SNHU Housing Price Prediction Model Project

User Generated

RnmlR89

Mathematics

Southern New Hampshire University

Description

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 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, 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{"version":"1.1","math":"b1"} and intercept b0{"version":"1.1","math":"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?


Unformatted Attachment Preview

Region East North Central East North Central East North Central East North Central East South Central East North Central East South Central East North Central East South Central East North Central East South Central East North Central East South Central East North Central East South Central East North Central East South Central East North Central East South Central East North Central East North Central East North Central East North Central East North Central East North Central East North Central East North Central East North Central East South Central East North Central East South Central East North Central East North Central East North Central East South Central East North Central East North Central East North Central East North Central East North Central East South Central East North Central East South Central East South Central East South Central State County median listing price oh wayne $185,393 oh clermont $305,466 in porter $309,355 wi kenosha $277,265 ky mccracken $173,171 il kendall $266,967 tn washington $250,241 in vigo $94,994 tn maury $320,556 il williamson $130,832 tn davidson $368,237 in vanderburgh $129,848 tn dickson $266,698 il jackson $99,574 ky fayette $307,807 il madison $128,071 ky jefferson $272,991 in bartholomew $280,054 ky campbell $266,046 il la salle $127,516 oh knox $192,226 il peoria $127,413 il rock island $121,610 oh fairfield $241,069 oh scioto $116,585 in henry $83,253 oh ashland $145,082 oh muskingum $178,592 al cullman $230,323 oh ashtabula $136,560 tn hawkins $173,769 oh lake $200,285 in madison $105,965 wi dane $372,965 al colbert $174,531 wi waukesha $454,316 in hendricks $290,093 mi grand traverse $354,811 wi douglas $202,147 il henry $110,053 tn rutherford $327,340 mi barry $226,830 ms hancock $222,636 ms desoto $272,396 tn madison $169,445 median $'s per square foot $105 $115 $124 $141 $89 $119 $112 $63 $148 $76 $204 $81 $150 $64 $132 $82 $125 $101 $93 $84 $92 $71 $88 $113 $74 $57 $88 $89 $107 $76 $98 $103 $63 $168 $82 $172 $108 $194 $121 $69 $141 $116 $114 $103 $82 median square feet 1651 1828 2401 1840 1967 2140 2131 1606 2299 1651 1954 1695 1840 1620 2308 1591 2059 2712 1331 1482 1686 1811 1440 1961 1591 1545 1556 1833 2043 1539 1680 1751 1709 2132 2025 2451 2662 1696 1588 1449 2390 1928 1826 2446 1978 East North Central East North Central East North Central East North Central East North Central East North Central East South Central East South Central East South Central East South Central East South Central East South Central East North Central East North Central East North Central East North Central East South Central East South Central East North Central East North Central East South Central East North Central East North Central East South Central East South Central East North Central East North Central East North Central East North Central East North Central East South Central East North Central East South Central East North Central East South Central East North Central East North Central East North Central East North Central East North Central East North Central East South Central East South Central East North Central East South Central East North Central East North Central wi oh mi mi mi mi al al al ms ky tn oh oh oh mi ms tn wi il tn in wi tn ms in wi mi oh mi ky il tn oh al oh oh il il oh wi ms al il al mi wi sauk darke saginaw eaton ionia lapeer jefferson russell etowah hinds hardin putnam tuscarawas miami huron jackson lamar bradley st. croix sangamon robertson marion dodge sevier lafayette wayne racine clinton stark wayne franklin knox jefferson richland marshall warren jefferson stephenson winnebago marion sheboygan forrest coffee coles baldwin calhoun milwaukee $252,807 $114,255 $135,287 $200,486 $173,249 $250,866 $236,029 $171,943 $171,312 $160,493 $215,134 $267,793 $147,157 $224,955 $137,148 $161,705 $229,208 $248,360 $325,794 $145,027 $293,579 $210,745 $229,174 $321,584 $272,587 $91,427 $265,164 $242,100 $167,376 $129,273 $215,678 $106,418 $291,508 $124,954 $231,376 $361,191 $97,735 $106,561 $133,320 $99,970 $242,461 $138,286 $180,057 $93,141 $343,107 $149,855 $170,059 $131 $74 $83 $100 $98 $140 $120 $80 $79 $81 $98 $119 $85 $95 $80 $93 $89 $116 $158 $81 $138 $96 $117 $155 $131 $64 $133 $121 $91 $103 $103 $67 $131 $79 $97 $113 $64 $60 $76 $75 $129 $56 $91 $65 $157 $85 $120 1832 1563 1581 1914 1792 1854 1762 2027 2051 1983 2079 2255 1503 1744 1667 1676 1765 2179 2094 1781 2216 2126 1736 2126 1705 1541 1701 1991 1726 1327 1937 1590 2189 1552 2307 1941 1405 1704 1676 1446 1643 1358 1945 1591 1991 1686 1440 East South Central East North Central East North Central East North Central East North Central East North Central East South Central East South Central East North Central East North Central East North Central East South Central East North Central East South Central East South Central East North Central East South Central East North Central East South Central East South Central East North Central East South Central East North Central East North Central East South Central East South Central East North Central East North Central East North Central East North Central East North Central East South Central East North Central East South Central East North Central East North Central East North Central East North Central East North Central East South Central East North Central East South Central East North Central East South Central East North Central East North Central East North Central al mi in il il mi tn tn wi mi oh al wi al al in tn in ky al mi al wi mi ky tn wi in oh oh oh tn wi ky il il mi wi mi tn oh ky mi al wi mi oh montgomery bay grant ogle whiteside muskegon williamson anderson washington isabella lucas elmore la crosse morgan autauga allen cumberland elkhart madison talladega washtenaw houston portage shiawassee warren hamilton marathon st. joseph washington summit clark montgomery jefferson pulaski cook tazewell st. joseph brown monroe sullivan union daviess livingston mobile walworth macomb hamilton $198,906 $102,450 $75,309 $178,639 $103,858 $186,990 $638,563 $243,971 $338,276 $162,376 $135,854 $230,043 $265,857 $208,151 $222,915 $212,572 $244,879 $198,174 $223,330 $164,177 $386,643 $187,302 $217,479 $148,682 $273,027 $318,038 $206,244 $173,080 $179,427 $143,028 $122,454 $258,870 $284,994 $181,216 $314,629 $138,981 $190,389 $290,112 $200,123 $203,127 $251,187 $200,036 $320,810 $179,192 $331,738 $227,820 $270,027 $90 $76 $53 $90 $71 $102 $196 $110 $148 $89 $89 $101 $118 $92 $97 $98 $113 $92 $107 $90 $191 $93 $106 $96 $122 $134 $99 $90 $92 $90 $83 $117 $144 $74 $166 $78 $100 $125 $117 $97 $122 $103 $161 $93 $166 $135 $110 2070 1434 1510 1867 1457 1696 3314 2057 2116 1751 1598 2045 2127 2236 2268 1927 2030 2117 2003 1810 2005 2082 2037 1512 2141 2320 2012 1839 1866 1531 1471 2273 1988 1588 1442 1803 1847 1984 1721 2038 1928 1885 1969 1830 1879 1654 1571 East South Central East North Central East North Central East North Central East South Central East North Central East North Central East North Central East North Central East North Central East South Central East South Central East North Central East North Central East North Central East South Central East North Central East South Central East North Central East North Central East North Central East South Central East South Central East North Central East North Central East North Central East North Central East North Central East North Central East South Central East North Central East North Central East North Central East South Central East North Central East North Central East South Central East North Central East North Central East South Central East South Central East North Central East North Central East North Central East North Central East North Central East North Central ms oh mi in ky in mi wi in mi al al wi il in al mi ky il in oh ky tn oh in il oh in oh ms oh oh oh ms oh mi tn oh il tn ms oh il mi il oh mi madison medina marquette johnson christian monroe van buren manitowoc la porte st. clair tuscaloosa calhoun rock kane hancock madison genesee laurel mchenry boone lorain boone blount columbiana clark grundy seneca morgan franklin jackson portage cuyahoga erie lauderdale lawrence oakland hamblen mahoning champaign mcminn lee licking vermilion montcalm st. clair trumbull