Answer To: 1. Introduction: Summarize what topic you are analyzing. What is the dependent variable, y, you are...
Suraj answered on May 14 2021
Assignment
Topic: Multiple Regression Analysis
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Date:
Introduction: In today’s generation making a prediction is common and also very important aspect for us. Many large organisations are running their business based on the predictions also like building models for making prediction of the sales, for losses or profits. Thus, predictions are very important for today’s generation because everyone do their business based on those predictions even if someone have to start his day, he/she will always check for the forecast of the weather which is also a kind of prediction. Thus, we can say that prediction is one of the most important part of our daily life. Thus, our topic is also based on the prediction of the price of the house. That we will try to make a best model which will perfectly predicts the price of a house located in a particular area of a nation. The data set is extracted from an online website which provides reliable data sets to work on various projects. So, the data set is also extracted from there. The data set is a huge data set consist of 14 different parameters. The meaning of parameter that is different types of variables those are necessary and will play a very important role in the prediction of the house price. These 14 variables and their description is mentioned as follows:
1.) Price: This is our main variable also we can say the response or dependent variable. We will predict this variable by using a typical statistical technique. This is the price of the house in dollars ($).
2.) Bedroom: This variable describes the number of bedrooms a particular house has. This is considered as the independent variable.
3.) Bathrooms: This variable describes the number of bathrooms a particular house has. This is considered as the independent variable.
4.) Sqft_living: This variable describes the total square feet area of the living portion of the house.
5.) Sqft_lot: This variable describes the total square feet area of the of the house.
6.) Floors: This variable defines the total number of floors a particular house has.
7.) Water front: This variable defines that is there any water front like any water view point in the front of the house.
8.) Condition: This variable defines the rating about the condition of the house. The range of this variable is from 1 to 5. Where 1 means worst condition and 5 means perfect.
9.) Yr_built: This variable defines the year number in which a particular house is built
10.) Yr_renovated: This variable defines the year number in which a particular house is renovated.
11.) Street: This is the street number or in other words address of the house.
12.) City: This is the city name in which a particular house is situated.
13.) State zip: This variable defines the zip code of the state where the house is situated.
14.) Country: This variable defines the country name from where the data set is gathered.
Thus, overall, the data set consists of the 13 different independent variables and 1 dependent variable. Some of the independent variables do not have any impact on the model so they will not consider in the model while running regression analysis.
Methodology: To make the model we will use the multiple linear regression technique because this technique is most useful and reliable. Also, it is very easy to apply this technique. Hence, because of the reliable results and easy to use we will select multiple linear regression to do our analysis.
The multiple linear regression technique has some assumption. We have to check these assumptions before running the analysis. The assumptions are given as follows:
a. There is a linear relationship between the dependent and independent variable. This is the most important assumption and this assumption is check by potting scatter plot between dependent and independent variables.
b. The distribution of the residuals is normally distributed with mean 0 and with constant variance.
c. There is no multicollinearity in the model.
d. No autocorrelation present in the model.
We will do our whole analysis using the statistical software SPSS and the scatter plots are plotted using the MS-Excel.
Let’s check the linearity assumption by plotting different scatter plots. The plots are given as follows:
Explanation: Here, we can see that most of the scatter plots does not depicts any kind of relationship just because the independent variable is a kind of discreate variable also there is difference between the scale of the dependent and independent variable. Only sqft living variable shows some kind of positive relationship and also sqft lot variable shows a little bit relationship. Rest all the variables does not show any kind of relationship between dependent and independent variable.
Now, we will make the regression model and the residual normality assumption is also checked. Before that let’s discuss a bit about the multicollinearity.
The problem of multicollinearity arises when the independent variables are correlated with each other. Rather than relationship with dependent variable. They are correlated with each other. If the correlation between the independent variables is greater than 0.7 then those variables are considered as multicollinear. Also, a value is calculated named as variation inflation factor (VIF) is also calculated to check the multicollinearity. If the value of VIF greater than 4 then we can assume that there is a multicollinearity between the independent variables.
Since, most of the variables are excluded from the analysis because they are not important for the model as they are mostly categorical variables like the street name. Which is not so important for the analysis.
The normality plot of the dependent variable price is given as follows:
Explanation: Here, we can see from the normality plot, that normality assumption of residual is not followed. For normality the curve has to be straight but it is not straight from any point. Hence, we concluded that the residual assumption of normality is not fulfilled. This will make impact on the value of the R square also.
Multicollinearity: The multicollinearity diagnosis also conducted and they are given as follows:
Explanation: Here, we need to focus on the condition index column to do our analysis. If the value of condition index for a certain variable is greater than 15 then multicollinearity is suspected and the higher the value the more stronger the multicollinearity is considered.
From the above table, the condition index value for all the variables is less than 15 except the last variable where the value is 21.756. Thus, this variable is suspected under the multicollinearity.
Now, the multiple regression model is developed using these variables and the output table is given as follows:
There are two kinds of regression coefficients that is unstandardized and standardized coefficients.
From the Sig. column we can check the significance of the coefficients. In general t-test is used to check the significance of the regression coefficients but here in the above table, it is automatically conducted. Let us suppose the level of significance to be 0.05 that is default value. From that column, compare the p-value of the coefficients with the level of significance 0.05. If the p-value is less than 0.05 then we can consider that coefficient as significant for the model.
Thus, from the values only two variables have p-value greater than 0.05. Those variables are bathrooms and floors. Thus, these two variables are considered as not significant and rest the other variables are significant for the model.
The multiple regression model is given as follows using the unstandardized coefficient values as follows:
Price = -97179.31 – 62016.73*bedrooms + 15165.34*bathrooms + 283.33*sqft living
– 0.737*sqft lot + 13153.42*floors + 61476.35*condition
The goodness of fit of the model is check from the value of R-square which is also called coefficient of determination. When there are many variables in the model then R-square does not provide good results also this is the drawback of this also. Then in that situation adjusted R square value given us good result. Thus, the goodness of fit table is given as follows:
The adjusted R-square value of the model is 0.196 which indicates that 19.6% variation in the dependent variable price is explained by the independent variables. Since this value is very low and we can’t consider this as good model to make predictions.
Thus, to make it as good model we need to make the dependent variable normally distributed and also remove all the not significant variables.
Conclusion: The final conclusion for all the analysis is given as follows:
Since, our aim is to do the regression analysis and make a perfect model to predict the house price. The main reason of the failure is that not satisfying the assumptions. Because when ever the assumptions for a particular analysis are violates then it will directly make effect on the performance of the model. The same happened here. The assumption of linearity and assumption of multicollinearity for one variable is violated and also the normality condition of residual is not satisfied.
The final multiple regression model is given as follows:
Scatter plot
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