1. Bar graphs offer a simple way to compare numeric values of any kind, . Bar graphs can be either horizontal or vertical. One axis represents the categories, while the other represents the value of each category.
2. Quartiles and the Interquartile Range
2.1. Interquartile Range (IQR)
2.1.1. is calculated by obtaining the difference between the 1st & 3rd quartile; (Q3 – Q1)
2.1.2. indicates therange of where the middle half and 50% of the data set lies.
2.2. Quartiles
2.2.1. divide your data into quarters
2.2.2. Normally distributed data
3. Cumulative Frequency Graphs and Percentiles
3.1. Cumuative frequency graph
3.1.1. is the running total of the frequencies
3.1.2. .
3.2. Percentiles
3.2.1. a score in the 85th or 95th percentile indicates 85% or 95% of the data lies AT or BELOW that value.
3.2.2. provide a score in which a certain percentage of data lies AT or BELOW the data set.
4. Measures of Central Tendancy
4.1. Mean
4.1.1. also called the expected value or average, is the central value of a discrete set of numbers: specifically, the sum of the values divided by the number of values.
4.1.2. Formula: m = sum of terms / number of terms
4.2. Mode
4.2.1. the value that appears most often
4.3. Median
4.3.1. Q2 is the value of the Median
4.3.2. Q1 and Q3 are the medians of the lower and upper halves of the data set, respectively.
4.3.3. the middle number of set of data points
4.3.4. Formula for the place of the median:(n+1) / 2
5. Graphs
5.1. Line graph
5.1.1. illustrate how related data changes over a specific period of time. One axis might display a value, while the other axis shows the timeline.
5.1.2. .
5.2. Bar Graph
5.2.1. .
5.3. Pictograph
5.3.1. A pictograph uses pictures or symbols to display data instead of bars. Each picture represents a certain number of items.
5.3.2. .
5.4. Histogram
5.4.1. A histogram is another type of bar graph that illustrates the distribution of numeric data across categories. People often use histograms to illustrate statistics.
5.4.2. .
6. Box and Whisker Plot
6.1. Whiskers
6.1.1. represent the lowest and highest values in the data set
6.1.2. The length of the lower (left) whisker represents 25% of the data below the 1st quartile
6.1.3. he length of the upper (right) whisker represents 25% of the data above the 3rd quartile.
6.2. Box plot
6.2.1. the length of the bo shows the interquartilw range (IQR)
6.2.2. The box plot shows the values of Q1 , Q2 , and Q3
6.3. box and whisker plot
6.4. Outliers
6.4.1. Asterisks are outliers
6.4.2. plotted as asterisk (*)
7. Varience and Satandard Deviation
7.1. Varience
7.1.1. average of the squared differences from the Mean
7.1.2. formula: s^2= Σ(X1 − X2) ^2 / n-1
7.1.3. To obtain varience:
7.1.3.1. 1.) Obtain Mean.
7.1.3.1.1. 2.) find the DIFFERENCE of the Mean to EACH value in the set.
7.2. Variance and Standard Deviation uses all of the values in the set to calculate an accurate measure of spread
7.3. Standard Deviation
7.3.1. Formula: √Σ(X1 − X2) ^2 / n-1
7.3.2. can be found by taking the square root of the calculated variance
7.3.3. measures the average of how far each value is from the mean
8. Key Concepts
8.1. Range
8.1.1. Formula: Maximum Value–Minimum Value
8.1.2. the area of variation between upper and lower limits on a particular scale
8.2. Relative frequency
8.2.1. defined as the number of times that the event occurs during experimental trials, divided by the total number of trials conducted.
8.3. Cumulative frequency
8.3.1. is the running total of the frequencies
8.4. Frequency table
8.4.1. lists items and shows the number of times the items occur
8.5. Modal Class
8.5.1. the class with the highest frequency
9. Defintions of Key Terms
9.1. Population
9.1.1. an entire collection of individuals about which we want to draw conclusions
9.2. Census
9.2.1. the collection of information from the whole population
9.3. Sample
9.3.1. a subset of the population wich should be chosen at random to avoid bias in the results
9.4. Survey
9.4.1. a subset of the population wich should be chosen at random to avoid bias in the results
9.5. Data
9.5.1. information about individuals in a population
9.6. Parameter
9.6.1. a numerical quantity measuring some aspect of a population
9.7. Numerical Variable
9.7.1. describes a charecteristic which has a numerical value that can be counted or measured
9.8. Categorical variables
9.8.1. describes a particular quality or charecteristic which has a numerical value that can be counted or measured
9.9. Distribution
9.9.1. The pattern of variation of data which can be discribed as
9.9.1.1. .
9.9.1.2. .
9.9.1.3. .
9.10. Statistic
9.10.1. a quantity calculated from data gathered from a sample usually used to esimate a population parameter
9.11. Outliers
9.11.1. data values that are either much larger or much smaller then the general body of data; they should be included in analysis unless they are the result of human or other known error
9.11.2. what qualifies as an outlier: if the value is 1.5 times the IQR it is an outlier
9.12. Discrete variable
9.12.1. takes exact number values and is often a result of counting
9.13. Continuous Variable
9.13.1. takes numerical values with a certain continous rangeand is usually a result of measuring