CEO Performance Explain why it does not make sense to find a least-squares regression line for the CEO Performance data from Problem 33 in Section 4.1.
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
12. Regression
Linear Regression & Least Squares Method
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Join thousands of students who trust us to help them ace their exams!Watch the first videoMultiple Choice
A regional sales manager records data on the number of clients a salesperson contacts in a week (x) and the total sales generated that week (y). The data from 10 salespeople is shown below. Find the equation of the regression line and use it to predict sales if the salesperson contacts (a) 6 clients; (b) 40 clients

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B
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Verified step by step guidance1
Step 1: Understand the problem. The goal is to find the equation of the regression line (y = mx + b) using the given data, where x represents the number of clients contacted and y represents the total sales generated. Then, use the regression equation to predict sales for x = 6 and x = 40.
Step 2: Calculate the mean of x and y. The mean of x (number of clients) is calculated as the sum of all x values divided by the number of data points. Similarly, the mean of y (sales) is calculated as the sum of all y values divided by the number of data points.
Step 3: Compute the slope (m) of the regression line using the formula: m = (Σ(x_i - x̄)(y_i - ȳ)) / (Σ(x_i - x̄)^2), where x̄ and ȳ are the means of x and y, respectively. This involves calculating the deviations of each x and y value from their respective means, multiplying these deviations, summing them, and dividing by the sum of squared deviations of x.
Step 4: Calculate the y-intercept (b) using the formula: b = ȳ - m * x̄. Substitute the values of the slope (m) and the mean of x and y into this formula to find the y-intercept.
Step 5: Use the regression equation y = mx + b to predict sales for x = 6 and x = 40. Substitute x = 6 and x = 40 into the equation to calculate the corresponding y values (predicted sales).
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