Categorical and Quantitative Data
1
Mean, median, and mode
Understand and calculate the different types of averages.
6.SP.A.2
2
Creating box and whisker plots
Drag to put the numbers in order, then create their graph.
6.SP.B.4
3
Interpreting and comparing data distribution
Represent data with plots on the real number line (dot plots, histograms, and box plots).
HSS-ID.A.1
4
Exploring Standard Deviation
Drag the dots to move the data points to create the correct standard deviation
HSS-ID.A.3
5
Standard Deviation of a Population
Find the average and the standard deviation of each data set
HSS-ID.A.2
6
Empirical Rule (Bell Curve)
Use the empirical rule to determine the percentage with in the scoring range.
HSS-ID.A.4
7
Estimating the line of best fit
Given a random assortment of points, find the best fit line for them.
HSS-ID.B.6c
8
Linear models of bivariate data
Use the given data to observe linear trends.
HSS-ID.B.6c
9
Frequencies of bivariate data
What can we conclude from survey results?
(New Name: Interpreting two-way tables)
HSS-ID.B.6
10
Trends in categorical data
Determine the trends in the data table.
HSS-ID.B.5
11
Types of Statistical Studies
A study of the types of data and statistical studies.
HSS-ID.C.9


Probability

12
Simple Probability
Probability is the “Winners” divided by the “Possibilities.”
7.SP.C.7
13
Basic Set Notation
How to write the different combinations of Sets.
HSS-CP.A.1
14
Identifying dependent and independent events
Determine what is independent and dependent data
HSS-CP.A.2
15
Dependent Probability
Find the conditional probability of A given B as the fraction of B’s outcomes that also belong to A, and interpret the answer in terms of the model.
HSS-CP.B.6
16
Adding Probabilities
Apply the Addition Rule, P(A or B) = P(A) + P(B) – P(A and B), and interpret the answer in terms of the model.
HSS-CP.B.7
17
Multiplying Dependent Probabilities
Apply the general Multiplication Rule in a uniform probability model, P(A and B) = P(A)P(B|A) = P(B)P(A|B), and interpret the answer in terms of the model.
HSS-CP.B.8
18
Combinations
Introduction combination problems
HSS-CP.B.9
19
Permutations
Introduction permutation problems
HSS-CP.B.9
20
Permutations and combinations
Use permutations and combinations to compute probabilities of compound events and solve problems.
HSS-CP.B.9
21
Probability with permutations and combinations
Use permutations and combinations to compute probabilities of compound events and solve problems.
HSS-CP.B.9


Probability to make decisions

22
Constructing Probability
Graph the corresponding probability distribution using the same graphical displays as for data distributions.
HSS-MD.A.1
23
Expected Value
Calculate the expected value of a random variable; interpret it as the mean of the probability distribution.
HSS-MD.A.2
24
Expected values with calculated probabilities
Develop a probability distribution for a random variable defined for a sample space in which theoretical probabilities can be calculated; find the expected value.
HSS-MD.A.3
25
Expected values with empirical probabilities
Develop a probability distribution for a random variable defined for a sample space in which probabilities are assigned empirically; find the expected value.
HSS-MD.A.4
26
Making decisions with expected values
Calculate the expected value of a random variable; interpret it as the mean of the probability distribution.
HSS-MD.B.5