Quantitative Social Science Methods and Statistics, Essay Example

The necessary hypothesis is that the length of stay depends on the destination.

Linear regression

• Y=a+bx
• Where y=dependent variable
• A=y intercept: x=0
• B= slope
• X= independent variable
• Dependent variable=Los
• Independent variable= destiny
• Los= a + b destiny

When the value of los increases the value of destiny also increases and vice versa is true.

This bivariate analysis infers that there is a relationship between the two variables. The dependent variable that is the length of stay during a tourist visit to a place depends on the other factor of measurement, which in this case is the destiny (Chachi, 2021). The independent variable in this case is presumed to be stable and remains unaffected by other variables in the measure. The independent variable is the presumed cause. This hypothesis implies that the amount of time the Norwegian tourist take during their tourists visits to various places depend on the place or destiny of their visit (Chachi, 2021). Different places make the Norwegian who visit the places to spent different amounts of time. Other places could be more att4rcative and interesting and tourist can spent a considerable amount of time while other destination may not be attractive or interesting for a lengthy stay (Chachi, 2021).

The variable los is the dependent one, and book is the independent one. Specify the necessary hypotheses (based on a hunch and/or on prior knowledge) and illuminate these by means of a linear regression. Tell me about your results.

The necessary hypothesis for this case is the length of stay during a tourist visit depend on the booking.

Linear regression

• Y=a + bx
• Los= a + b (book)

When there is an increase in the value of book, the value of los also increases.

Where a represents the y-intercept at x=0, while b represents the linear regression’s slope or gradient. This implies that if the booking done is for a longer period, then the length of stay a Norwegian tourist will take will be longer. If the book is for a shorter period, the length of stay during a tourist visit to a given destiny will be shorter (Chachi, 2021).

The inescapable conclusion is that the number of journeys determines the duration of stay.

• y=a+bx
• los =a+b (num_trips)

Depending on how many visits you make, you may adjust the duration of your stay. When the number of trips increases, the length of stay also increases (Chachi, 2021).

This infers that if the number of trips is high, the length of stay during a tourist visit is will be longer. If the number of trips during a tourists visit is low, the duration of stay during a tourist visit will be short (Haralayya, 2021).

The necessary hypothesis is the length of stay (los) is dependent on the destiny, the booking, and the number of trips.

•  y=a+b₁x₁+b₂x₂+b₃x₃…b_nx_n
• Los=a+b₁destin+b₂book+b₃num_trips+…b_nx_n

The length of stay depends on the Destin, book and num_trip. An increase in length of stay results in increase in destiny, book, and number of trips (Haralayya, 2021).

This infers that the amount of time a tourist will take during a tourist visit will depend on the combination of the three independent factors. By means of multivariate analysis, it implies that the length of stay is subject to one or all the three factors (Haralayya, 2021).

The necessary hypothesis is trip spend depends on the destination, the length of stay, age, and gender.

• Y=a+b₁x₁+b₂x₂+b₃x₃+…b_nx_n
• Trip spent = a+ (Destin) x₁+ (los)x₂+ (gender)x₃

The number of trips to be spent depends on the destiny, the length of stay and gender.

This implies that trip spend during a tourists visit depend on one or all the three independent variables in question. There is collinearity between trip spent and the destination of the visit, the length of the visit, the age of the tourist, and the gender of the tourist.

References

Chachi, J., Taheri, S. M., & D’Urso, P. (2021). Fuzzy regression analysis based on M-estimates. Expert Systems with Applications, 115891.

Daniel, A. I., Shama, S., Ismail, S., Bourdon, C., Kiss, A., Mwangome, M., … & O’Connor, D. L. (2021). Maternal BMI is positively associated with human milk fat: a systematic review and meta-regression analysis. The American journal of clinical nutrition, 113(4), 1009-1022.

De Menezes, D. Q. F., Prata, D. M., Secchi, A. R., & Pinto, J. C. (2021). A review on robust M-estimators for regression analysis. Computers & Chemical Engineering, 107254.

Haralayya, D., & Aithal, P. S. (2021). Factors Determining the Efficiency in Indian Banking Sector: An Tobit Regression Analysis. International Journal of Scientific Development and Research (IJSDR), 6(6), 1-6.

Ho, T. H., Nguyen, D. T., Ngo, T., & Le, T. D. (2021). Efficiency in Vietnamese banking: A meta-regression analysis approach. International Journal of Financial Studies, 9(3), 41.

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