Regression Models and Data Interpretation

Regression Models and Data Interpretation

Assessment

Interactive Video

Mathematics

9th - 10th Grade

Hard

Created by

Thomas White

FREE Resource

Mrs. Ga's lesson covers identifying linear, quadratic, and exponential data, and finding regression models for these data types using a graphing calculator. The video includes a warm-up exercise, practical examples, and problem-solving activities to reinforce learning. Students learn to analyze data from tables and scatter plots, and apply regression models to find the best fit for data trends.

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12 questions

Show all answers

1.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What types of data are being identified in this lesson?

Linear, Logarithmic, Polynomial

Linear, Cubic, Logarithmic

Quadratic, Cubic, Exponential

Linear, Quadratic, Exponential

2.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

Why is a graphing calculator necessary for this lesson?

To draw graphs manually

To perform complex calculations

To find regression models

To solve algebraic equations

3.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

In the warm-up exercise, what formula is used to compare Lisa and Laura's accounts?

Linear Growth Formula

Compound Interest Formula

Simple Interest Formula

Exponential Decay Formula

4.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What is the key feature of linear data in a table of values?

Common ratio in x-values

Common difference in x-values

Common ratio in y-values

Common difference in y-values

5.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

How can you identify quadratic data from a table of values?

Common ratio of differences in y-values

Common difference of differences in y-values

Common ratio in y-values

Common difference in y-values

6.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What indicates exponential data in a table of values?

Common difference in x-values

Common ratio in x-values

Common ratio in y-values

Common difference in y-values

7.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

When interpreting scatter plots, what is a key consideration?

Exact quadratic fit

Perfect linearity

General trend estimation

Exact match of data points

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