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: Proportion2h 10m
- 9. Hypothesis Testing for One Sample5h 9m
- Steps in Hypothesis Testing1h 6m
- Performing Hypothesis Tests: Means1h 4m
- Hypothesis Testing: Means - Excel42m
- Performing Hypothesis Tests: Proportions37m
- Hypothesis Testing: Proportions - Excel27m
- Performing Hypothesis Tests: Variance12m
- Critical Values and Rejection Regions28m
- Link Between Confidence Intervals and Hypothesis Testing12m
- Type I & Type II Errors17m
- 10. Hypothesis Testing for Two Samples5h 37m
- 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
- Two Variances and F Distribution29m
- Two Variances - Graphing Calculator16m
- 11. Correlation1h 24m
- 12. Regression3h 33m
- Linear Regression & Least Squares Method26m
- Residuals12m
- Coefficient of Determination12m
- Regression Line Equation and Coefficient of Determination - Excel8m
- Finding Residuals and Creating Residual Plots - Excel11m
- Inferences for Slope31m
- Enabling Data Analysis Toolpak1m
- Regression Readout of the Data Analysis Toolpak - Excel21m
- Prediction Intervals13m
- Prediction Intervals - Excel19m
- Multiple Regression - Excel29m
- Quadratic Regression15m
- Quadratic Regression - Excel10m
- 13. Chi-Square Tests & Goodness of Fit2h 21m
- 14. ANOVA2h 28m
3. Describing Data Numerically
Interpreting Standard Deviation
Problem 2.4.20
Textbook Question
Salary Offers You are applying for jobs at two companies. Company C offers starting salaries with μ = \$59,000 and σ = \$1500. Company D offers starting salaries with μ = \$59,000 and σ = \$1000. From which company are you more likely to get an offer of \$62,000 or more? Explain your reasoning.
Verified step by step guidance1
Step 1: Recognize that the problem involves comparing probabilities of getting a salary offer of \$62,000 or more from two companies. This requires calculating the z-scores for \$62,000 for both companies, as the z-score standardizes the value relative to the mean and standard deviation of each distribution.
Step 2: Use the z-score formula: z = (X - μ) / σ, where X is the value of interest (\$62,000), μ is the mean salary, and σ is the standard deviation. For Company C, substitute μ = 59,000 and σ = 1,500 into the formula.
Step 3: Similarly, calculate the z-score for Company D using the same formula, but substitute μ = 59,000 and σ = 1,000.
Step 4: Once the z-scores are calculated for both companies, use a standard normal distribution table (or a statistical software) to find the probability corresponding to each z-score. Since the problem asks for \$62,000 or more, calculate the area to the right of the z-score (1 - cumulative probability).
Step 5: Compare the probabilities for both companies. The company with the smaller probability of \$62,000 or more is less likely to offer that salary, while the company with the larger probability is more likely. Explain your reasoning based on the spread (standard deviation) of the distributions.
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Key Concepts
Here are the essential concepts you must grasp in order to answer the question correctly.
Normal Distribution
Normal distribution is a probability distribution that is symmetric about the mean, showing that data near the mean are more frequent in occurrence than data far from the mean. In this context, both companies' salary offers can be modeled as normal distributions, characterized by their mean (μ) and standard deviation (σ). Understanding this concept is crucial for determining the likelihood of receiving a salary offer above a certain threshold.
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Z-Score
A Z-score indicates how many standard deviations an element is from the mean. It is calculated using the formula Z = (X - μ) / σ, where X is the value of interest. By calculating the Z-scores for the $62,000 salary offer for both companies, we can compare how likely it is to receive such an offer relative to each company's salary distribution.
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Probability and Area Under the Curve
In the context of normal distributions, the probability of a certain outcome can be determined by calculating the area under the curve of the distribution. This area represents the likelihood of receiving a salary offer of $62,000 or more. By using Z-scores to find the corresponding probabilities from standard normal distribution tables, we can assess which company is more likely to provide a higher salary offer.
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Multiple Choice
In a normal distribution, approximately what percentage of observations lie within one standard deviation of the mean (i.e., between and )?
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