Identify the Components of Simple Linear Regression Models and Discuss

question 49

Multiple Choice

Identify the components of simple linear regression models and discuss their applications Use the data given below to answer the following question(s) .
Following is an extract from the database of a construction company. The table shows the height of walls in feet and the cost of raising them. The estimated simple linear regression equation is given as Ŷ = b0 + b1X. (Hint: Use Excel functions) . $$\begin{array} { | l | l | } \hline\begin{array} { l } \text { Height } \\\text { (ft) }\end{array} & \text { Cost (\$) } \\\hline 4 & 670 \\\hline 3 & 430 \\\hline 7 & 810 \\\hline 9 & 1100 \\\hline 6 & 790 \\\hline 8 & 880 \\\hline 5 & 760 \\\hline 11 & 1200 \\\hline\end{array}$$
-Which of the following is true about Excel outputs Multiple R?

• A) It is often referred to as the coefficient of determination.
• B) A value of 0 indicates positive correlation.
• C) A negative slope of the regression line denotes a positive Multiple R.
• D) It is another name for the sample correlation coefficient, r.

---

Definitions:

Cognitive Structure

The cognitive structures and thought models that people employ to make sense of and categorize information.

Associative Network

Model of memory in which nodes or ideas are connected by associative links along which cognitive activation can spread.

Fuzzy Set

A mathematical concept where an element's membership in a set is graded rather than binary, reflecting real-world ambiguities.

Exemplar

A typical or standard example that represents a larger category or idea.