Data Analytics Midterm Quest

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Statistics and data basics quiz

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Aimee Argueta

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Data Analytics Midterm Quest


Froggy Jumps Data Analytics Midterm QuestVersión en línea Statistics and data basics quiz por Aimee Argueta 1 1. Statistics is best defined as: a Creating charts only b Organizing and interpreting data for decision-making c Collecting random numbers 2 2. One complete row in a dataset is called a(n): a Observation b Variable c Column 3 3. Which is quantitative data? a Monthly sales revenue ($12,500) b Customer satisfaction level c Region (North, South) 4 4. A frequency distribution is used to: a Summarize how often values occur b Remove outliers c Predict future sales 5 5. Which graph is appropriate for grouped quantitative data? a Histogram b Bar chart c Pie chart 6 6. In a bar chart, bars: a Must touch b Show correlation c Do not touch 7 7. Data: 10, 15, 20, 25, 30. Find the mean. a 17 b 18 c 20 8 8. Find the median of: 5, 9, 12, 18, 25, 30. a 14 b 13 c 15 9 9. When data contains outliers, the most reliable measure of center is: a Mean b Median c Range 10 10. The weighted mean is appropriate when: a All values are equally important b Data is categorical c Some values count more than others 11 Find the range of: 8, 12, 20, 25, 40 a 32 b 20 c 40 12 Quartiles divide ordered data into __________ equal parts. a Three b Two c Four 13 If Q1 = 20 and Q3 = 35, compute the IQR. a 10 b -15 c 15 14 The IQR measures: a Total spread b Spread of the middle 50% c Distance from mean 15 An observation is considered an outlier if it is: a Outside mean ± SD b Outside Q1 − 1.5×IQR or Q3 + 1.5×IQR c Larger than the maximum 16 Variance is harder to interpret than standard deviation because: a It ignores extreme values b It is always negative c Its units are squared 17 Mean = 80, SD = 10. Using the Empirical Rule, approximately what interval contains about 95% of observations? a b 90% ≈ Mean ± 4SD = 80 ± 20 → (90, 120). c 18 Mean = 50, SD = 5, Value = 60. Compute the z-score. a 2 b 6 c 8 19 A z-score of −2 indicates the value is: a 2 units below the mean b 2 standard deviations below the mean c An outlier automatically 20 The coefficient of variation (CV) is most useful when: a Comparing datasets with different scales b The mean equals zero c Data is categorical 21 A scatter plot is used to analyze: a One categorical variable b Two quantitative variables c One quantitative variable only 22 Covariance tells us: a Exact strength of relationship b Direction of relationship c Causation 23 The correlation coefficient ranges between: a 0 and 1 b −1 and 1 c 0 and 100 24 What is the purpose of df.head() in pandas? a Displays first few rows b Shows last rows c Calculates mean 25 What does this command compute? df.groupby('Region')['Sales'].mean() a Overall average sales b Total sales c Average sales for each region

Data Analytics Midterm Quest

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Data Analytics Midterm QuestVersión en línea

por Aimee Argueta

1. Statistics is best defined as:

2. One complete row in a dataset is called a(n):

3. Which is quantitative data?

4. A frequency distribution is used to:

5. Which graph is appropriate for grouped quantitative data?

6. In a bar chart, bars:

7. Data: 10, 15, 20, 25, 30. Find the mean.

8. Find the median of: 5, 9, 12, 18, 25, 30.

9. When data contains outliers, the most reliable measure of center is:

10. The weighted mean is appropriate when:

Find the range of: 8, 12, 20, 25, 40

Quartiles divide ordered data into __________ equal parts.

If Q1 = 20 and Q3 = 35, compute the IQR.

The IQR measures:

An observation is considered an outlier if it is:

Variance is harder to interpret than standard deviation because:

Mean = 80, SD = 10. Using the Empirical Rule, approximately what interval contains about 95% of observations?

Mean = 50, SD = 5, Value = 60. Compute the z-score.

A z-score of −2 indicates the value is:

The coefficient of variation (CV) is most useful when:

A scatter plot is used to analyze:

Covariance tells us:

The correlation coefficient ranges between:

What is the purpose of df.head() in pandas?

What does this command compute? df.groupby('Region')['Sales'].mean()