Sheet1Weeks on the MarketSelling Price1Weeks on the MarketSelling Price23$243,270Coefficient of variation52.15%Coefficient of variation21.30%48$324,360Mean28.9365079365Mean272582.9047619059$168,540Standard Error1.9011507842Standard Error7313.834661976726$267,756Median26Median26775620$232,140Mode32Mode19080040$397,500Standard Deviation15.0899165391Standard Deviation58051.762937503251$346,620Sample Variance227.7055811572Sample Variance3370007180.1520818$190,800Kurtosis1.7963691261Kurtosis0.110433273125$276,660Skewness1.3718794746Skewness0.754780574862$298,920Range69Range25440033$241,680Minimum9Minimum16854011$286,200Maximum78Maximum42294015$193,980Sum1823Sum1717272326$273,480Count63Count6327$222,60056$422,94012$295,7402[PLACE YOUR DIAGRAM FOR PART 2 HERE]29$270,02960$373,01411$198,03433$281,14425$235,62250$417,18864$381,28223$190,80031$329,22578$333,86641$326,26814$317,68219$211,43832$290,70934$311,64070$359,49915$298,69712$375,00024$340,00020$310,000[TYPE YOUR EXPLANATION FOR PART 2 HERE] This would be a good data set for simple linear regression because there appears to ba a positive relationship between the Weeks on the Market and Selling Price. That is as Weeks on the Market increases so to does the Selling Price.28$279,90032$278,50028$273,00012$272,00020$270,00012$270,0003SUMMARY OUTPUT32$258,50028$255,000Regression Statistics24$253,0001- and +1Multiple R0.612885399624$249,000closer to 1 is betterR Square0.37562851332$245,000Adjusted R Square0.365392914928$244,000Standard Error46245.328773413932$241,000Observations6324$239,50028$238,000ANOVA32$236,500dfSSMSFSignificance F20$235,000Regression178483988734.398978483988734.398936.69824739710.000000093428$235,000Residual61130456456435.032138630433.3611424$233,000Total62208940445169.42932$230,00012$229,000CoefficientsStandard Errort StatP-valueLower 95%Upper 95%Lower 95.0%Upper 95.0%24$224,500Intercept204356.26692061512680.235761684316.11612518580179000.568662474229711.965178757179000.568662474229711.96517875716$220,000Weeks on the Market2357.8048184318389.2110743076.05790783990.00000009341579.52918189543136.08045496821579.52918189543136.080454968216$213,0000.000000093420$212,00028$204,000RESIDUAL OUTPUTObservationPredicted Selling PriceResidualsif p value is greater than alpha then value not good.1258585.777744548-15315.77774454752317530.8982053436829.10179465673225576.510286502-57036.51028650194265659.1921998432096.8078001575251512.363289252-19372.3632892526298668.45965788998831.54034211147324604.31266063922015.68733936128246796.753652388-55996.75365238839263301.38738141113358.612618588810350540.165663389-51620.165663388911282163.825928866-40483.825928865812230292.11992336655907.880076634513239723.339197093-45743.339197092914265659.1921998437820.80780015715268016.997018275-45416.997018274916336393.33675279886546.663247202117232649.92474179763090.075258202618272732.606655138-2703.606655138519345824.55602652527189.443973474820230292.119923366-32258.119923365521282163.825928866-1019.825928865822263301.387381411-27679.387381411223322246.50784220794941.492157793124355255.77530025326026.224699747425258585.777744548-67785.777744547526277448.21629200251776.783707997927388265.042758298-54399.042758298228301026.2644763225241.735523679529237365.53437866180316.46562133930249154.55847082-37716.558470820231279806.02111043410902.97888956632284521.63074729827118.369252702333369402.604210844-9903.604210843634239723.33919709358973.660802907135232649.924741797142350.07525820336260943.58256297979056.417437020737251512.36328925258487.63671074838270374.8018367079525.198163293439279806.021110434-1306.02111043440270374.8018367072625.198163293441232649.92474179739350.075258202642251512.36328925218487.63671074843232649.92474179737350.075258202644279806.021110434-21306.02111043445270374.801836707-15374.801836706646260943.582562979-7943.582562979347260943.582562979-11943.582562979348279806.021110434-34806.02111043449270374.801836707-26374.801836706650279806.021110434-38806.02111043451260943.582562979-21443.582562979352270374.801836707-32374.801836706653279806.021110434-43306.02111043454251512.363289252-16512.36328925255270374.801836707-35374.801836706656260943.582562979-27943.582562979357279806.021110434-49806.02111043458232649.924741797-3649.924741797459260943.582562979-36443.582562979360242081.144015525-22081.144015524761242081.144015525-29081.144015524762251512.363289252-39512.36328925263270374.801836707-66374.80183670664[TYPE YOUR ANSWER TO PART 4 HERE] (Compare p-value with alpha.) Based on an analysis of the p-value of 0.0000000933948 (9.33948E-08), we can see that the regression model is significant at the 1% level of significance. The relationship between the Weeks on the Market and Selling Price is significant at the 1% level of significance. Based on an analysis of the residual plot, the 4 assumptions that were about the regression model's error term appear to be valid. The regression model for the "Weeks on the Market" and "Selling Price" is = 204356.2669 + 2357.8048x. Based on this sample data regression model, 37.56% of the variability in the "Selling Price" can be explained by the "Weeks on the Market". Based on the regression analysis above there appears to be a moderately strong positive relationship between "Weeks on the Market" and "Selling Price". This is evidenced by a strength of relationship measure of 0.6129.= 204356.2669 + 2357.8048x291595.0452025935[TYPE YOUR ANSWER TO PART 5 HERE] = 204356.2669 + 2357.8048*37 = 291595.0452. Based on the current regression model if a condo were on the market for 37 weeks, they could expect it to sell for $291595.0452.

Sheet1

Selling PriceWeeks on the MarketWeeks on the Market vs. Selling Price

Compatibility Report

Weeks on the MarketResidualsWeeks on the Market Residual Plot

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