Introduction

[Describe the report: Include in this section a brief overview, including the purpose of the report and your approach.]

The purpose of this report is to prepare an analysis report to predict the median listing price based on the square footage by using a linear regression, random sample, graph and table to produce an analysis.

Data Collection

[Sampling the data: Outline how you obtained your sample data, including the response and predictor variables.]

I obtained my sample data by using the function =RAND () in excel. I started by creating a column next to the listing price and naming it random and entered the function=RAND () into the first cell of the column and clicked enter which a gave me my first random number and I copy and paste the function down to the last cell in the data. I then used the sort function to get my random data and I then selected the first 50 regions. My response variable Y=median listing price and predictor variable X=median square feet.

[Scatterplot: Insert a correctly labeled scatterplot of your chosen variables.]

Data Analysis

[Histogram: Insert the histogram of the two variables. Be sure to include appropriate labels.]

[Summary statistics: Insert a table to show the summary statistics.]

Median Square Feet

Mean

2,350

median

1,929

Standard Deviation

1149.18

Median Listing Price

Mean

$361,704.00

Median

$328,300.00

Standard Deviation

$137,050.34

[Interpret the graphs and statistics: Describe the shape, center, spread, and any unusual characteristic (outliers, gaps, etc.) and what they mean based on your sample data and the graphs you created.]

The shape of the graph is a linear with a strong positive slope. The linear regression equation is y-hat=119210.6+103.1737X. This means that the prices go up in price by 103.1737 per square foot and the y intercept is $119210.6. The data correlates with the rest of the United States. It has a weak positive slope to the linear regression line when you compare the median listing price with the median square feet.

[Explain how these characteristics of the sample data compare to the same characteristics of the national population. Also, determine whether your sample is representative of the national housing market sales.]

The Regression Model

[Scatterplot: Include the scatterplot graph of the sample with a line of best fit and the regression equation.]

[Based on your graph, explain whether a regression model can be developed for the data and how.]

The graph can be represented with a linear regression line with a strong positive slope. R=0.865122 and r squared = 0.7484 which correlates to a strong positive linear regression because R is greater than .70

[Discuss associations: Explain the associations in the scatterplot, including the direction, strength, form in the context of your model.]

[Find r: Calculate the correlation coefficient and explain how it aligns with your interpretation of the data from the scatterplot.]

I calculated the correlation R by using the formula =correl (listing price, square feet) to get my data so the correlation R = 0.865122

The Line of Best Fit

[Regression equation: Insert the regression equation.]

y-hat=119210.6+103.1737X

[Interpret regression equation: Interpret the slope and intercept in context.]

[Strength of the equation: Interpret the strength of the regression equation, R-squared.]

[Use regression equation to make predictions: Use the regression equation to make a sample prediction.]

Conclusions

[Summarize findings: Summarize your findings in clear and concise plain language. Outline any questions arising from the study that might be interesting for follow-up research.]