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FIN534 Chapter 6.1 Trendlines & Regression

Authored by Maryam Othman

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University

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FIN534 Chapter 6.1 Trendlines & Regression
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8 questions

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1.

MULTIPLE CHOICE QUESTION

5 mins • 1 pt

A regression model that involves a single independent variable is called ______.

Single regression

Unit regression

Simple regression

Individual regression

2.

MULTIPLE CHOICE QUESTION

5 mins • 1 pt

Which of the following is true of linear functions used in predictive analytical models?

It is used when the rate of change in a variable decreases at a decreasing level

It is used when there is a steady decrease or increase over a range of a variable

It is used when there is increase at increasing rate

It is used when there is rise or fall at a constant rate

3.

MULTIPLE CHOICE QUESTION

5 mins • 1 pt

______ are mathematical functions used in predictive analytical models which define phenomena that increase at a specific rate, and is represented by the formula y=axby=ax^b  

Exponential functions

Power functions

Polynomial functions

Linear functions

4.

MULTIPLE CHOICE QUESTION

5 mins • 1 pt

 y=ax3+bx2+cx+d?y=ax^3+bx^2+cx+d?  

Which of the following mathematical functions, used in predictive analytical models, is represented by the formula above?

Polynomial functions

Exponential functions

Power functions

Logarithmic functions

5.

MULTIPLE CHOICE QUESTION

5 mins • 1 pt

In Excel's Trendline tool, the value of the ______ gives the measure of fit of line to the data.

linear function

R-squared

p-value

intercept value

6.

MULTIPLE CHOICE QUESTION

5 mins • 1 pt

Which of the following is true of the R-squared value?

A value of 1.0 for R-squared indicates maximum deviation of the data from the line

If the value of R-squared is above 1.0, the line will be at a perfect fit for the data

The value of R-squared will always be between -1 and 1

As the value of R-squared increase, the line will be a better fit for the data

7.

MULTIPLE CHOICE QUESTION

5 mins • 1 pt

For an independent variable Y, the error associated with the  ithi^{th}  observation is:

ei = Yi - Ŷi

Yi = (ei)^2 - Ŷi

(Ŷi)^2 ei = Yi

ei = (Yi + Ŷi)^2

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