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Sample Size and Variability
        A smaller sample size or a higher variability will result in a wider confidence interval with a larger margin of error. If
        you want a higher level of confidence, that interval will not be as tight. A tight interval at 95% or higher confidence
        is ideal.

        Example: Variation around an estimate
        Suppose, you survey 100 British people and 100 Americans with regard to their television-watching habits, and find
        that both the groups devote an average of 35 hours of television every week.
        Now, assume your research helped you discover that the British people have a wide variation in the number of hours
        spent watching television, while all the Americans spent similar hours.
        Even though both groups have the same point estimate (average number of hours watched), the British estimate will
        have a wider confidence interval as compared to the American estimate since there is more diversity in data.

                                        Average hours of TV watched per week,
                                               G.B. (orange) vs USA (blue)
                               8
                              Number of observations  6





                               4


                               2


                               8
                                   0               20              40              60
                                                        Hours watched

        Graph showing two sample populations with the same mean but different levels of variation around the mean.




             Indian viewers, on an average, watch television for 3 hours 44 minutes per day, as per the viewership monitoring agency
             BARC India. BARC’s latest Broadcast India 2018 survey states that in urban areas, average time spent (ATS) per viewer is
             about 4 hour 06 minutes, whereas in rural India, it is about 3 hours and 27 minutes.



        4.6 autOMatiC Data COLLeCtiOn via in-BuiLt SenSOrS

        A growing number of intelligent information-aware devices or smart devices, like sensors, actuators, smartphones,
        smart wristbands, tablets, devices based on readers’ Radio Frequency Identification (RFID) and Machine-to-Machine
        (M2M), have led to the exponential growth of generated-data volume. A smart device is any type of instrument or
        machine that has its own computing proficiency.
        Data is collected through smart devices. These devices are managed through an Internet based technology known
        as Internet of Things (In short IoT.). Well-known examples of IoT devices include smart speakers like Amazon Alexa
        or Google Home, smartwatches like the Apple Watch, Internet-connected baby monitors, video doorbells, toys, etc.
        Sensor is a device that responds to a physical stimulus (such as heat, light, sound, pressure, magnetism, or a particular
        motion) and transmits a resulting impulse for automatically controlled actuators.

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