kalamazoo $383,014 $295,120 $197,664 $274,236 $157,317 $310,446 $288,037 $156,276 $251,713 $220,498 $234,492 $139,222 $218,531 $321,961 $322,137 $272,291 $175,373 $185,918 $279,481 $448,568 $193,257 $303,341 $305,363 $123,632 $249,307 $254,322 $111,671 $235,101 $277,720 $173,821 $222,828 $167,181 $220,956 $150,470 $136,922 $356,046 $204,633 $130,292 $191,054 $179,252 $217,755 $247,030 $78,821 $165,194 $156,788 $105,326 $247,848 $138 $116 $116 $106 $95 $137 $151 $90 $114 $125 $123 $81 $114 $136 $106 $107 $108 $94 $124 $118 $96 $88 $132 $71 $121 $118 $71 $104 $137 $92 $104 $89 $106 $72 $86 $165 $103 $72 $107 $98 $96 $118 $54 $99 $82 $62 $113 2823 2392 1498 2641 1641 2113 1927 1745 1898 1674 1808 1699 1802 2329 3098 2588 1585 1938 2173 3700 1816 1357 2368 1505 1876 1936 1632 2267 1822 1896 1976 1722 1726 1985 1574 2141 2077 1597 1760 1800 2305 1963 1577 1678 1767 1532 2067 East North Central East North Central East South Central East North Central East North Central East South Central East South Central East North Central East North Central East South Central East North Central East South Central East South Central East North Central East South Central East South Central East South Central East North Central East North Central East South Central East South Central East South Central East North Central East South Central East North Central East South Central East North Central East South Central East South Central East South Central East North Central East North Central East North Central East North Central East North Central East North Central East North Central East North Central East North Central East North Central East South Central East North Central East South Central East North Central East North Central East South Central East North Central wi in tn in in ms ms oh oh tn oh tn al il ms ky al in il ms tn tn oh ky il ky mi tn ky ms in wi mi mi oh wi in il in in al il al in wi tn oh grant floyd coffee kosciusko warrick rankin oktibbeha ross sandusky carter montgomery wilson shelby macoupin warren bullitt blount tippecanoe lake pearl river roane knox hancock scott mclean henderson ingham sumner jessamine lowndes howard wood cass midland delaware chippewa lawrence will hamilton dearborn st. clair macon dale delaware eau claire loudon pickaway $166,765 $232,319 $245,765 $252,047 $253,514 $269,900 $256,304 $147,978 $121,552 $161,777 $135,456 $390,161 $337,981 $94,494 $144,309 $263,951 $196,315 $260,405 $408,310 $188,360 $275,057 $303,574 $212,813 $284,542 $157,728 $169,269 $157,511 $349,224 $313,381 $172,746 $124,139 $165,051 $247,961 $194,042 $440,864 $226,137 $155,672 $289,846 $378,444 $220,555 $227,248 $106,893 $132,211 $107,724 $230,510 $376,042 $187,420 $89 $105 $116 $131 $106 $120 $141 $99 $76 $95 $82 $153 $137 $64 $80 $127 $104 $110 $159 $98 $118 $126 $105 $124 $89 $94 $95 $147 $124 $84 $77 $85 $138 $101 $143 $121 $87 $132 $115 $95 $122 $61 $73 $69 $117 $141 $108 1877 2098 2093 1864 2413 2228 1675 1473 1633 1699 1485 2606 2466 1494 1833 1964 1862 2255 2529 1945 2088 2406 1934 2291 1780 1781 1628 2431 2492 1995 1571 1817 1707 1786 3099 1720 1745 2181 3483 1778 1859 1749 1739 1540 1880 2647 1795 East South Central East North Central East North Central East North Central East North Central East North Central East North Central East South Central East North Central East North Central East North Central East North Central East South Central East North Central East South Central East North Central East South Central East South Central East South Central East South Central East North Central East South Central East North Central East North Central East South Central East North Central East South Central East North Central East South Central East North Central East North Central East North Central East North Central East North Central East North Central tn mi mi wi il mi oh ky oh mi wi mi ms il ms oh al al ky al wi al wi oh al oh ky in ky wi il wi il oh oh greene ottawa lenawee ozaukee dupage allegan geauga kenton allen berrien columbia kent jones kankakee harrison greene jackson lauderdale hopkins lee outagamie walker fond du lac belmont limestone butler boyd lake oldham calumet dekalb winnebago adams wood athens Mean Median Standard Deviation $200,778 $317,160 $172,305 $495,568 $412,547 $302,151 $361,668 $239,476 $135,852 $332,796 $272,602 $294,443 $162,864 $151,231 $215,519 $219,241 $182,322 $200,476 $136,254 $283,911 $288,793 $155,773 $172,589 $119,143 $262,265 $236,344 $131,828 $215,915 $390,609 $193,293 $210,494 $224,277 $135,215 $246,079 $169,160 $220,584 $215,134 84090.8833 $102 $148 $108 $176 $181 $157 $115 $79 $82 $146 $128 $136 $65 $96 $105 $98 $93 $92 $83 $117 $123 $81 $97 $76 $110 $94 $69 $111 $140 $103 $108 $110 $88 $118 $68 1936 2118 1626 2472 2203 1913 2758 1176 1626 2131 1968 2098 1891 1577 1942 1679 2003 2098 1730 2381 2079 1813 1682 1546 2368 1582 1845 1902 2818 1789 1844 1818 1598 2075 1090 1916 1856 365.9382617 Random_number 0.527502266 0.808334531 0.376016944 0.844372541 0.533450382 0.214533575 0.770496674 0.955531649 0.09413987 0.615241041 0.728639202 0.609221011 0.502998494 0.237397285 0.413228048 0.260519748 0.636188179 0.637835968 0.437798643 0.108155824 0.577207089 0.218087317 0.974670769 0.864079868 0.252349998 0.250920945 0.51184694 0.637878497 0.399781619 0.063795101 0.029196792 0.341104016 0.324313747 0.964625476 0.54751894 0.510682883 0.724442584 0.967748935 0.305239883 0.492804136 0.117465784 0.301779025 0.255844414 0.863654999 0.034963998 0.074986868 0.262962766 0.919360392 0.642467621 0.226857776 0.027943144 0.648004861 0.984211556 0.612052179 0.102332596 0.85072666 0.117010138 0.647520741 0.399960942 0.919473842 0.598116644 0.094310928 0.142608217 0.223298196 0.273969729 0.50944626 0.289098201 0.469812839 0.743567805 0.80366188 0.54454862 0.034315693 0.566845458 0.254362656 0.998706296 0.595260443 0.180693596 0.096062215 0.46648806 0.075033746 0.425507906 0.775150735 0.636294387 0.243376501 0.6479858 0.005589045 0.364560696 0.306108489 0.800566717 0.172161757 0.656092215 0.472463466 0.700848709 0.873832567 0.64436122 0.732897172 0.996504028 0.072216893 0.879133307 0.787593755 0.409926008 0.551063466 0.633476184 0.523654868 0.596712493 0.150929189 0.012244021 0.244989771 0.120635511 0.384410961 0.110711259 0.911055822 0.46149487 0.903793659 0.142018478 0.946583133 0.144466702 0.160574552 0.862723536 0.782758836 0.11955102 0.471154375 0.48766515 0.311064132 0.020213217 0.800678822 0.092255784 0.001454076 0.884655376 0.504661988 0.07181634 0.249590253 0.392464156 0.000749634 0.999275774 0.348574613 0.643232628 0.547924484 0.698550004 0.028586974 0.529945952 0.806522216 0.650609501 0.853019941 0.5943683 0.037993379 0.831733288 0.475690338 0.653051164 0.632608078 0.797727467 0.052885514 0.322618135 0.577503727 0.526388626 0.333782047 0.539736338 0.330585661 0.760306519 0.044039527 0.804672682 0.364511777 0.123358321 0.68910467 0.261040928 0.764174236 0.283558125 0.468381804 0.701899734 0.128672053 0.759241913 0.898847517 0.467750904 0.558739683 0.532856474 0.991168759 0.85992574 0.007387234 0.349944889 0.028418116 0.266363248 0.559395967 0.334689168 0.420127866 0.634505395 0.160236255 0.574062599 0.572191659 0.545123273 0.547374837 0.764040341 0.992748126 0.09388334 0.089407457 0.915423437 0.20423816 0.992279723 0.6412799 0.630566876 0.546146399 0.601087879 0.922634579 0.789196172 0.956337453 0.153157798 0.463152522 0.446352074 0.712991013 0.679052415 0.401677484 0.718429943 0.234462978 0.863719754 0.460791003 0.774487442 0.64273812 0.176444981 0.726621278 0.959028739 0.831449614 0.528141339 0.176088096 0.383260165 0.467611 0.39166359 0.117186123 0.969095409 0.771238936 0.19059531 0.229988594 0.037696212 0.684189533 0.429820363 0.788474561 0.059678803 0.550575152 0.800264701 0.02270326 0.98669295 0.94481513 0.211413113 0.091230893 0.120482211 0.73638933 0.796058705 0.965316742 0.228941907 0.745356684 0.142779564 0.039152781 0.830788729 0.857105269 0.031556816 0.403974841 0.489306274 0.733215047 0.266184621 0.169091685 0.468220643 0.293200383 0.352117681 0.146270746 0.697502208 0.317657498 0.51346145 0.198613964 0.679223826 0.074478903 median listing Region State County price East North Central oh wayne $185,393 East North Central oh clermont $305,466 East North Central in porter $309,355 East North Central wi kenosha $277,265 East South Central ky mccracken $173,171 East North Central il kendall $266,967 East South Central tn washington $250,241 East North Central in vigo $94,994 East South Central tn maury $320,556 East North Central il williamson $130,832 East South Central tn davidson $368,237 East North Central in vanderburgh $129,848 East South Central tn dickson $266,698 East North Central il jackson $99,574 East South Central ky fayette $307,807 East North Central il madison $128,071 East South Central ky jefferson $272,991 East North Central in bartholomew$280,054 East South Central ky campbell $266,046 East North Central il la salle $127,516 East North Central oh knox $192,226 East North Central il peoria $127,413 East North Central il rock island $121,610 East North Central oh fairfield $241,069 East North Central oh scioto $116,585 East North Central in henry $83,253 East North Central oh ashland $145,082 East North Central oh muskingum $178,592 East South Central al cullman $230,323 East North Central oh ashtabula $136,560 Mean $204,460 Median $188,810 Standard Deviation 81962.07 median $'s per square foot $105 $115 $124 $141 $89 $119 $112 $63 $148 $76 $204 $81 $150 $64 $132 $82 $125 $101 $93 $84 $92 $71 $88 $113 $74 $57 $88 $89 $107 $76 median square feet Random_number 1651 0.66409 1828 0.855047 2401 0.370035 1840 0.761581 1967 0.188345 2140 0.338412 2131 0.570176 1606 0.462966 2299 0.724548 1651 0.539727 1954 0.041215 1695 0.129131 1840 0.981948 1620 0.10054 2308 0.85583 1591 0.696185 2059 0.631723 2712 0.719486 1331 0.876527 1482 0.742444 1686 0.201899 1811 0.981197 1440 0.633991 1961 0.774657 1591 0.702983 1545 0.640731 1556 0.012114 1833 0.010297 2043 0.357546 1539 0.615698 1837 1819 319.6364 median listing price $185,393 $305,466 $309,355 $277,265 $173,171 $266,967 $250,241 $94,994 $320,556 $130,832 $368,237 $129,848 $266,698 $99,574 $307,807 $128,071 $272,991 $280,054 $266,046 $127,516 $192,226 $127,413 $121,610 $241,069 $116,585 $83,253 $145,082 $178,592 $230,323 $136,560 Scatter Plot showing Me $400,000 $350,000 Median Listing Price Region State County East North Central oh wayne East North Central oh clermont East North Central in porter East North Central wi kenosha East South Central ky mccracken East North Central il kendall East South Central tn washington East North Central in vigo East South Central tn maury East North Central il williamson East South Central tn davidson East North Central in vanderburgh East South Central tn dickson East North Central il jackson East South Central ky fayette East North Central il madison East South Central ky jefferson East North Central in bartholomew East South Central ky campbell East North Central il la salle East North Central oh knox East North Central il peoria East North Central il rock island East North Central oh fairfield East North Central oh scioto East North Central in henry East North Central oh ashland East North Central oh muskingum East South Central al cullman East North Central oh ashtabula median square feet 1651 1828 2401 1840 1967 2140 2131 1606 2299 1651 1954 1695 1840 1620 2308 1591 2059 2712 1331 1482 1686 1811 1440 1961 1591 1545 1556 1833 2043 1539 $300,000 $250,000 $200,000 $150,000 $100,000 $50,000 $0 0 Scatter Plot showing Median Square feet against median listing price y = 175.97x - 118809 median listing price Linear (median listing price) 500 1000 1500 2000 Median Square Feet 2500 3000 Selling Price and Area Analysis for D.M. Pan National Real Estate Company Report: Selling Price and Area Analysis for D.M. Pan National Real Estate Company E Southern New Hampshire University 1 Selling Price Analysis for D.M. Pan National Real Estate Company 2 Introduction With the real estate industry is growing fast and many investors are now considering staking their money on this venture. With the increasing growth of big data, many Real estate owners now considers to apply data to help them in pricing, and knowing the demands of people and this helps them in making better decision especially in capturing the full needs of their clients. To show the trends in the growth of day to day needs of people, price changes, government taxation most of the real estate owners employees linear regression analysis which when two variables of choice are compared and their relationship and pattern is determined, a better understanding is derived from the analysis. Thus, this report works to explicitly explain how the average median square feet affects the average median listing price from a given random sample of 30 from a selected region from the National Statistics data. A scatter plot is then used to show the relationship and pattern of the variables and then gives the trend line and the regression