Analisis Normalitas, Heterokedastisitas, Multikolinearitas, dan Autokorelasi pada Model Regresi Linear Berganda: Studi Kasus Laporan Keuangan PT Solusi Sinergi Digital Tbk (WIFI)
Article Sidebar
Main Article Content
Abstract
This study aims to analyze the feasibility of a multiple linear regression model through classical assumption testing on the financial data of PT Solusi Sinergi Digital Tbk (WIFI). Classical assumption testing is an essential stage in regression analysis to ensure that the estimator is BLUE (Best Linear Unbiased Estimator). The dependent variable used is profit and loss, while the independent variables consist of sales revenue, cost of goods sold, administrative expenses, financial expenses, and tax expenses. The research method employs a quantitative approach with multiple linear regression analysis using R software. Classical assumption testing includes residual normality test, multicollinearity test using Variance Inflation Factor (VIF), heteroscedasticity test using Breusch-Pagan, and autocorrelation test using Durbin-Watson. The results show that the regression model, after logarithmic transformation, meets the normality assumption, does not experience multicollinearity, is free from heteroscedasticity, and does not contain autocorrelation. Thus, the regression model is feasible to explain the relationship between financial variables and the company’s profit and loss. This study is expected to serve as a reference for academics and practitioners in analyzing corporate financial performance using econometric methods.
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
Andriani, S. (2017). Uji Park Dan Uji Breusch Pagan Godfrey Dalam Pendeteksian Heteroskedastisitas Pada Analisis Regresi. Al-Jabar : Jurnal Pendidikan Matematika, 8(1), 63–72. https://doi.org/10.24042/ajpm.v8i1.1014
Avram, C., & Mărușteri, M. (2022). Normality assessment, few paradigms and use cases. Revista Romana de Medicina de Laborator, 30(3), 251–260. https://doi.org/10.2478/rrlm-2022-0030
Cardoso, F. C., Berri, R. A., Lucca, G., Borges, E. N., & Mattos, V. L. D. de. (2023). Normality tests: a study of residuals obtained on time series tendency modeling. Exacta, 23(1), 134. https://doi.org/10.5585/2023.22928
Chaudhuri, S., & Renault, E. (2020). Score tests in GMM: Why use implied probabilities? Journal of Econometrics, 219(2), 260–280. https://doi.org/10.1016/j.jeconom.2020.03.004
Gosselin, R.-D. (2024). Testing for normality: a user’s (cautionary) guide. Laboratory Animals, 58(5), 433–437. https://doi.org/10.1177/00236772241276808
Hair, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., Danks, N. P., & Ray, S. (2021). Partial Least Squares Structural Equation Modeling (PLS-SEM) Using R. Springer International Publishing. https://doi.org/10.1007/978-3-030-80519-7
Hou, G., & Feng, C. (2024). Innovation-driven policy and firm investment. Finance Research Letters, 61, 105001. https://doi.org/10.1016/j.frl.2024.105001
Lubis, R. M. F., Situmorang, Z., & Rosnelly, R. (2021). Autoregressive Integrated Moving Average (ARIMA-Box Jenkins) Pada Peramalan Komoditas Cabai Merah di Indonesia. JURNAL MEDIA INFORMATIKA BUDIDARMA, 5(2), 485. https://doi.org/10.30865/mib.v5i2.2927
Maulana, C., & Hajarisman, N. (2023). Penerapan Transformasi Box Cox untuk Mengatasi Masalah Ketidakstasioneran dan Pola Periodik dalam Data Deret Waktu pada Ekspor Bidang Pertanian di Indonesia. Bandung Conference Series: Statistics, 3(2), 763–772. https://doi.org/10.29313/bcss.v3i2.9371
Mauludiyanto, A., Hendrantoro, G., P, M. H., & Suhartono, S. (2009). PEMODELAN ARIMA DAN DETEKSI OUTLIER DATA CURAH HUJAN SEBAGAI EVALUASI SISTEM RADIO GELOMBANG MILIMETER. JUTI: Jurnal Ilmiah Teknologi Informasi, 107–112. https://doi.org/10.12962/j24068535.v7i3.a76
Saeedbakhsh, S., Mohammadi, M., Younesi, S., & Sattari, M. (2025). Using Internet of Things for Child Care: A Systematic Review. International Journal of Preventive Medicine, 16. https://doi.org/10.4103/ijpvm.ijpvm_191_23
Setya Budi, A. D. A., Septiana, L., & Panji Mahendra, B. E. (2024a). Memahami Asumsi Klasik dalam Analisis Statistik: Sebuah Kajian Mendalam tentang Multikolinearitas, Heterokedastisitas, dan Autokorelasi dalam Penelitian. Jurnal Multidisiplin West Science, 3(01), 01–11. https://doi.org/10.58812/jmws.v3i01.878
Setya Budi, A. D. A., Septiana, L., & Panji Mahendra, B. E. (2024b). Memahami Asumsi Klasik dalam Analisis Statistik: Sebuah Kajian Mendalam tentang Multikolinearitas, Heterokedastisitas, dan Autokorelasi dalam Penelitian. Jurnal Multidisiplin West Science, 3(01), 01–11. https://doi.org/10.58812/jmws.v3i01.878
Sirait, J. V., Hamidah, H., & Siregar, M. E. S. (2024). Pengaruh Capital Structure, Liquidity, dan Tangibility terhadap Profitability. Jurnal Disrupsi Bisnis, 7(1), 171–184. https://doi.org/10.32493/drb.v7i1.37694
Slimani, B., Graef, J. R., & Ouahab, A. (2024a). Multivalued Contraction Fixed-Point Theorem in b-Metric Spaces. Mathematics, 12(4), 567. https://doi.org/10.3390/math12040567
Slimani, B., Graef, J. R., & Ouahab, A. (2024b). Multivalued Contraction Fixed-Point Theorem in b-Metric Spaces. Mathematics, 12(4), 567. https://doi.org/10.3390/math12040567
Uhm, T., & Yi, S. (2023). A comparison of normality testing methods by empirical power and distribution of P -values. Communications in Statistics - Simulation and Computation, 52(9), 4445–4458. https://doi.org/10.1080/03610918.2021.1963450
Utika, N., . M., & Yozza, H. (2013). PERMASALAHAN AUTOKORELASI PADA ANALISIS REGRESI LINIER SEDERHANA. Jurnal Matematika UNAND, 2(2), 26–34. https://doi.org/10.25077/jmu.2.2.26-34.2013