Graphs in science
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This document discusses line graphs and how to construct them. It explains that line graphs are used to show how one variable changes in response to another variable. It provides steps for plotting a line graph, including labeling the axes, scaling the axes appropriately, plotting the data points, drawing a line of best fit, and adding a title. It also defines key terms like slope, linear and nonlinear graphs, and trends in data.
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This document discusses different methods for displaying univariate data, including frequency tables, bar charts, segmented bar charts, dot plots, stem plots, histograms, and box plots. It provides details on how each method organizes and presents the data, when each method is best used, and examples of key aspects like axes, bars, intervals, and 5 number summaries. The document is intended to explain the options for visually representing single variable data sets.
2.5
This document discusses using linear models and scatter plots to determine the relationship between two data sets. It defines a scatter plot as a graph that relates two sets of data by plotting ordered pairs. A trend line approximates the relationship between the variables and can be used to make predictions, while the line of best fit found using technology is the most accurate trend line. Examples are provided on making a scatter plot from temperature and electricity cost data and finding the equation of a trend line.
Reflection, Scaling, Shear, Translation, and Rotation
The algorithm takes input coordinates for a 2D or 3D point and applies various linear transformations - reflection, scaling, shear, translation, and rotation. For reflections, it calculates the reflected coordinates across lines or planes through different axes. For scaling and translation, it multiplies/adds the input coordinates with scaling/translation factors. For rotation, it uses rotation matrices to calculate the rotated coordinates around different axes. It prints the transformed coordinates after applying each transformation.
3.1 Measures of center
This document provides an overview of measures of central tendency including the mean, median, and mode. It discusses how to calculate and interpret each measure using examples with data sets. The mean is calculated by adding all values and dividing by the total number. The median is the middle value when data is arranged in order. The mode is the value that occurs most frequently. Other measures discussed include the midrange and calculating the mean from a frequency distribution. Proper rounding of measures is also covered.
LINE AND SCATTER DIAGRAM,FREQUENCY DISTRIBUTION
The document discusses line graphs and frequency distributions.
1) A line graph uses points connected by lines to show how a value changes over time or as another variable changes. Line graphs are used to track qualitative data and compare changes over time for multiple groups.
2) A frequency distribution organizes data into classes and counts the frequency of observations in each class. It can group or ungroup data. The distribution includes the class, frequency, and tally marks.
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Graphs in science
- 1. Ch.1 Section 5 Pages 35-41 Graphs in Science
- 3. Bar Graphs
- 4. Line Graphs
- 5. Cool
- 10. Line of best Fit Line of best fit emphasizes the overall trend shown by all the data taken as a whole
Editor's Notes
- Variables and Graphing A good experiment attempts to control all variables except the variable(s) that are being manipulated to see if a change can be observed and a cause and effect relationship can be reasoned. Variable A property or condition that can change. A variable may or may not cause a significant change. Manipulated variable is the variable that the experimenter decides to change to see if there is or is not an effect. Responding variable is the variable that will change as a result of the change in the manipulated variable. It may also be observed and or measured to determine the quantity or quality of change. When we plot information on a graph the manipulated variable always is plotted on the X - axis and the responding variable is always plotted on the Y - axis . Independent variable is another name for manipulated variable. It is independently selected by the experimenter to be manipulated. Dependent variable is watched by the experimenter and will respond to the manipulated or independent variable if there is a relationship. These relationships can be represented on a chart or graph: Before deciding to use a chart or graph a person needs to decide if the data is continuous or categorical. Continuous is data that can be represented on a continum with infinite possibilities. Temperature for example can be represented by whole numbers, decimal numbers... Categorical is data that is represented by a limited number of examples. Temperature could be categorical if it were categorized into hot, medium, or cold. Kinds of vehicles, people's favorite food... One might argue that favorite foods could be infinite, however it can be represented by categories and a category labeled others. Therefore, limiting the possibilities. Continuous data is best shown on a line graph and categorical in bar, pie, or other kinds of charts. Extrapolation is a prediction that is made from outside (extra) the data points collected and represented on the graph. Interpolation is a prediction that is made between the data points (inter, like interstate, between or among states).