MechaCar_Statistical_Analysis

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  • 2022-05-17 03:29
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MechaCar_Statistical_Analysis 线性回归预测MPG 车辆重量小于0.05不太可能产生随机变化。 车辆重量非常小,应将其视为零坡度。 是的,r平方值表明它是一个很好的预测模型。 悬架线圈摘要统计 是的,差异小于最大100磅,因此符合设计规范。 悬架线圈的T检验 这测试了等于1500的零均值和不等于1500的零均值。95%CI显示它介于1497.507和1500.053之间。 研究设计:MechaCar vs竞赛 我将执行另一个多元线性回归来比较燃油效率,消费者每月平均行驶里程和安全等级。 所有这些数据都必须是可量化的,因此我将使用每加仑行驶的英里数,每月行驶的英里数和十分之十的等级。 无效的是,大多数人口希望在城市中达到30mpg +,每月要行驶1,000+英里,并希望获得6+以上的安全等级。 替代方案是在城市中少于30mpg,每月开车少于1000英里,并且
MechaCar_Statistical_Analysis-main.zip
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  • Total summary.png
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  • Del1.png
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  • Lot 3 test.png
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  • Lot 2 test.png
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  • One sample t test.png
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  • .DS_Store
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  • Lot 1 test.png
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  • README.md
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  • Suspension_Coil.csv
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  • MechaCar_mpg.csv
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  • .RData
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  • MechaCarChallenge.R
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  • .Rhistory
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  • .DS_Store
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内容介绍
# MechaCar_Statistical_Analysis ## Linear Regression to Predict MPG ![Del1](https://github.com/rindneremily/MechaCar_Statistical_Analysis/blob/main/images/Del1.png) Vehicle weight is unlikely to give a random variance as it is below 0.05. The vehicle weight is very small and should be considered to have a zero slope. Yes, the r squared value shows that it is a good model of prediction. ## Summary Statistics on Suspension Coils ![Total summary](https://github.com/rindneremily/MechaCar_Statistical_Analysis/blob/main/images/Total%20summary.png) ![Lot 1 test](https://github.com/rindneremily/MechaCar_Statistical_Analysis/blob/main/images/Lot%201%20test.png) ![Lot 2 test](https://github.com/rindneremily/MechaCar_Statistical_Analysis/blob/main/images/Lot%202%20test.png) ![Lot 3 test](https://github.com/rindneremily/MechaCar_Statistical_Analysis/blob/main/images/Lot%203%20test.png) Yes, the variance is less than the maximum of 100 pounds so it meets the design specifications. ## T-Tests on Suspension Coils ![One sample t test](https://github.com/rindneremily/MechaCar_Statistical_Analysis/blob/main/images/One%20sample%20t%20test.png) This tested the null mean equaling 1500 and the altnerative not equaling 1500. The 95% CI shows that it is between 1497.507 and 1500.053. ## Study Design: MechaCar vs Competition I would perform another multiple linear regression to compare fuel efficiency, average miles driven per month of the consumer, and safety rating. All of this data needs to be quantifiable so I would use miles per gallon, miles driven per month, and a rating out of ten. The null would be that the majority of the population want 30mpg+ in the city, drive 1,000+ miles per month, and want a safety rating 6+. The alternative would be less than 30mpg in the city, drive less than 1000 miles per month, and want a safety rating below 6.
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