equation which is then used to predict the listing price of a certain house from a given square footage. Representative Data Sample East North Central was my region of choice, considering it had large stream of data comparatively and that would give me a better representation of the entire data from the sample size created. The random sample generated is presented in excel workbook as well as the mean, median, and standard deviation of the median listing price and the median square foot variables. Data Analysis The average Median listing price which actually signifies the asking price of most of the homes in East North Central Region is at $220,584 with an average of 1916 Median Square feet. Comparing this metrics with the National Statics where all regions have an average of $288,408 Selling Price Analysis for D.M. Pan National Real Estate Company 3 and 1945 Median Listing Price and Median Square feet respectively. This implies that this region is a true reflection of the national data and it’s statistically significant for conducting an analysis for the overall data. The figures show that as the economy deteriorates, there is an increasingly significant impact on the real estate industry from the regional level to the national at large. Thus, as the economy continues to decline home and property buying also feel the impact. The random sample used in this analysis is extremely random as I have generated it using Microsoft excel from the data of the selected region (East North Central). The procedure applied for generating the random sample include: first, adding a new column giving it a name ‘Random_Number’. Then in the first cell immediately after the column head, insert the function “=RAND (); which when you press enter a random number is generated automatically. Copy this generated random number from the first cell and paste it in the other cells in the same column to generate other random numbers then after ensuring all the rows contains each random number, finally sort all the records by Random_Number column. From that I chose the first 30 which then became the random sample. Scatterplot Selling Price Analysis for D.M. Pan National Real Estate Company 4 Scatter Plot showing Median Square feet against median listing price $400,000 y = 175.97x - 118809 Median Listing Price $350,000 $300,000 $250,000 $200,000 median listing price $150,000 Linear (median listing price) $100,000 $50,000 $0 0 500 1000 1500 2000 2500 3000 Median Square Feet The Pattern Scatter plots are very useful graphs for explaining the trends within the data. Each point within the plot has two coordinates termed as x and y variables. In this case we use Median Square Feet as the x-variable which is on the x-axis and Median Listing Price as the y-variable which lies on the vertical axis. The two variables x and y indicates a positive relationship as they tend to move from left to right; implying that as the values on the x-axis increases, the y-values also increases; and that leads to an Uphill pattern which is its shape. Outliers are points that are far from the trend line or regression line. And in this case points [1954, $368,237] and [2712, $280,054] are considered to be the main outliers. With a square footage of 2712, this home is considered outlier since it attracts more money to buy and few can afford to get it and on the other hand features such as desirable location or enhanced amenities like high advanced securities with CCTV’s installed, swimming pools, more number of bedrooms and bathrooms leads to an increased money for Selling Price Analysis for D.M. Pan National Real Estate Company 5 purchasing houses such as 1954 and that’s explains why it’s regarded as an outlier since its listing price is at $368,237 which is a lot of money for average people. The trend line equation, which is also the regression equation as shown in the scatter plot is y=175.97x – 118809 where y is dependent variable (Median Listing Price) and x is the predictor variable (Median square feet). Therefore if 1200 square foot house is given, it will be listed at $92,355 because if you replace x in the equation with 1200 you get y = 175.97(1200) 118809 =92,355.
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Housing Price Prediction Model for D.M. Pan Real Estate Company
[Your Name]
Southern New Hampshire University

