symphony
nazneen
31 December 2017
setwd("C:/Users/Taiyyab Ali/Desktop/R language")
Housingpriceindex <- read.csv("Housingpriceindex.csv")
View(Housingpriceindex)cor(Housingpriceindex$index_nsa,Housingpriceindex$Population,method = "pearson")## [1] 0.1021095Positive correlation between population and housing price index, as expected with increase in population housing price index increases but slop of housing price index is very high in comparison to population, which can be the reason of weak correlation.
cor.test(Housingpriceindex$index_nsa,Housingpriceindex$Population,method = "pearson")##
## Pearson's product-moment correlation
##
## data: Housingpriceindex$index_nsa and Housingpriceindex$Population
## t = 4.2397, df = 1706, p-value = 2.358e-05
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.05494486 0.14881948
## sample estimates:
## cor
## 0.1021095As p-value is less than 0.01, this correlation is statistically significant.
cor.test(Housingpriceindex$index_nsa,Housingpriceindex$Violent.Crimes)##
## Pearson's product-moment correlation
##
## data: Housingpriceindex$index_nsa and Housingpriceindex$Violent.Crimes
## t = -1.4656, df = 1706, p-value = 0.1429
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.08275273 0.01198973
## sample estimates:
## cor
## -0.03546118People don’t like to buy houses in vicinity of area full of crime, so the correlation is negative.
cor.test(Housingpriceindex$index_nsa,Housingpriceindex$Homicides)##
## Pearson's product-moment correlation
##
## data: Housingpriceindex$index_nsa and Housingpriceindex$Homicides
## t = -3.5359, df = 1706, p-value = 0.0004173
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.13219098 -0.03801809
## sample estimates:
## cor
## -0.08529503Correlation is negative as expected and also statistically significant with p-value = 0.0004173.Usually people avoid these kind of area to live because negative things affect minds to do negative.
cor.test(Housingpriceindex$index_nsa,Housingpriceindex$Rapes)##
## Pearson's product-moment correlation
##
## data: Housingpriceindex$index_nsa and Housingpriceindex$Rapes
## t = -4.885, df = 1706, p-value = 1.131e-06
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.16396959 -0.07041383
## sample estimates:
## cor
## -0.1174523Negative correlation with p-value = 1.131e-0.6. statistically significnt. Again people don’t like to buy house where rape cases are high, it’s not safe for there children.
