7. Probability & Statistics

7.01 Formulate questions and collect bivariate data

7.02 Scatterplots and lines of fit

7.03 Linear, quadratic, and exponential models

7.04 Univariate data, center, and spread

7.05 Histograms with smooth curves

7.06 Normal distributions

7.08 Permutations and combinations

United States of America Virginia

Algebra 2

7.01 Formulate questions and collect bivariate data

Adaptive

Worksheet

Interactive practice questions

Which of these questions can be answered by collecting bivariate data?

$What is the average height of students in a school?$

A

$How many students prefer online learning over in-person learning?$

B

$Is there a relationship between the amount of sleep students get and their academic performance?$

C

$What is the total sales revenue of a company in the last quarter?$

D

Easy

< 1min

Which of the following scenarios involves collecting bivariate data?

Easy

< 1min

Given a dataset on $daily temperatures$ and $clothing sales$ , which of the following questions would require bivariate data analysis?

Easy

< 1min

$A school collects data on the shoe size of each student in the school.$

Is this an example of univariate data or bivariate data?

Easy

< 1min

Outcomes

A2.ST.2

The student will apply the data cycle (formulate questions; collect or acquire data; organize and represent data; and analyze data and communicate results) with a focus on representing bivariate data in scatterplots and determining the curve of best fit using linear, quadratic, exponential, or a combination of these functions.

A2.ST.2a

Formulate investigative questions that require the collection or acquisition of bivariate data and investigate questions using a data cycle.

A2.ST.2b

Collect or acquire bivariate data through research, or using surveys, observations, scientific experiments, polls, or questionnaires.

A2.ST.2c

Represent bivariate data with a scatterplot using technology.

A2.ST.2g

Make predictions, decisions, and critical judgments using data, scatterplots, or the equation(s) of the mathematical model.

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