Simple/Multiple linear regression - Austin - IPP 3 Exam 2

Simple/Multiple linear regression - Austin - IPP 3 Exam 2

Professional Development

19 Qs

quiz-placeholder

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Simple/Multiple linear regression - Austin - IPP 3 Exam 2

Simple/Multiple linear regression - Austin - IPP 3 Exam 2

Assessment

Quiz

Professional Development

Professional Development

Medium

Created by

LECOM DE 2026

Used 44+ times

FREE Resource

19 questions

Show all answers

1.

MATCH QUESTION

1 min • 1 pt

Match the following definitions with their

Variable control

A statistical method that allows you to examine the relationship between two or more variables of interest

Regression Analysis

When not maintained properly leads to bias. controlling for one variable while we change another

Confounder

Situations in which researchers must accept that some of or all of the design without modification

Bias

A form of error. statistical terms/values that do not provide an accurate representation of the population

Nature experiments

Any factor that prevents appropriate statistical interpretation of a results within practical context of a study

2.

MATCH QUESTION

1 min • 1 pt

Match the following definitions with their descriptions

Biased Samples

Any variable that produces a modifying effect

Confounding effect

A specific and observable factor that is omitted from the analysis

Omitted Variable

Fundamentally different estimates then the population parameter

Effect Modification

A variable that, when accounted for, leads to a meaningfully different interpretation of relationship between the primary and independent variables and the dependent variables

Inefficient

The unadjusted results when adjustments lead to changes in any estimate of variation

3.

MATCH QUESTION

1 min • 1 pt

Match the following definitions with their descriptions

Mediating Effect

Simplest process of estimation which assumes dependent variable is continous

Unbiased Sample

Statistical adjustments for the cofounder result in

Residual

A intermediate between the primary independent variable and the dependent variable that can be used to analyze a relationship

Ordinary least squares (OLS)

A value between 0 and 1. As it approaches 0 regression explains very little variation in y. As it approaches 1 it explains it well.

R2

Vertical relationship between each data point and trendline

4.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

An R2 close to 0 indicates a strong correlation and linear relationship

True

False

5.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

Both confounding variables and/or omitted variables can lead to bias

True

False

6.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

For which of the following types of data would you use dummy variables?

Patients height

Time spent drinking coffee

Hours spent traveling

Visited another country

7.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

You design a study to determine if the numbers of hours watching scrubs correlates to the grade range achieved in IPP3. You also consider other factors such as location, video speed used, and average time snacking. Which of the following is the independent variable

Number of hours watching scrubs

Time snacking

Video speed used

Grade acheived

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