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1. When you have a well-thought-out dataset Option B. Although it might not always be accurate, you can use good business judgment to identify a causal factor.
2. The first thing you must do before evaluating the new software is option C. Define how "success" is to be measured.
3. Cherry-picking mean in the context of data analytics is option D. Selection bias.
4. The most likely way for a sample selection to lead to inaccurate results is option D. Introducing bias into the sample.
5. Keep in mind about using averages is option A. Variation is often hidden by averages, regardless of how good the dataset is.
6. When there is a data fail is option D. You can use advanced statistics to massage the data.
7. When analyzing the data is option D. Issue a regency issue.
8. The key to how you frame your questions is option A. Keep your questions focused and actionable.
9. Draw viable conclusions from the dataset by is option B. You can draw viable conclusions from an existing dataset, provided you frame actionable and detailed questions.
10. The key areas at the intersection of finding answers and business questions option B. Past events, current predictions, and business acumen.
11. "standard deviation" tell you is option B. The distance observations are from the mean.
12. Option D. Use the formula =MODE.MULT, highlight the cells, then click Shift + Ctrl + Enter at the same time.
13. Option D. Interviews with store managers are an example of qualitative data.
14. Analytics type is the building block for all other types of business analytics is option B. Descriptive analytics.
1. When dealing with a well-thought-out dataset, suggests that despite potential inaccuracies, one can rely on good business judgment to identify a causal factor.
2. Before evaluating new software, emphasizes the importance of defining how "success" will be measured. Establishing clear metrics and criteria helps determine the effectiveness and value of the software
3. Selection bias Cherry-picking in the context of data analytics refers to the act of selectively choosing or emphasizing specific data points, samples.
4.The most likely way for sample selection to result in inaccurate results which involves introducing bias into the sample. Bias in sample selection can skew the representation of the population, leading to results that do not accurately reflect the true characteristics
5. Emphasizes that regardless of dataset quality, averages often conceal variation. By focusing solely on the average, important fluctuations and patterns within the data can go unnoticed, leading to an incomplete understanding of the underlying trends and characteristics.
6. Suggests that when there is a data fail, one can use advanced statistics to manipulate or "massage" the data. However, it is important to note that manipulating data to fit desired outcomes can compromise the integrity .
7. Refers to issuing a "regency issue" when analyzing data. However, without further context or clarification, it is not clear what a regency issue entails in the context of data analysis.
8.Emphasizes that the key to framing effective questions lies in keeping them focused and actionable. By ensuring that questions are clear, specific, and capable of generating practical insights, one can enhance the quality and usefulness of the resulting information.
9. Suggests that viable conclusions can be drawn from an existing dataset by framing actionable and detailed questions. By formulating specific inquiries that align with the dataset's context, one can extract meaningful insights.
10. Highlights three key areas at the intersection of finding answers and business questions: past events, current predictions, and business acumen. This involves leveraging historical data, making informed predictions based on current information, and applying business knowledge and expertise to drive decision-making.
11. States that "standard deviation" tells you the distance observations are from the mean. Standard deviation is a statistical measure that quantifies the dispersion or spread of data points in relation to the mean.12. Use the formula =MODE.MULT, highlight the cells, then click Shift + Ctrl + Enter at the same time. To search through values in column D and rows 1 to 27 to determine if there is more than one mean in Excel on a PC, you can use the formula =MODE.MULT.
13.Interviews with store managers. Interviews with store managers are an example of qualitative data. Qualitative data is non-numerical and typically consists of descriptions, opinions, or subjective information
14. Descriptive analytics. Descriptive analytics is the building boll other types of business analytics. It involves summarizing and interpreting historical data to gain insights and understand patterns, trends, and relationships within the data.
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