1. Frequency Table
1.1. Advantages
1.1.1. Easy to understand and interpret
1.1.2. Used to compare multiple data sets
1.1.3. Used to create a histogram
1.2. Disadvantages
1.2.1. Cannot visually see information unless you create a graph out of it
1.2.2. Data sets can be skewed when not put into graph form
2. Graphs
2.1. Bar Graph
2.1.1. Advantages
2.1.1.1. Can compare numbers is grouped categories
2.1.1.2. Visually Strong
2.1.1.3. Can compare multiple data sets easily
2.1.2. Disadvantages
2.1.2.1. Needs explanation and labeling
2.1.2.2. Bars can be moved around to show different effects, which might skew date
2.1.2.3. Hard to see part/whole relationship
2.1.2.4. Not able to be used as a predictive tool
2.2. Pictograph
2.2.1. Advantages
2.2.1.1. Easy to read
2.2.1.2. Visually appealing
2.2.1.3. Can handle large data sets
2.2.2. Disadvantages
2.2.2.1. Hard to quantify partial icons
2.2.2.2. Icons must be the same size to be accurate
2.2.2.3. Very simple design
2.3. Pie Chart/Circle Graph
2.3.1. Advantages
2.3.1.1. Shows part-to-whole relationships
2.3.1.2. Can work with large data sets
2.3.1.3. Easily read
2.3.1.4. Self-explanatory
2.3.2. Disadvantages
2.3.2.1. An "other" category can cause problems with data sets
2.3.2.2. Difficult to compare only 2 data sets
2.3.2.3. If in percentage form, the total might be hard to see unless otherwise specified
2.3.2.4. Hard to make by hand
2.4. Histogram
2.4.1. Avantages
2.4.1.1. Good for large data sets
2.4.1.2. Can represent grouped data (intervals)
2.4.1.3. Easy to read
2.4.2. Disadvantages
2.4.2.1. Can't see individual data
2.4.2.2. Must complete a frequency table first
2.4.2.3. Difficult to compare only 2 data sets
2.4.2.4. Data used can be manipulated
2.5. Line Graph
2.5.1. Advantages
2.5.1.1. Can sow change over time
2.5.1.2. Can be used to compare 2 data sets (Double Line Graph)
2.5.2. Disadvantages
2.5.2.1. Must be clearly labeled
2.5.2.2. Can be difficult to accurately read
3. Plots
3.1. Stem and Leaf Plot
3.1.1. Advantages
3.1.1.1. Nice, visual organization
3.1.1.2. Back-to-back good for comparing
3.1.2. Disadvantages
3.1.2.1. Difficult to construct
3.1.2.2. Can be time consuming
3.1.2.3. Hard to read
3.1.2.4. Not best used with large data sets
3.1.2.5. Rarely used outside of standardized testing
3.2. Scatter Plot
3.2.1. Advantages
3.2.1.1. Shows how closely 2 sets of data are related
3.2.1.2. Easy to read
3.2.1.3. Easy to create
3.2.2. Disadvatnages
3.2.2.1. Must be clearly labeled
3.2.2.2. Data points that are near each other might be difficult to read
3.3. Dot Plot
3.3.1. Advantages
3.3.1.1. Able to see individual data
3.3.1.2. Easy to read
3.3.1.3. Easy to create
3.3.1.4. Can identify extreme values
3.3.2. Disadvantages
3.3.2.1. Does not work well with large data sets
3.3.2.2. Have to multiply to find totals
3.3.2.3. X's must consistently be the same size
3.4. Box and Whisker Plot
3.4.1. Advantages
3.4.1.1. Can display large sets of data
3.4.1.2. Helps show how data can cluster
3.4.1.3. Shows the minimum, maximum, range, and median of a given data set
3.4.2. Disadvantages
3.4.2.1. Can be difficult to understand
3.4.2.2. Must know how to find the information such as the median to complete the plot
4. Types of Data
4.1. Categorical Data
4.1.1. Advantages
4.1.1.1. Can show multiple data sets
4.1.1.2. Able to clearly see difference between sets
4.1.1.3. Able to be used in multiple graphs
4.1.1.4. Can show data only by name
4.1.1.5. Ordered categories
4.1.2. Disadvantages
4.1.2.1. Data set might be broad
4.1.2.2. Don't always know the amount of data unless otherwise specified
4.2. Numerical Data
4.2.1. Advantages
4.2.1.1. Can clearly see ratios
4.2.1.2. Easy to define (can only add and subtract)
4.2.1.3. Starts with an absolute zero
4.2.2. Disadvantages
4.2.2.1. Can be too general
4.2.2.2. Not always valid
4.2.2.3. Cannot always answer questions numerically
4.3. Discrete Data
4.3.1. Advantages
4.3.1.1. Quick and easy to collect
4.3.1.2. Easy to understand
4.3.2. Disadvantages
4.3.2.1. May require large sample sizes
4.3.2.2. Data can be lost within large sample sizes
4.4. Continuous Data
4.4.1. Advantages
4.4.1.1. Can use smaller sample sizes
4.4.1.2. Show change over time
4.4.1.3. Can show small levels of change
4.4.2. Disadvantages
4.4.2.1. May need outside measuring tools
5. Statistical Terms
5.1. Mean
5.1.1. Advantages
5.1.1.1. Finds the average of a data set
5.1.1.2. Easy to find (Add all numbers in data set, and divide that answer by the total numbers given)
5.1.2. Disadvatanges
5.1.2.1. Can only be used for data that involves numbers (Cannot find mean in Categorical Data)
5.1.2.2. Most sensitive tendency (Affected by outliers)
5.2. Median
5.2.1. Advatages
5.2.1.1. Shows the exact middle of data set
5.2.1.2. Can help show test scores for an entire classroom
5.2.1.3. Easy to find (order numerical data from least to greatest or greatest to least)
5.2.2. Disadvantages
5.2.2.1. May require more math if there is not an exact middle (Find the average of the two middles)
5.2.2.2. Requires accuracy when organizing data
5.3. Mode
5.3.1. Advantages
5.3.1.1. Easy to find
5.3.1.2. Shows most frequently occurring data point
5.3.1.3. Can be used with categorical or numerical data
5.3.2. Disadvantages
5.3.2.1. Isn't based off all values given
5.3.2.2. Can have one or more mode, or none at all
5.3.2.3. Cannot use any mathematical equations on it
5.4. Range
5.4.1. Advantages
5.4.1.1. Easy to see and calculate
5.4.1.2. Shows highest and lowest values of given data set
5.4.1.3. Does not change in concrete data set
5.4.2. Disadvantages
5.4.2.1. Not based off all given terms
5.4.2.2. Can be changed if new information is presented in data set
5.5. Mean Absolute Deviation
5.5.1. Advantages
5.5.1.1. Describes variation in data set
5.5.1.2. Shows how spread out the different variables in a data set are
5.5.2. Disadvantages
5.5.2.1. Not easy to formulate right away