1. Data, Tables & Graphs October 24, 2016 BIOL 260
October 24, 2016
BIOL 260
Handbook of Biological Investigation Ch. 3, 4, 5, 7

2. Turn in Worksheet #8
Turn In Final Topic title form

3. Guests Today Mrs. Holshouser from AP Biology class
Bartlett Yancey High School

4. Scientific Method
Once you have a hypothesis you should start thinking about the types of data that you will collect.
Variable: characteristic that differs in a group.
What type of scale will we use to measure the variable?
Are there more men than women in this class? Variable is gender. Group is class population.
Types of variables nominal scale, ordinal scale for discrete data = each item separate of whole unit. Nominal sorts data is named groups. Ordinal scale organized in terms of relationship to one another. Scale = median.
Continuous data: continuous points taken on a scale. Measure interval or ration. The interval and ration scale indicate distance between the items.

5. Types of Data Qualitative Data Quantitative Data Discrete Data
Nominal scale
Ordinal scale
Continuous Data
Interval
Ratio scale
Describe qualitative and quantitative data!

6. Right so we have more males than females (see the icons are smaller) Using this type of data we understand trends, presence/absence. Do you think it has its place in science? Can you think of an example?

8. What about this figure Qual or Quant? How do you know.

9. Quantitative data includes the values
Quantitative data includes the values. From this we can collect more detailed information about the subject or experiment. Not just presence or absences but amount, performance and then compare the different groups or variables tested.

10. Types of Data Qualitative Data Quantitative Data Discrete Data
Nominal scale
Ordinal scale
Continuous Data
Interval
Ratio scale
Now we are going to focus on Quantitative data. Within this category there is discrete and continuous.
discrete data = each item separate of whole unit. For discrete data use nominal scale, ordinal scale.
Nominal sorts data is named groups. Ordinal scale organized in terms of relationship to one another.
Continuous data: continuous points taken on a scale. Measure interval or ratio. The interval and ratio scale indicate distance between the items.

11. each item separate of whole unit

12. continuous points taken on a scale

13. Scales of Measurement Scale Nominal
Numbers are assigned (ex. to runners)
Ordinal
Rank is assigned (ex. to winners)
Third Place
Second Place
First Place
Interval
Rated on a scale
(ex. Performance) no zero point (absence)
8.2
9.1
9.6
Ratio
Value proportional to true zero point.
15.2
14.1
13.4 7 8 3
Discrete Data
So once we know what kind of data we expect to produce what are we going to do with it once we record it?
Continuous Data

14. Organizing collected data
Plot data on bar graph to determine frequency.
What is the distribution?
Characterizing the data as a whole.
You need to organize your data before you can analyze it.
Histogram show here in bar graph allows you to do so.
Then you can ask questions about the distribution – DATA AS WHOLE
WHAT DISTRIBUTION DO WE SEE?

15. Central Tendencies of data
Plot data on bar graph to determine frequency.
What are tendencies?
Characterizing single points that make up data: what normally happens.
MEAN: numerical average
MEDIAN: middle value in distribution
MODE: most frequently occurring value
What values do you want to know when given a distribution? Central tendency values characterize single points that make up the whole.
What values do you typically calculated when you see a distribution? Need a hint: grades

16. Dispersion of data Plot data on bar graph to determine frequency.
What are tendencies?
What is happening outside of central tendencies?
RANGE: difference between highest and lowest value
STANDARD DEVIATION: distance between mean and point of inflection
VARIANCE: how are data points grouped (ex. clustered or not) SD2
Outside of data as whole, distribution or central tendencies we also have some other values that we use to characterize the data.
With these we are looking at what happens outside of central tendencies. Looking at DISPERSION
There are other tendencies one may want to determine – this happens outside of central tendencies.

17. Normal Distribution 99.6% 95.4% 68.2% What is stdev?
Calculator or know formula
95.4%
68.2%
Bell shaped curve the highest point is the mean, median and mode.
Point of inflection where curve changes. Shown with arrows.
34.1% is one standard deviation away from mean 68.2% data. How clustered are these data points?
With three standard deviation points your accounted for 99.6 % of your data so this data is well clustered with a normal and non-skweded distribution
Sum of all ((values – mean)^2)/number of data points
Square root of value

18. Statistical analysis of data
Descriptive
Trend, tendencies, statistics
Correlation
Regression = strength of relationship

19. Difference in distribution

20. Difference in Means

21. Difference in variability

22. 1. Where is this data recorded. 2. What happens to the data 3
1. Where is this data recorded? 2. What happens to the data 3. How you do choose what data goes into an article?

23. Precision Vs. Accuracy Why does it matter? What is the difference?
Which term do you want to describe your data?
Precision refers to closeness of two or more points
Accuracy refers to closeness of value to standard
How would you describe these targets?
Accurate and precise
Precise not accurate
Not precise but accurate
Not precise or accurate

24. Induction of creatine deaminase in C. neoformans and c. bacillispsorus.
The authors collected data on the activity of the enzyme creatinine deiminase in two species of bacteria to determine if activity was increased by various nitrogen sources

25. Soil analysis of four famrs near Malverne, Vermont.

26. Effect of spermidine on transformation on bacillus subtilis
What are the differences between graphs and tables. When would you use a graph, table. What would you make sure was included in each? How would you format them?