Suppose a population has a mean of and a standard deviation of . What is the z-score for a value of ? Round your answer to two decimal places as needed.
Table of contents
- 1. Intro to Stats and Collecting Data1h 14m
- 2. Describing Data with Tables and Graphs1h 55m
- 3. Describing Data Numerically2h 5m
- 4. Probability2h 16m
- 5. Binomial Distribution & Discrete Random Variables3h 6m
- 6. Normal Distribution and Continuous Random Variables2h 11m
- 7. Sampling Distributions & Confidence Intervals: Mean3h 23m
- Sampling Distribution of the Sample Mean and Central Limit Theorem19m
- Distribution of Sample Mean - Excel23m
- Introduction to Confidence Intervals15m
- Confidence Intervals for Population Mean1h 18m
- Determining the Minimum Sample Size Required12m
- Finding Probabilities and T Critical Values - Excel28m
- Confidence Intervals for Population Means - Excel25m
- 8. Sampling Distributions & Confidence Intervals: Proportion1h 25m
- 9. Hypothesis Testing for One Sample3h 29m
- 10. Hypothesis Testing for Two Samples4h 50m
- Two Proportions1h 13m
- Two Proportions Hypothesis Test - Excel28m
- Two Means - Unknown, Unequal Variance1h 3m
- Two Means - Unknown Variances Hypothesis Test - Excel12m
- Two Means - Unknown, Equal Variance15m
- Two Means - Unknown, Equal Variances Hypothesis Test - Excel9m
- Two Means - Known Variance12m
- Two Means - Sigma Known Hypothesis Test - Excel21m
- Two Means - Matched Pairs (Dependent Samples)42m
- Matched Pairs Hypothesis Test - Excel12m
- 11. Correlation1h 24m
- 12. Regression1h 50m
- 13. Chi-Square Tests & Goodness of Fit2h 21m
- 14. ANOVA1h 57m
3. Describing Data Numerically
Mean
Problem 3.1.30
Textbook Question
In Exercises 29–32, compute the mean of the data summarized in the frequency distribution. Also, compare the computed means to the actual means obtained by using the original list of data values, which are as follows: (29) 31.4 minutes; (Exercise 30) 140.6 minutes; (Exercise 31) 55.2 years; (Exercise 32) 240.2 seconds.

Verified step by step guidance1
Step 1: Identify the midpoint of each class interval. The midpoint is calculated as the average of the lower and upper boundaries of each interval. For example, for the interval 70–89, the midpoint is (70 + 89) / 2 = 79.5.
Step 2: Multiply the midpoint of each class interval by its corresponding frequency to find the weighted contribution of each interval. For example, for the interval 70–89, the weighted contribution is 79.5 × 4.
Step 3: Sum all the weighted contributions obtained in Step 2. This gives the total weighted sum of the data.
Step 4: Sum all the frequencies to find the total number of data points. For example, add 4 + 7 + 6 + 6 + 18 + 5 + 1 + 3.
Step 5: Divide the total weighted sum (from Step 3) by the total frequency (from Step 4) to compute the mean of the data summarized in the frequency distribution.
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Key Concepts
Here are the essential concepts you must grasp in order to answer the question correctly.
Mean
The mean, or average, is a measure of central tendency calculated by summing all data values and dividing by the number of values. In the context of a frequency distribution, the mean can be computed by multiplying each midpoint of the class intervals by its corresponding frequency, summing these products, and then dividing by the total frequency. This provides a representative value of the dataset.
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Frequency Distribution
A frequency distribution is a summary of how often each value or range of values occurs in a dataset. It organizes data into classes or intervals, showing the number of observations (frequency) within each interval. This helps in visualizing the distribution of data and is essential for calculating measures like the mean, as it provides the necessary structure for analysis.
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Comparison of Means
Comparing the mean calculated from a frequency distribution to the mean derived from the original dataset allows for an assessment of accuracy and representation. Discrepancies between these means can indicate how well the frequency distribution captures the underlying data. This comparison is crucial for validating the results and understanding the potential impact of data summarization techniques.
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