TY - GEN
T1 - Statistical Analysis of Car Data Using Analysis of Covariance (ANCOVA)
AU - Syam, Thaer
AU - Syam, Mahmoud M.
AU - Khan, Adnan
AU - Syam, Mahmoud I.
AU - Syam, Muhammad I.
N1 - Funding Information:
This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.
Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2023
Y1 - 2023
N2 - Engineering statistics involves data concerning manufacturing processes such as: component dimensions, tolerances, type of material, and fabrication process control. In this work, some statistical analysis was done on a car data which was collected in 1983. This data set contains a sample of 66 cars from three different countries producers (USA, Japan, and Europe) in terms of their price and 13 different features. The objective was to determine how these quantitative variables affect the car price using the Analysis of Covariance (ANCOVA). Some analysis was done to find the best Multiple Linear Regression (MLR) model that identifies the most significant factors that influence the car price. The addition of removal of new factors was based on trial and error and based on the value of the linear correlation with the car price (Pearson coefficient of correlation r). 9 out of 13 factors were chosen based on MLR and the model selection/reduction analysis. The conclusion from ANCOVA analysis found that the European cars are the most expensive cars when compared to USA and Japan. Japan cars are the second expensive and USA cars are the cheapest.
AB - Engineering statistics involves data concerning manufacturing processes such as: component dimensions, tolerances, type of material, and fabrication process control. In this work, some statistical analysis was done on a car data which was collected in 1983. This data set contains a sample of 66 cars from three different countries producers (USA, Japan, and Europe) in terms of their price and 13 different features. The objective was to determine how these quantitative variables affect the car price using the Analysis of Covariance (ANCOVA). Some analysis was done to find the best Multiple Linear Regression (MLR) model that identifies the most significant factors that influence the car price. The addition of removal of new factors was based on trial and error and based on the value of the linear correlation with the car price (Pearson coefficient of correlation r). 9 out of 13 factors were chosen based on MLR and the model selection/reduction analysis. The conclusion from ANCOVA analysis found that the European cars are the most expensive cars when compared to USA and Japan. Japan cars are the second expensive and USA cars are the cheapest.
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U2 - 10.1007/978-3-031-21700-5_1
DO - 10.1007/978-3-031-21700-5_1
M3 - Conference contribution
AN - SCOPUS:85151049434
SN - 9783031216992
T3 - Springer Proceedings in Mathematics and Statistics
SP - 1
EP - 11
BT - Mathematical Methods for Engineering Applications - ICMASE 2022
A2 - Yilmaz, Fatih
A2 - Queiruga-Dios, Araceli
A2 - Martín Vaquero, Jesús
A2 - Mierluş-Mazilu, Ion
A2 - Rasteiro, Deolinda
A2 - Gayoso Martínez, Víctor
PB - Springer
T2 - 3rd International Conference on Mathematics and its Applications in Science and Engineering, ICMASE 2022
Y2 - 4 July 2022 through 7 July 2022
ER -