CompTIA DA0-001 Exam With Confidence Using Practice Dumps

 Normalizing the variables means scaling them to a common range, such as 0 to 1 or -1 to 1, so that they have the same weight in the score calculation. Recoding the variables means changing their values or categories, which would alter their meaning and distribution. Calculating the percentiles of the variables means ranking them relative to each other, which would not account for their actual magnitudes. Calculating the standard deviations of the variables means measuring their variability, which would not make them comparable. References: CompTIA Data+ Certification Exam Objectives, page 10

Comprehensive and Detailed In-Depth Explanation:

A confidence interval provides a range within which we expect a population parameter to lie, based on sample data. Increasing the confidence level means we want to be more certain that the interval contains the parameter, which affects the margin of error.

Option A:The margin of error does not change.

Rationale:This is incorrect. Increasing the confidence level requires a wider interval to maintain the higher degree of certainty, which increases the margin of error.

Option B:The interval remains the same.

Rationale:This is incorrect. To achieve a higher confidence level, the interval must widen, altering its range.

Option C:The interval becomes narrower.

Rationale:This is incorrect. A higher confidence level necessitates a wider interval to ensure the parameter is captured within the range.

Option D:The margin of error increases.

Rationale:Correct. Increasing the confidence level from 90% to 95% means we are more confident that the interval contains the true parameter. To achieve this increased confidence, the margin of error must increase, resulting in a wider interval.

Surveys sent to 100 randomly selected homes that are reflective of the population. This is because a random sample is a type of sample that is selected by using a random method, such as a lottery or a computer-generated number, which ensures that every element in the population has an equal chance of being selected. A random sample can result in a representative sample, which means that the sample reflects the characteristics and diversity of the population. By sending surveys to 100 randomly selected homes that are reflective of the population, the analyst can ensure that the sample is representative of the county’s households and their income levels. The other sampling methodsare not likely to result in a representative sample. Here is why:

A stratified phone survey of 100 people that is conducted between 2:00 p.m. and 3:00 p.m. would result in a biased sample, which means that the sample favors or excludes certain groups or elements in the population. By conducting the survey only between 2:00 p.m. and 3:00 p.m., the analyst would miss out on people who are not available or reachable at that time, such as those who are working or sleeping. This could affect the representativeness and generalizability of the sample.

A systematic survey that is sent to 100 single-family homes in the county would result in an unrepresentative sample, which means that the sample does not reflect the characteristics and diversity of the population. By sending surveys only to single-family homes, the analyst would ignore other types of households, such as apartments, condos, or mobile homes. This could affect the accuracy and reliability of the sample.

Surveys sent to ten randomly selected homes within 5mi (8km) of the county’s office would result in a small sample, which means that the sample size is too low to capture the variability and diversity of the population. By sending surveys only to ten homes within a limited area, the analyst would miss out on many households that are located in different parts of the county. This could affect the precision and confidence of the sample.