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
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

A
B
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D

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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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