cor.test(Housingpriceindex$index_nsa,Housingpriceindex$Assaults)##
## Pearson's product-moment correlation
##
## data: Housingpriceindex$index_nsa and Housingpriceindex$Assaults
## t = 1.5716, df = 1706, p-value = 0.1162
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.009426004 0.085298648
## sample estimates:
## cor
## 0.03802174Weak positive correlation with p-value = 0.1126, which is statistically insignificant.
cor.test(Housingpriceindex$index_nsa,Housingpriceindex$Robberies)##
## Pearson's product-moment correlation
##
## data: Housingpriceindex$index_nsa and Housingpriceindex$Robberies
## t = -3.9478, df = 1706, p-value = 8.208e-05
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.14193599 -0.04793135
## sample estimates:
## cor
## -0.09514578Weak negative correlation as expected with p-value 8.208e-05,which is statistically significant.
library(corrgram)
corrgram(Housingpriceindex[ ,c(1,2,4:9)],order = TRUE,upper.panel = panel.pie, lower.panel = panel.shade,text.panel = panel.txt, main= "corrgram plot of housing price index with each variable",diag.panel = panel.minmax)
Model1 = index_nsa ~ Year + Population + Homicides + Robberies + Rapes + Violent.Crimes + Assaults + City..State
fit <- lm(Model1, data = Housingpriceindex)
summary(fit)##
## Call:
## lm(formula = Model1, data = Housingpriceindex)
##
## Residuals:
## Min 1Q Median 3Q Max
## -84.289 -15.412 -2.201 10.405 182.836
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -9.360e+03 1.282e+02 -73.007 < 2e-16 ***
## Year 4.748e+00 6.417e-02 74.003 < 2e-16 ***
## Population 5.613e-06 1.576e-06 3.562 0.000378 ***
## Homicides 4.162e-03 1.218e-02 0.342 0.732642
## Robberies 7.298e-04 5.229e-04 1.396 0.163040
## Rapes -1.106e-02 3.995e-03 -2.769 0.005679 **
## Violent.Crimes -9.682e-05 3.736e-04 -0.259 0.795527
## Assaults 7.458e-05 7.445e-04 0.100 0.920211
## City..StateArlington, TX 1.725e+00 6.244e+00 0.276 0.782419
## City..StateAtlanta, GA 1.800e+00 6.129e+00 0.294 0.768990
## City..StateAurora, CO 3.150e+01 6.210e+00 5.073 4.37e-07 ***
## City..StateAustin, TX 2.932e+01 6.171e+00 4.751 2.20e-06 ***
## City..StateBaltimore, MD 9.210e+00 6.148e+00 1.498 0.134281
## City..StateBoston, MA 2.259e+01 6.240e+00 3.620 0.000304 ***
## City..StateBuffalo, NY -1.590e+01 6.187e+00 -2.570 0.010249 *
## City..StateCharlotte, NC -5.170e+00 6.234e+00 -0.829 0.407051
## City..StateChicago, IL 1.124e+00 6.117e+00 0.184 0.854237
## City..StateCincinnati, OH -6.436e+00 6.158e+00 -1.045 0.296118
## City..StateCleveland, OH -9.708e+00 6.109e+00 -1.589 0.112198
## City..StateColumbus, OH -5.089e+00 6.153e+00 -0.827 0.408337
## City..StateDallas, TX 5.267e+00 6.180e+00 0.852 0.394213
## City..StateDenver, CO 3.208e+01 6.190e+00 5.183 2.46e-07 ***
## City..StateDetroit, MI -4.220e+00 6.178e+00 -0.683 0.494706
## City..StateFresno, CA 5.140e+00 6.225e+00 0.826 0.409060
## City..StateHonolulu, HI -1.526e+01 6.262e+00 -2.436 0.014952 *
## City..StateHouston, TX 1.253e+01 6.199e+00 2.021 0.043476 *
## City..StateIndianapolis, IN -9.631e+00 6.241e+00 -1.543 0.122967
## City..StateJacksonville, FL 1.443e+01 6.267e+00 2.303 0.021392 *
## City..StateLouisville, KY 1.080e+00 6.227e+00 0.173 0.862357
## City..StateMemphis, TN -4.843e+00 6.217e+00 -0.779 0.436099
## City..StateMesa, AZ 2.342e+01 6.269e+00 3.736 0.000194 ***
## City..StateMiami, FL 3.096e+01 6.183e+00 5.007 6.10e-07 ***
## City..StateMilwaukee, WI 4.964e+00 6.331e+00 0.784 0.433169
## City..StateMinneapolis, MN 1.904e+01 6.200e+00 3.072 0.002164 **
## City..StateNashville, TN 7.028e+00 6.311e+00 1.114 0.265562
## City..StateNewark, NJ 1.271e+01 6.216e+00 2.044 0.041125 *
## City..StateOakland, CA 1.399e+01 6.223e+00 2.249 0.024664 *
## City..StateOmaha, NE 4.025e+00 6.276e+00 0.641 0.521376
## City..StateOrlando, FL 5.917e+00 6.277e+00 0.943 0.345995
## City..StatePhiladelphia, PA 6.232e+00 6.189e+00 1.007 0.314084
## City..StatePhoenix, AZ 2.406e+01 6.237e+00 3.857 0.000119 ***
## City..StatePittsburgh, PA -2.853e+00 6.195e+00 -0.460 0.645262
## City..StatePortland, OR 2.731e+01 6.295e+00 4.339 1.52e-05 ***
## City..StateRaleigh, NC -3.007e+00 6.249e+00 -0.481 0.630426
## City..StateSacramento, CA -1.063e+00 6.206e+00 -0.171 0.864078
## City..StateSeattle, WA 1.551e+01 6.160e+00 2.517 0.011913 *
## City..StateTampa, FL 1.640e+01 6.214e+00 2.639 0.008392 **
## City..StateTucson, AZ 1.090e+01 6.223e+00 1.752 0.080037 .
## City..StateTulsa, OK 4.952e+00 6.225e+00 0.795 0.426474
## City..StateWashington, DC 1.243e+01 6.152e+00 2.020 0.043540 *
## City..StateWichita, KS 1.576e+00 6.184e+00 0.255 0.798872
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 27.21 on 1657 degrees of freedom
## Multiple R-squared: 0.8088, Adjusted R-squared: 0.803
## F-statistic: 140.2 on 50 and 1657 DF, p-value: < 2.2e-16