Machine Learning and Artificial Intelligence

Columbia University

 

Multiple Regression Analysis for AIRBNB

1.     The Results of the Data Analysis

This paper presents a multiple regression analysis of the dataset involving six predictor variables; quests included maximum nights, review scores, reviews per month, availability 356, as well as extra people. Multiple regression analysis is one of the primary forms of regression analysis in statistics. Typically, multiple regression analysis helps to explain the correlation between a continuous dependent variable and two or more predictor variables. The continuous dependent variable in this analysis is the overall rating with six independent variables, as shown in the table below.

Table 1 Variables

Variables Entered/Removeda
Model	Variables Entered	Variables Removed	Method
1	guests included maximum nights, review scores value, reviews per month, availability_365, extra peopleb	.	Enter
a. Dependent Variable: Value_Overall_Rating
b. All requested variables entered.

 

The model summary indicates the multiple correlation coefficient of 0.710 which indicate a good prediction. Moreover, the results indicate that the independent variables explain 50.5% of the variability of the dependent variables.

 

Table 2 Model Summary

Model Summary
Model	R	R Square	Adjusted R Square	Std. Error of the Estimate
1	.710a	.505	.504	4.853
a. Predictors: (Constant), guests included, maximum nights, review scores value, reviews per month, availability_365, extra people

 

Table 3 ANOVA tests

ANOVAa
Model		Sum of Squares	df	Mean Square	F	Sig.
1	Regression	136689.182	6	22781.530	967.360	.000b
	Residual	134236.164	5700	23.550
	Total	270925.346	5706
a. Dependent Variable: Value Overall Rating
b. Predictors: (Constant), guests included, maximum nights, review scores value, reviews per month, availability_365, extra people

 

F(6,5700) = 967.360, P> 0.0005,  indicating that the regression model is  good fit of the data.

Table 4 Coefficients

Coefficientsa
Model		Unstandardized Coefficients		Standardized Coefficients	T	Sig.	95.0% Confidence Interval for B
		B	Std. Error	Beta			Lower Bound	Upper Bound
1	(Constant)	39.632	.770		51.465	.000	38.122	41.142
	reviews per month	-.028	.032	-.008	-.879	.380	-.090	.034
	Review scores value	5.926	.080	.703	74.063	.000	5.769	6.083
	availability_365	-.001	.000	-.028	-2.965	.003	-.002	.000
	Maximum nights	-3.575E-8	.000	-.007	-.736	.462	.000	.000
	Extra people	.006	.002	.026	2.594	.010	.001	.010
	Guests included	.225	.047	.048	4.805	.000	.133	.317
a. Dependent Variable: Value_Overall_Rating

The standard coefficients indicate -0.008, 0.703, – 0.028, -0.007, -0.026, and – 0.48 for the reviews per month, review scores value, maximum nights, extra people, and guests included respectively. The standardized coefficients indicate that the dependent variables are statistically significant. P > 0.05.

 

2.     Unanswered Questions after this Analysis

There are numerous answered questions regarding this analysis, and some of them include whether the descriptive statistics of the major independent variables and the dependent variable to determine their distribution. It is essential to determine the mean, standard deviation, as well as the variance of the data to determine whether there are any deviations from the mean.   The other answered question includes whether there is an association between the dependent variable and the independent variables.  It is essential to determine whether there is any relationship between the six predictor variable and the dependent variable before proceeding with further analysis.

3.     The Business Implications of These Insights – What Should the Company Do About These Results?

Since the dependent variables are statistically significant, business people can take into consideration the reviews per month, review scores value, maximum nights, extra people and guests included respectively. The business should devise strategies like marketing to increase the number of guests in their hotels to increase the overall rating as well as profitability. The six variables above indicate that they are significant factors that affect the overall rating of the hotel in the tourism industry

 

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