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**Experiments and Variables**

Physical Science

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**C2 Experiments and Variables**

Supplies: Pencil and paper Standards: 9b) Evaluate the accuracy and reproducibility of data. 9c) Distinguish between variable and controlled parameters in a test. 9g) Distinguish between linear and nonlinear relationships on a graph of data.

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**C2.1 Variables and Relationships**

You will be taking notes in either Cornell Notes format or Charting format.

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**C2.1 Variables and Relationships**

Definitions Variable: a quantity that can be precisely specified, often with a numerical value. For example, position and speed are variables. Value: the particular number (with units) or choice that a variable may have. Graph: a mathematical diagram showing one variable on the vertical (y) axis and the second variable on the horizontal (x) axis.

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**C2.1 Variables and Relationships**

Independent variable: in an experiment, a variable that is changed by the experimenter and/or causes changes in the dependent variable. Dependent variable: in an experiment, a variable that responds to the changes in the independent variable.

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**2.1 Variables and Relationships**

A variable is a quantity that has a value which describes something. The variable color represents all possible choices and red is a value, a specific choice of color.

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**2.1 Variables and Relationships**

Which is the variable and which is the value?

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**2.1 Using Variables in Physical Science**

Some common variables in physical science include: mass time position angle temperature volume

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**2.1 Using Variables in Physical Science**

Physical science is all about relationships between variables. A good way to show a relationship between two variables is to use a graph. .

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**2.1 Using Variables in Physical Science**

A graph is a mathematical diagram that shows: one variable on the y (vertical) axis a second variable on the x (horizontal) axis.

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**2.1 Using Variables in Physical Science**

Two variables may have: a strong relationship, a weak relationship, or no relationship at all.

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2.1 Direct Relationships In a direct relationship, when one variable increases, the other also increases. This is also called a linear relationship.

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**2.1 Inverse Relationships**

In an inverse relationship, when one variable increases, the other decreases.

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**2.1 Complex Relationships**

Some relationships are neither direct nor inverse. This graph shows a non-linear relationship.

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2.1 Designing a graph A graph makes it easy to see if changes in one variable cause changes in the other variable (the effect). The variable that causes the change is called the independent variable. The dependent variable shows the effect of changes in the independent variable.

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2.1 Designing a graph The independent variable is always placed on the x-axis. The dependent variable is always placed on the y-axis.

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2.1 Reading a graph A graph is a simple form of model that connects two or more variables. Scientists use models to make and test predictions.

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**2.1 Four steps to make a graph**

Step 1: Choose which will be the dependent and independent variables. The dependent variable goes on the y-axis and the independent variable goes on the x-axis. Step 2: Make a scale for each axis by counting boxes to fit your largest value. Count by multiples of 1, 2, 5, or 10. Step 3: Plot each point by finding the x-value and drawing a line upward until you get to the right y-value. Step 4: Draw a smooth curve that shows the pattern of the points. Do not just connect the dots.

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**2.1 Using math to describe variables**

Math is the best language to describe relationships between variables. When you write out a relationship in math, you use a single letter or symbol to represent each variable. We can show the relationship between variables for volume in two different ways.

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2.1 Basic Math Operations The relationships between variables are represented in math by operations. Four operations you know are: add subtract multiply divide.

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**2.1 Solving for one variable**

Formulas allow you to calculate any one variable if you know the values of the others.

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2.1 Basic Math Operations A formula is a relationship that gives one variable in terms of other variables.

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C2.2 Experiments and Data You will be taking notes in either Cornell Notes format or Charting Notes format.

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**C2.2 Experiments and Data Definitions**

Experiment: a situation specially set up to investigate the relationship between variables. Procedure: a description of an experiment that details the equipment used, the techniques used, and the data collected. Data: information collected during an experiment or other scientific inquiry. Data is often values of variables measured in an experiment.

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C2. Experiment and Data Analysis: the process of evaluating data. Analysis may include thinking, creating graphs, doing calculations, and discussing ideas with others. Conclusion: a statement of what was learned in an experiment or observation. Experimental variable: a variable that changes in an experiment. Control variable: a variable that is kept constant in an experiment.

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C2. Experiment and Data Accuracy: describes how close a measurement is to the true value. Error: is the difference between a measurement and the true value. Average: a mathematical process in which you add up all the values, then divide the result by the number of values. Significant: a difference between two measured results is significant if the difference is greater than the error in measurement.

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2.2 Experiments and Data An experiment is a situation specially set up to investigate the relationships between specific variables. Experiments test whether or not a hypothesis has scientific support.

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2.2 Experiments and Data An experiment is a situation specially set up to investigate the relationships between specific variables. Experiments test whether or not a hypothesis has scientific evidence or support.

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**2.2 Converting between units**

If experiments can not be performed, then a theory can be tested by comparing predictions of the theory with observations of what occurs in nature. Several different theories of how solar systems form are currently under scientific review.

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2.2 Experiments and Data The first step in designing a good experiment is to clearly state what is to be tested (make a hypothesis). The next part, called the procedure, tests the hypothesis using good experimental design. In many experiments data in the form of values or measurements is the scientific evidence. Scientists analyze the data by thinking, graphing, or doing calculations. If the experiment is successful, the analysis will lead to a conclusion, which is usually a statement about the hypothesis.

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**2.2 Controlled Experiments**

In a controlled experiment only one variable is changed at a time.

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2.2 Other Experiments In some tests you may be asked to identify the experimental variable by looking at data collected in an experiment.

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2.2 Error Error is the difference between a measurement and the true value of what is measured. Which of these values has the LEAST amount of error? Which value has the MOST error?

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2.2 Accuracy In science, the word accuracy means how close a measurement is to the true value of what is being measured. Which of these values is the MOST accurate?

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2.2 Averages When you make many measurements of the same thing you will notice that the values cluster around an average. To calculate an average, add up all the measurements and divide by the total number of measurements. Some measurements are more than the average and some are less. How are averages useful? Trivial picture of how to find an average

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2.2 Estimating Error In science, two measurements are considered the same if their difference is less than or equal to the amount of error.

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2.2 Reproducibility Reproducibility means two things in science experiments: If you repeat the experiment the same way, you always get the same result. Others who repeat your experiment get the same result. Is the data from these two groups reproducible? Why or why not?

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2.2 Drawing Conclusions The point of experiments is to produce data that allows scientists to come to conclusions. Which group’s data gives a more valid conclusion about the hypothesis?

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