Graphs ppt

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Graphs ppt

  1. 1.  Do it before the experiment - don’t wait until you start the experiment to figure out how to record your data, do it as part of the plan before you start  Where do the variables go? - independent on the LEFT - dependent on the RIGHT  No units in the tables - DO NOT include labels in the table, only include them in the title boxes! When you are recording data it is important to remember to be specific, and record everything! It is better to record too much, and then not need to use the data, than to not record enough information!
  2. 2. QUANTITATIVE means a measured quantity.  Deals with numbers.  Data which can be measured.  Length, height, area, volume, weight, speed, time, temperature, humidity, sound levels, cost, members, ages, etc.  Quantitative → Quantity QUANTITATIVE means describing a “quality” such as color, smell, shape, etc  Deals with descriptions.  Data can be observed but not measured.  Colors, textures, smells, tastes, appearance, beauty, etc.  Qualitative → Quality
  3. 3. Continuous data  data that could be any number on a continuum  changes over time are usually continuous (imagine the slope of a hill) Discreet data data that has only certain options (imagine a set of steps)  number of people, shoe size, type of exercise are all types of discreet data  whenever you create groups you create discreet data, i.e. - 0-5minutes, 6- 10minutes, 11-15minutes are discreet  groups even though time is usually continuous
  4. 4.  Organise raw data in a table.  One example… Independent variable Dependent variable average trials
  5. 5.  Example:  Effect of the type of fertiliser in plant growth. Compost or fertiliser Height (cm) Trial 1 Trial 2 Trial 3 Trial 4 Trial 5 Compost 8 6 8 7 9 Fertiliser 5 7 5 3 5 control 4 3 5 8 0
  6. 6.  After you have completed your experiment you will need to process your raw data. Do you need to find the average? Maybe a percentage, total, orvdifference is best? It will depend on your data!  Explain in words include a few written sentences to explain why you chose the formula you did don’t just say, “because I have to process my data”!
  7. 7. After you have processed your data, you need to present it in a second table. This will be the table that you use to make your graph, and your conclusion. New table - create a second table after your data processing section Smaller table - yes, it is going to be smaller than the raw data table Variables - independent variable in the left column - dependent variable in the right column(s)
  8. 8.  Example:  Effect of the type of fertiliser in plant growth. Compost or fertiliser Average Height (cm) Compost 8 Fertiliser 5 control 4
  9. 9.  We use data collected in our experiments to make graphs.  To understand data better.  To identify patterns in data  A correct collection of data is essential to make correct graphs.  Sometimes data need to be manipulated. Average…  Use your processed data to create a graph that shows the results of your experiment. It should be neat, including proper titles, and must be the proper type of graph!
  10. 10. Type of graph depends on the type of data your independent variable produces  Line graphs or scatter plots : continous data.  Bar graphs: discreet data, compare groups.  Pie charts: use for percentages or parts of a whole.
  11. 11.  Used when data produced by the independent variable is discreet.  When one of the data variables is “time”, it goes on the x axis. Generally independent variable goes in the x axis.  Many line graphs show changes over time or the change of one variable (responding variable) due to the change of another variable (manipulated variable). 5 10 15 20 25 30 35 40 45 50 55 0 1 2 3 4 5 6 Growth of Plant A Over Time Time (Days) PlantHeight(cm)
  12. 12. Chocolate MIlk Sold 0 20 40 60 80 100 120 Monday Tuesday Wednesday Thursday Friday Day AmountSold Chocolate
  13. 13.  Used for discreet data.  Scatterplots contain a line of best fit, which is a straight line drawn through the center of the data points that best represents the trend of the data. Scatterplots provide a visual representation of the correlation, or relationship between the two variables.
  14. 14. Types of Correlation  Positive correlation: Both variables move in the same direction. In other words, as one variable increases, the other variable also increases. As one variable decreases, the other variable also decreases.  i.e., years of education and yearly salary are positively correlated.  Negative correlation: The variables move in opposite directions. As one variable increases, the other variable decreases. As one variable decreases, the other variable increases.  i.e., hours spent sleeping and hours spent awake are negatively correlated. No Correlations It means that there is no apparent relationship between the two variables. For example, there is no correlation between shoe size and salary. This means that high scores on shoe size are just as likely to occur with high scores on salary as they are with low scores on salary.
  15. 15.  Use it when a set of measurements can be split into discrete and comparable groups  To show the relative change between these groups 0 1 2 3 4 5 6 Average Plant Growth over 50 Days Plant A (Control) Plant B (Fer- tilizer Added) Plant C (Compost Added) Plant A Plant B Plant C AverageGrowthinCentimeters
