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  1. Population Analysis


    First, read the required sections in Chapters 12 and 13 of your text. Next, considering the survey you created in the previous module for your new product concept (and its target audience), briefly outline your sampling plan by addressing the following in your initial post:

    What is the target population for your sampling plan?

    What is the sampling frame for your sample plan? That is, what are the elements from which your sample is being drawn?

    What are the sampling units (primary, secondary, tertiary) subject to selection in your sample plan?


Subject Geography Pages 4 Style APA


Ford Motors’ Sampling Plan

Sampling is an effective tool of gathering views from a wide range of individuals who are chosen from a particular group with the bid of finding out more regarding the whole population in general (You et al., 2019). Fenton et al. (2018) reason that it is often extremely time-consuming and costly for researchers to collect information from a whole population. However, a careful sampling of the targeted demographic offers an accurate depiction of the target marketplace. As such, instead of gathering data across the world regarding its new Ford water-powered self-driving car product, this paper provides a sampling plan for Ford Motors, building a correct picture of the company’s target marketplace with regard to the new product using common patterns and trends.

Target Population

The target population for this survey is Americans aged 18 years and above from the entire America. Males and females, regardless of their marital status, will be enrolled in this survey. A total of 112,919,434 Americans, therefore, will form this survey’s target population. The selection of the U.S. population is informed by the fact that Ford Motors is among the largest carmaker in the United States (U.S.), implying that the company’s automobiles are in high demand in the U.S. Being among major automakers in the U.S. insinuates that a good percentage of the American population is informed regarding Ford Motors products, and thus would be interested in participating in a survey linked to a company that has a high countrywide recognition (Lee et al., 2016). Similarly, automakers are fast embracing the autonomous driving knowhow, implying that there is an increasing demand for self-driving cars, a shift that is associated with the need to protect the environment through the use of ecofriendly fuels (Aslam & Al-Marshadi, 2018). With the increase in the number of self-driving cars in the U.S., Americans will have the knowledge about how they operate, thus will be able to compare and contrast Ford Motors’ new Ford water-powered self-driving car and other cars by other companies (You et al., 2019).

Sampling Frame

To ensure that the sample used during this study is reliable, a large sample size will be employed. The sampling frame will include PIA – CARS Database System Information, Vehicle Configuration database (VCdb), Cars and Automakers Database, Teoalida’s Car Database, Data Axle USA, Bureau of Transportation Statistics, the USA Auto – Comprehensive Auto Owner Database, BB Direct Automobile Mailing List, and National Highway Traffic Safety Administration (NHTSA).

Sampling Units

This survey’s sampling units will include those who have possessed vehicles, those who have worked in the automobile industry, and those who have used Ford Motors vehicles. With a targeted population of 112,919,434, the sampling size needed would be 385 at 95% level and 5% margin of error. Thus for this study, a total 6,000 email invites will be sent to the potential participants with the hope that 600 of the survey would be returned, assuming a response rate of 10%.




Aslam, M., & Al-Marshadi, A. (2018). Design of Sampling Plan Using Regression Estimator under Indeterminacy. Symmetry, 10, 754.

Fenton, G., Naghibi, F., & Hicks, M. (2018). Effect of sampling plan and trend removal on residual uncertainty. Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards, 12, 253 – 264.

Lee, A. H. I., Wu, C. W., & Chen, Y. W. A (2016). Modified variables repetitive group sampling plan with the consideration of preceding lots information. Ann Oper Res, 238355–373. https://doi.org/10.1007/s10479-015-2064-5

You, C., Lu, J., Filev, D., & Tsiotras, P. (2019). Advanced planning for autonomous vehicles using reinforcement learning and deep inverse reinforcement learning. Robotics Auton. Syst., 114, 1-18.



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