2.3-2.4 - Stat 133 Recitation 2.3 and 2.4 Regression (Part...

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Stat 133 Recitation 2.3 and 2.4 Regression (Part 1) We are comparing years of education and hours on the internet in the last month, to see if a relationship exists. If a relationship does exist, we want to predict Internet use using education level. The output is given below. Descriptive Statistics: Education, Internet Variable Mean StDev Variance Minimum Maximum Educatio 11.000 1.920 3.687 7.000 17.000 Internet 26.316 9.411 88.570 2.000 54.000 Pearson’s Correlation: 0.642 1. Interpret the correlation between Education and Internet use 2. Would a line fit this data well? Explain by interpreting the correlation. 3. Define which variable is X and which one is Y. 4. Find the slope of the best fitting line. 5. Find the Y-intercept of the best fitting line. 6. Find the equation of the best fitting line. 7. Use the line to predict Internet use for someone with 16 years of education. *8. For what education levels is it appropriate to use this line to make predictions? Use the statistics given in the problem to answer this. 9. The linear regression of the computer output for the Internet/Education data is shown below. Use this output to find the equation of the best fitting line and use it to verify your answer to #6 above. Predictor Coef SE Coef T P Constant -8.290 2.665 -3.11 0.002 Education 3.1460 0.2387 13.18 0.000
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Stat 133 Recitation 2.3 and 2.4 Data was collected on amount of rainfall (inches) and amount of corn produced (bushels per acre) for a number of years in Kansas. The output is shown below. Predictor Coef SE Coef T P Constant 89.543 6.703 13.36 0.000 Rainfall 0.12800 0.01375 9.31 0.000 Correlation of Rainfall and Corn = 0.608 10. If rainfall increases by 1 inch, how much do you predict corn production will change by? a. 89.543 bushels per acre b. 0.128 bushels per acre c. 0.608 bushels per acre d. None of the above or can’t tell without more information 11. How well does this slope to at making a good prediction about the change in corn production for each additional inch of rain? Suppose that the price (in $thousands) and size (in square feet) of a random sample of houses in Viroqua, Wisconsin was analyzed by a new statistician using Minitab. The group plans to us the data to help set prices for homes based on their size. Descriptive Statistics: Size Variable N N* Mean SE Mean StDev Variance Minimum Q1 Median Q3 Size 10 0 1993 110 349 121907 1526 1699 1957 2271 Variable Maximum Size 2595 Descriptive Statistics: Price Variable N N* Mean SE Mean StDev Variance Minimum Q1 Median Price 10 0 219.1 19.0 60.1 3612.1 148.0 163.5 200.5 Variable Q3 Maximum Price 269.8 315.0 Correlation: 0.9041 Regression Analysis Predictor Coef SE Coef T P-value Constant -90.88 52.62 -1.73 0.12 Size 0.15556 0.02605 5.97 0.00 12. True or False: Because there is a minus sign on 90.88, that means the slope of the best fitting line here is negative.
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This note was uploaded on 01/05/2012 for the course STAT 133 taught by Professor Rumseyjohnson during the Fall '07 term at Ohio State.

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2.3-2.4 - Stat 133 Recitation 2.3 and 2.4 Regression (Part...

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