Table of contents
- 1. Intro to Stats and Collecting Data55m
- 2. Describing Data with Tables and Graphs1h 55m
- 3. Describing Data Numerically1h 45m
- 4. Probability2h 16m
- 5. Binomial Distribution & Discrete Random Variables2h 33m
- 6. Normal Distribution and Continuous Random Variables1h 38m
- 7. Sampling Distributions & Confidence Intervals: Mean1h 3m
- 8. Sampling Distributions & Confidence Intervals: Proportion1h 12m
- 9. Hypothesis Testing for One Sample1h 1m
- 10. Hypothesis Testing for Two Samples2h 8m
- 11. Correlation48m
- 12. Regression1h 4m
- 13. Chi-Square Tests & Goodness of Fit1h 20m
- 14. ANOVA1h 0m
3. Describing Data Numerically
Standard Deviation
Problem 2.4.54c
Textbook Question
Shifting Data Sample annual salaries (in thousands of dollars) for employees at a company are listed.
40 35 49 53 38 39 40
37 49 34 38 43 47 35
c. Each employee in the sample takes a pay cut of $2000 from their original salary. Find the sample mean and the sample standard deviation for the revised data set.

1
Step 1: Understand the problem. The original data set represents annual salaries in thousands of dollars. Each employee takes a pay cut of $2000, which is equivalent to subtracting 2 from each value in the data set (since the salaries are in thousands). This will create a new data set.
Step 2: Calculate the sample mean for the original data set. The formula for the sample mean is: , where represents each data point and is the number of data points. Compute this for the original data set.
Step 3: Adjust the sample mean for the revised data set. Since subtracting a constant from each data point shifts the entire data set by that constant, the new sample mean is simply the original sample mean minus 2.
Step 4: Calculate the sample standard deviation for the original data set. The formula for the sample standard deviation is: . Compute this for the original data set.
Step 5: Adjust the sample standard deviation for the revised data set. Subtracting a constant from each data point does not affect the spread of the data, so the sample standard deviation remains the same as the original data set.

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Key Concepts
Here are the essential concepts you must grasp in order to answer the question correctly.
Sample Mean
The sample mean is the average of a set of values, calculated by summing all the values and dividing by the number of observations. In this context, it represents the average salary of employees after a uniform pay cut. The mean is sensitive to changes in data, so a consistent adjustment, like a pay cut, will directly affect its value.
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Sample Standard Deviation
The sample standard deviation measures the amount of variation or dispersion in a set of values. It indicates how much individual salaries deviate from the sample mean. When all salaries are uniformly adjusted (e.g., a pay cut), the standard deviation remains unchanged, as the relative differences between salaries do not alter.
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Uniform Transformation
A uniform transformation refers to applying the same change to all data points in a dataset. In this case, reducing each salary by $2000 is a uniform transformation. This type of adjustment affects the mean but not the standard deviation, as it does not alter the distribution of the data, only shifts it.
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