  16. 16. Chocolate Milk Sold 53 72 112 33 76 0 20 40 60 80 100 120 Monday Tuesday Wednesday Thursday Friday Day AmountSold Monday Tuesday Wednesday Thursday Friday
  17. 17.  When showing parts of a whole..i.e. percentages
  18. 18. Chocolate Milk Sold Monday Tuesday Wednesday Thursday Friday
  19. 19. On what day did they sell the most chocolate milk? a. Tuesday b. Friday c. Wednesday Chocolate Milk Sold 53 72 112 33 76 0 20 40 60 80 100 120 Monday Tuesday Wednesday Thursday Friday Day AmountSold Monday Tuesday Wednesday Thursday Friday
  20. 20. Chocolate Monday Tuesday Wednesday Thursday Friday On what day was the least amount of chocolate milk sold? a. Monday b. Tuesday c. Thursday
  21. 21. Chocolate 0 20 40 60 80 100 120 Monday Tuesday Wednesday Thursday Friday Day AmountSold Chocolate On what day did they have a drop in chocolate milk sales? a. Thursday b. Tuesday c. Monday
  22. 22. Don’t forget to include...  title  x and y axis  axis titles including units  proper scale of numbers X-axis horizontal Y-axis vertical
  23. 23. Y Axis (label with the name of the variable you are going to represent here, and the
  24. 24. X Axis (label with the name of the variable you are going to represent here, and the unit.)
  25. 25. T - Title Teachers’s Favorite Singer
  26. 26. T - Title A - Axis Teachers’s Favorite Singer
  27. 27. T - Title A – Axis S – Scale Teachers’s Favorite Singer Decide on an appropriate scale for each axis. Choose a scale that lets you make the graph as large as possible for your paper and data
  28. 28.  Scale is determined by your highest & lowest number.  In this case your scale would be from 2 – 22. Favorite Singer Number of Teachers Toby Keith 22 Madonna 15 Elvis 11 Sting 5 Sinatra 2
  29. 29.  The interval is decided by your scale.  In this case your scale would be from 2 – 22 and you want the scale to fit the graph.  The best interval would be to go by 5’s. Favorite Singer Number of Teachers Toby Keith 22 Madonna 15 Elvis 11 Sting 5 Sinatra 2
  30. 30. T – Title A – Axis I – Interval S – Scale Teachers’s Favorite Singer The amount of space between one number and the next or one type of data and the next on the graph. The interval is just as important as the scale Choose an interval that lets you make the graph as large as possible for your paper and data
  31. 31. T – Title A – Axis I – Interval S – Scale Teachers’s Favorite Singer 0 5 10 15 20 25
  32. 32. T – Title A – Axis I – Interval L – Labels S – Scale Teachers’s Favorite Singer 0 5 10 15 20 25 LABEL your bars or data points Singers Give the bars a general label. What do those words mean? NumberofTeachers Label your Y Axis. What do those numbers mean?
  33. 33.  Given the following data collected from measuring the growth of a plant’s root and stem over the weeks: Time (weeks) Independent variable Growth Root (cm) Growth Stem (cm) 1 1 1.5 2 1.5 2 3 2 3 4 2 3.5 5 2 4 6 2.5 5 7 2.5 6.5
  34. 34. 1. Use a grid 2. Write a title 3. Label the axis (magnitude and unit). Choose what you are going to represent on each of them (Remember that time always goes on the x axis) 4. Choose a scale. That is, the value you are going to give to each square of your grid, for each variable 0 1 2 3 4 5 6 7 0 0.5 1 1.5 2 2.5 3 Growth(cm) time (weeks) Plant growth
  35. 35.  Plot the first set of data (root growth) on the graph with dots. 0 1 2 3 4 5 6 7 0 0.5 1 1.5 2 2.5 3 Growth(cm) time (weeks)
  36. 36.  Join the dots with a line 0 1 2 3 4 5 6 7 0 0.5 1 1.5 2 2.5 3 Growth(cm) time (weeks) Plant Growth
  37. 37.  If there where to sets of data, just plot the second one following the same steps with a different colour.
  38. 38. PATTERNS  When making your conclusion you need to first identify the patterns in the data. Is the dependent variable increasing or decreasing? Is there a linear relationship, or exponential? How exactly are the variables related or not related?  Increase, decrease, or constant  data does not go “up”, it increases  data does not go “down”, it decreases  data does not stay the same, it is constant  sometimes data does 1, 2, or all 3 of these at different points Relationships between variables  - direct = both increase, or both decrease  - indirect = they are opposite
  39. 39.  Recall the purpose of the type of graph used and its advantages and disadvantages.  Read the TITLE . The TITLE briefly describes the data represented in the graph.  Read the footer or summary of the graph is included.  Read the labels of the axes. The independent or manipulated variable is usually on the x axis and the dependent or responding variable on the y axis.  Read the units of the axes. Ensure you know the quantity measured and the multiple or submultiple of the units used. Understanding the units used helps you to quantify relationships between variables.  Read the scales of the axes.. Is the range a small or large one? Many students take in the shape of the graph with out first considering the scale. This of course leads to erroneous conclusions.  Examine the symbols and the Key/Legend used. Sometimes the curves or columns are labelled.

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