Median Housing Price Prediction Model for D.M. Pan National Real Estate Company

2

Module Two Notes
Scatter Plot
$400,000
$350,000
y = 175.97x - 118809

Listing Price

$300,000
$250,000
$200,000
$150,000
$100,000
$50,000
$0
0

500

1000

1500
Square Feet

2000

2500

3000

Regression Equation
The regression equation for the line of best fit that I obtained using the scatterplot from
Module Two assignment is:
𝐇𝐨𝐮𝐬𝐞 𝐥𝐢𝐬𝐭𝐢𝐧𝐠 𝐩𝐫𝐢𝐜𝐞 = −𝟏𝟏𝟖𝟖𝟎𝟗 + 𝟏𝟕𝟓. 𝟗𝟕 ∗ 𝐬𝐪𝐮𝐚𝐫𝐞 𝐟𝐨𝐨𝐭𝐚𝐠𝐞
Determine r
The r coefficient between these 2 variables is 0.6862 and it means that the variables are
weakly positively correlated in the same direction, if one increases the other one will probably
increase. The direction of the association is positive or direct, we can determine that based both
in the sign of the correlation coefficient and also by looking at the scatterplot.
Examine the Slope and Intercepts
The slope of the regression line is $175.97, which means that on average the listing price of a
house increase by 175.97 per additional square feet. The intercept is -$118,809; and this

Median Housing Price Prediction Model for D.M. Pan National Real Estate Company

3

represents the price of a value with 0 square foot, which can’t be interpreted as the value of only
the land, since it’s negative. Even if the value would have made sense we should be careful about
it since the smallest house in the sample has a size of 1,331 sq. ft. which implies that the
intercept is an extrapolation of the data and it could be biased.
R-squared Coefficient
The R-squared is 0.4709 for this relationship, and it means that 47.09% of the variability
in the listing prices could be explained by the variability in the square footage. This is telling us
that the size of the house is one of the main factors to predict its listing price, but clearly there is
a big share that is not explained by size.
Conclusions
The relationship between square feet and sales price was not as strong as I expected, the
main reason might be that other variables such us the number of bedrooms the age of the house
and the quality of the building could be important characteristics when determining the price of a
home.
The worst part was the negative intercept I would have like to have a value that could
represent the value of land only. Anyway, if we compare this to the overall homes in the United
States, this region is clearly cheaper, mainly because houses are smaller in this region.
The equation I found, despite being not a perfect estimate it’s very useful to have a first
educated guess of the price of the house based only on its size, this is very easy to obtain and a
very quick tools to start with house valuations. Still, this would only work for houses between
1,300 sq2 and 2700 sq2, since these where the range of square footage in our sample.
Finally, for every 100 square feet, the listing price of the house goes up on average by
$17597.


Region
East North Central
East North Central
East North Central
East North Central
East South Central
East North Central
East South Central
East North Central
East South Central
East North Central
East South Central
East North Central
East South Central
East North Central
East South Central
East North Central
East South Central
East North Central
East South Central
East North Central
East North Central
East North Central
East North Central
East North Central
East North Central
East North Central
East North Central
East North Central
East South Central
East North Central
East South Central
East North Central
East North Central
East North Central
East South Central
East North Central
East North Central
East North Central
East North Central
East North Central
East South Central
East North Central
East South Central
East South Central
East South Central
East North Central
East North Central

median $'s per square
State
County median listing price
foot
oh
wayne
$185,393
$105
oh
clermont
$305,466
$115
in
porter
$309,355
$124
wi
kenosha
$277,265
$141
ky
mccracken
$173,171
$89
il
kendall
$266,967
$119
tn
washington
$250,241
$112
in
vigo
$94,994
$63
tn
maury
$32...


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