Discuss how you determine the direction of the association between the two variables.

Overview
Recall that samples
are used to generate a statistic, which businesses use to estimate
the
population parameter. You have learned how to take samples from populations and
use them to produce statistics. For two quantitative variables, businesses can
use scatterplots and the correlation coefficient to explore a potential linear
relationship.
Furthermore, they can quantify the relationship in a regression
equation.
Prompt
This assignment picks
up where the Module Two assignment left off(MODULE TWO IS
ATTACHED) and will use components of that
assignment as a foundation.
You have submitted
your initial analysis to the sales team at D.M. Pan Real Estate
Company. You
will continue your analysis of the provided Real
Estate Data spreadsheet using your
selected region to complete
your analysis. You may refer back to the initial
report you developed in the
Module Two Assignment Template to continue the work.
This document and
the National
Statistics and Graphs spreadsheet will support your
work on the
assignment.
Note: In the
report you prepare for the sales team, the dependent, or response, variable (y)
should be the
listing price and the independent, or predictor, variable (x)
should be
the square feet

Using the Module Three Assignment Template(SEE ATTACHMENT), specifically address the following:

Regression
Equation: Provide
the regression equation for the line of best fit using the scatterplot
from the Module Two assignment.
Determine r: Determine r and
what it means. (What is the relationship between the variables?)

Determine
the strength of the correlation (weak, moderate, or strong).
Discuss
how you determine the direction of the association between the two
variables.

Is there a positive or negative
association?
What do you see as the direction
of the correlation?

Examine
the Slope and Intercepts: Examine the slopeb1 and
intercept b0

Draw
conclusions from the slope and intercept in the context of this problem.

Does the intercept make sense
based on your observation of the line of best fit?

Determine
the value of the land only.
Note: You can assume, when the square footage of the house is
zero, that the price is the
value of just the land. This happens
when x=0, which is the y-intercept. Does this
value make sense in context?

Determine
the R-squared Coefficient: Determine the R-squared
value.

Discuss
what R-squared means in the context of this analysis.

Conclusions: Reflect on
the Relationship: Reflect on the relationship between square feet and
sales price by answering the following questions:

Is
the square footage for homes in your selected region different than for
homes overall in the United States?
For
every 100 square feet, how much does the price go up (i.e., can you use
slope to help
identify price changes)?
What
square footage range would the graph be best used for?

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