Happy Friday Scientists!DQ:

Identify the IV, DV and constants in the following scenario: A study was done to find if fuel types affect the engine’s performance in a car.

Today:1. Review Density Lab2. What are the different types of data and which type of

graph should we use for each? (study guide)3. Data and Graph Types Practice4. Review IV/DV/Constants5. HW: 6. Return work and discuss grades L4

Variables and Constants

• Variable – Any factor that can change in a scientific investigation or experiment

Types of Variables

Independent• What the scientist chooses

or changes • Known before starting the

experiment.• The factor that is

intentionally changed by the investigator in an experiment

• There can be only one independent variable in an experiment

Dependent• The factor that you measure

or observe as data• The factor that will change

as a result of the independent variable

• This variable depends on the independent variable.

• There can be one or more dependent variables in an experiment

Constants

• Sometimes called controlled variables• The factors or conditions that are kept the

same (unchanged) in an experiment• There are many controlled variables in an

experiment.

Graphing Study Guide

Complete the handout!Are you ready

to go?

• There are 4 major types of graphs we’ll use this year.

I can remember

that

Types of Data

1. Discrete data = consist of categories (days of the week, kind of animal, color)

2. Continuous data = consist of measurements involving a scale with equal measurements; a range of values (temperature, length of time in hours, height in centimeters)

Four major types of graphing

1. Pie chart/graph = consist of categories (display percentages)

2. Bar graph = used to represent discrete data; the IV is typically qualitative and DV is typically quantitative

Four major types of graphing

3. Line graph = used to represent continuous data, usually tracking changes over time; the IV and DV are both quantitative and the line connecting the points has meaning

4. Scatter plot = similar to a line graph, but the data points are NOT connected because a connecting line would have no meaning; a line of best fit might be constructed to show a trend in the data; IV and DV are typically both quantitative

Scatter plot

0 1 2 3 4 5 6 70

5

10

15

20

25Helicopter Drop Time Influenced by Pa-

perclips

# of Paperclips

Dro

p Ti

me

(s)

This is a line of best fit – it is a straight line that comes as close to possible as all the data points

The data points should NOT be directly connected because the IV is discrete

Bar Graph6th period student drink choices

0

5

10

15

20

25

pepsi coke rootbeer

dr.pepper

Drink Option

# o

f stu

den

ts

Series1

This is what we mean by discrete data – it’s a category and there’s no “middle value” between each category

Line Graph

Jill's Elevation Change While Hiking

015030045060075090010501200

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5

Time (hours)

Ele

va

tio

n (

m)

Each value here has meaning (I could determine Jill’s elevation at 3.2 hours) so I connect my data points with a line

Pie chart

Here I have data organized in a percentage out of 100 – this is good way to represent it

A few final thoughts

• Scientists must represent their data graphically in a fair way that tells the most accurate story

• It is possible to change the scale of a graph to make it look like the data tells a much different story than the numbers actually show

• Additional labels might also be used to make the reader infer certain things from the same set of data

For example…• At most, how different is the average rainfall

from this year’s rainfall?

This graph has been manipulated to make it look like there is a much bigger difference in rainfall than 0.4 cmBad scientist!

A more accurate graph

Good scientist!

Another Example

Quarter, YearImage source: http://www.politicalmathblog.com/?p=401

Num

ber o

f job

s (t

hous

ands

)

U.S. Jobs by Quarter

What conclusions can you draw from this graph?

Here’s the same graph with a few extra labels…

Image source: http://www.politicalmathblog.com/?p=401

The data is the same but it could be used to create different inferences

Examples

• Question: Does changing the temperature of a ball affect the height the ball will bounce?

• What is the scientist changing? (independent variable)– The temperature of the ball

• What is the scientist measuring? (dependent variable)– Bounce height of the ball when

dropped

• What should be kept the same or constant for a fair test? (constants)– Same ball– Dropped from same height– Onto same surface.– Etc.

• Question: Does changing the color of light change the growth rate of plants?

• Independent?– Color of light

• Dependent?– Growth rate of plants

• Constants?– Age of plant– Type of plant– Amount of light– Amount of water– When measured– Type of container– Etc.