Understanding Linear Regression and Correlation

Understanding Linear Regression and Correlation

Assessment

Interactive Video

Mathematics, Science

7th - 10th Grade

Hard

Created by

Aiden Montgomery

FREE Resource

The video tutorial explains how to analyze data points where x represents age and y represents the distance a baseball is thrown. It guides through entering data into Desmos, visualizing it, and using a graphing calculator to find the line of best fit and correlation coefficient. The tutorial emphasizes the positive correlation between age and throwing distance, and demonstrates how to use the model to predict outcomes for unrecorded ages.

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10 questions

Show all answers

1.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What does the variable 'x' represent in the given problem?

The height of a person

The weight of a baseball

A person's age in years

The distance a baseball is thrown

2.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What is the first step in analyzing the data points?

Drawing a scatter plot

Finding the average age

Calculating the median distance

Entering data into lists

3.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What tool is used to visualize the data points and find the line of best fit?

Microsoft Excel

Python

Desmos

Google Sheets

4.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What does a positive correlation indicate in this context?

As age increases, throwing distance decreases

As age increases, throwing distance increases

There is no relationship between age and throwing distance

Throwing distance is constant regardless of age

5.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

Which function on the graphing calculator is used to calculate the line of best fit?

Exponential Regression

Linear Regression

Logarithmic Regression

Quadratic Regression

6.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What is the approximate value of the correlation coefficient 'r' found in the video?

1.5

0.934

0.2

0.5

7.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What does a high 'r' value indicate about the model?

The model is too complex

The model is irrelevant

The model is a good fit for the data

The model is not reliable

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