Table of contents
- 1. Intro to Stats and Collecting Data24m
- 2. Describing Data with Tables and Graphs1h 55m
- 3. Describing Data Numerically53m
- 4. Probability1h 29m
- 5. Binomial Distribution & Discrete Random Variables1h 16m
- 6. Normal Distribution and Continuous Random Variables58m
- 7. Sampling Distributions & Confidence Intervals: Mean1h 3m
- 8. Sampling Distributions & Confidence Intervals: Proportion1h 5m
- 9. Hypothesis Testing for One Sample1h 1m
- 10. Hypothesis Testing for Two Samples2h 8m
- 11. Correlation48m
3. Describing Data Numerically
Describing Data Numerically Using a Graphing Calculator
Problem 10.1.9b
Textbook Question
Outlier Refer to the accompanying Minitab-generated scatterplot.

b. After identifying the 10 pairs of coordinates corresponding to the 10 points, find the value of the correlation coefficient r and determine whether there is a linear correlation.

1
Step 1: Identify the 10 pairs of coordinates from the scatterplot. Each point represents a pair (x, y). For example, the points in the lower cluster might have coordinates like (1, 1), (1, 2), (1, 3), etc., while the outlier point is at (10, 10). Carefully list all 10 pairs.
Step 2: Calculate the mean of the x-values and the mean of the y-values. These will be used in the formula for the correlation coefficient r.
Step 3: Use the formula for the correlation coefficient r: r = (Σ((x_i - x̄)(y_i - ȳ))) / sqrt(Σ((x_i - x̄)^2) * Σ((y_i - ȳ)^2)), where x̄ and ȳ are the means of x and y, respectively. Compute the numerator and denominator separately.
Step 4: Analyze the value of r. If r is close to 1 or -1, it indicates a strong linear correlation. If r is close to 0, it indicates little to no linear correlation. Consider the impact of the outlier on the correlation coefficient.
Step 5: Determine whether there is a linear correlation by comparing the calculated r value to a critical value from a correlation table (based on the sample size and significance level). If |r| exceeds the critical value, there is significant linear correlation; otherwise, there is not.

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