1494 Words6 Pages

1.0 Executive Summary Both descriptive and inferential analysis is carried out to study the factors determining the house price in Perlis. Inferential analysis focuses on analysis of correlation and regression in which independent variables are taken into account to predict the house price. Whereas descriptive analysis is run to see the characteristic of the sample used.
2.0 Introduction This research sets out to helping Wood Development to identify various factors determining the setting of housing prices in the southern Perlis. The objective of the study is to investigate whether variables like lot size, bedroom numbers and floor numbers can be the possible factors that determine the selling price of property. 3.0 Literature Review Many*…show more content…*

A basic descriptive analysis is used to summarize and define the characteristics of the sample used. Output like descriptive statistic and frequency distribution is shown in Figure 9 and Figure 10 respectively. Whereas inferential analysis includes correlation analysis, regression analysis, and ANOVA is executed. Correlation analysis is used to examine the strength of relationship between the Y-dependent variable (price) and the X-independent variables (lot, bedrooms, and floors). Pearson's r correlation categorization is used as the rules of thumb in determining the correlation strength: Also, a multiple regression analysis is run by using one dependent variable and three independent variables. A multiple regression equation is computed to study how much variation in Y-dependent variable can be influenced by the X-independent variable. ANOVA analysis is necessary to test the statistical significance of the overall regression. The benchmark for the goodness of fit depends on the p-value, where p-value ≤ 0.05 indicating that the overall regression is

A basic descriptive analysis is used to summarize and define the characteristics of the sample used. Output like descriptive statistic and frequency distribution is shown in Figure 9 and Figure 10 respectively. Whereas inferential analysis includes correlation analysis, regression analysis, and ANOVA is executed. Correlation analysis is used to examine the strength of relationship between the Y-dependent variable (price) and the X-independent variables (lot, bedrooms, and floors). Pearson's r correlation categorization is used as the rules of thumb in determining the correlation strength: Also, a multiple regression analysis is run by using one dependent variable and three independent variables. A multiple regression equation is computed to study how much variation in Y-dependent variable can be influenced by the X-independent variable. ANOVA analysis is necessary to test the statistical significance of the overall regression. The benchmark for the goodness of fit depends on the p-value, where p-value ≤ 0.05 indicating that the overall regression is

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