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 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 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 Summary Statistics and Graphs Real Estate Data PDF 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 Word Document, 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 slope and intercept . 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 x0, 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? You can use the following tutorial that is specifically about this assignment: MAT-240 Module 3 Assignment Video What to Submit Submit your completed Module Three Assignment Template as a Word document that includes your response, supporting charts/graphs, and your Excel file. I included the original analysis as well

Early paper

Real Estate Report

Students Name

Institutional-Affiliation

Instructor

Course

Due-Date

Real Estate Report

The data set provides several American counties' median selling price per unit. Square footage pricing is essential even though it only sometimes reflects everything investors want. The number of units bought in a given area multiplied by the average cost per square unit produces the mean price per square foot. Constructing a new home involves extensive use of cost per square foot. Since identical building materials are used in every project, the cost per square foot to rebuild a home will always be approximately the same for all buildings in a region.

Data

Median sample data were selected randomly from the Mountain area.

State	County	listing price in $		Square feet in thousand units
Arizona	Cochise	200,261		1.7920
Arizona	Coconino	471,580		2.2170
Arizona	Gila	360,235		1.9440
Arizona	Maricopa	394,964		2.2490
Arizona	Mohave	304,891		1.7650
Arizona	Navajo	300,469		1.9260
Arizona	Pima	291,769		2.0270
Arizona	Pinal	253,131		2.0130
Arizona	Yavapai	457,534		2.2160
Arizona	Yuma	228,601		1.6840
Colorado	Adams	418,735		2.5760
Colorado	Arapahoe	450,405		2.5270
Colorado	Boulder	620,894		2.6870
Colorado	Broomfield	598,153		3.5170
Colorado	Denver	534,393		1.6800
Colorado	Douglas	595,512		3.9450
Colorado	Eagle	699,443		1.7650
Colorado	El Paso	421,507		3.0500
Colorado	Garfield	611,190		2.4450
Colorado	Jefferson	542,946		2.7280
Colorado	La Plata	540,468		2.0920
Colorado	Larimer	446,208		2.6610
Colorado	Mesa	348,958		1.9660
Colorado	Pueblo	260,652		2.0780
Colorado	Weld	417,120		2.9530
Idaho	Ada	399,254		2.2850
Idaho	Bannock	255,125		2.5060
Idaho	Bonneville	283,417		2.8140
Idaho	Canyon	287,786		2.0240
Idaho	Kootenai	452,679		2.2340
	Average		$414,943		$2,346
	Median		$417,928		$2,226
	Standard deviation		$131,831		$528

Analyzing the Data

Square Feet

AVERAGE	SECOND QUARTILE OR MEDIAN	STANDARD DEVIATION	FIRST QUARTILE	THIRD QUARTILE	RANGE	MINIMA	MAXIMA
2345.577	2225.521	536.6081	1960.74	2667.33	2265.292	1679.667	3944.958

Listing Price

AVERAGE	SECOND QUARTILE OR MEDIAN	FIRST QUARTILE	THIRD QUARTILE	STANDARD DEVIATION	SKEWNESS	RANGE	MINIMA	MAXIMA
414942.7	417927.5	290772.9	535911.9	134084.8	0.30152	499182.2	200260.6	699442.9

ss

From the graphs above, the average and standard deviations are around the normal range, and the information reflects the countrywide variables. The graphs show that the data is skewed and comparable to the nationwide. This situation can be explained by the fact that the geographical regions were chosen randomly. To ensure the data was random, I chose the 30 counties within the Mountain Region without comparing their statistics.

Scatterplot

The Pattern

The regression function used was (y = 103.46x + 172264)

Therefore, R = 0.1714

The data shows a positive correlation between price and mean square feet.

Therefore, with 1,800 square foot house following the regression equation.

y = 103.46(1,800) + 172264 = 358493

The listed price is $ 358,493.