeles.
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Lesson 1.2 Random vs Biased Samples Notes
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Example 7: Determine the most popular type of car at Moon Valley by
surveying all the teachers.
CALCULATOR – Mathematical Notation…
∑
is the Greek letter “Sigma”. Sigma means to find the Summation.
Summation is the operation of adding a sequence of numbers; the result is
their sum or total.
There are many ways to do this. We will focus on two, with the calculator
and without the calculator.
Directions: Find the given sum.
X: 4 1 4 7 5
Y: 1 8 2 1 7
Example 8:  y (Solve with the calculator. List out the steps, so you can
repeat the outcome with a calculator.)
Example 9:
Example 10:
 x2
  2xy 
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Lesson 1.2 Random vs Biased Samples Notes
Example 11:
y2
x4
  x   y 
Example 12:
y4
2  xy
4
4 1
 xy 2 
Example 13:
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