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1) Following the four steps in Ch 14: Handout #2, perform the hypothesis test to answer this question: Is there a statistically significant relationship between Monthly Rent and Distance from parking? Use an a = 0.05 significance level. You can report the appropriate t test statistic from the output table above, but be aware that you could calculate it by hand using ttest = b₁/S₂. You can show the p-value approach only, and just report the p-value from the output (but the df = n-p-1, same as the SSE, if you want to check the CV approach too :). For the following questions, refer to the Regression Output Equations roadmap and the output above. 2) How many observations were in this dataset? (n = number of observations) n = 3) Identify the SSR, SSE, and SST on the output on page 1. SSR = SSE = SST = Which of these is minimized by the regression procedure, in order to determine the slope and intercept of the regression line? 2

1) Following the four steps in Ch 14: Handout #2, perform the hypothesis test to answer this question: Is there a statistically significant relationship between Monthly Rent and Distance from parking? Use an a = 0.05 significance level. You can report the appropriate t test statistic from the output table above, but be aware that you could calculate it by hand using ttest = b₁/S₂. You can show the p-value approach only, and just report the p-value from the output (but the df = n-p-1, same as the SSE, if you want to check the CV approach too :). For the following questions, refer to the Regression Output Equations roadmap and the output above. 2) How many observations were in this dataset? (n = number of observations) n = 3) Identify the SSR, SSE, and SST on the output on page 1. SSR = SSE = SST = Which of these is minimized by the regression procedure, in order to determine the slope and intercept of the regression line? 2

MATLAB: An Introduction with Applications
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
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Certainly! Here is a transcription of the text from the image, designed for an educational context: --- **Hypothesis Testing on the Relationship Between Monthly Rent and Distance from Parking** 1) Following the four steps in **Ch 14: Handout #2**, perform a hypothesis test to answer this question: **Is there a statistically significant relationship between Monthly Rent and Distance from parking?** Use an \(\alpha = 0.05\) significance level. You can report the appropriate t-test statistic from the output table above, but be aware that you could calculate it by hand using \( t_{\text{test}} = \frac{b_1}{s_{b_1}} \). You can show the p-value approach only, and just report the p-value from the output (but the df \( = n - p - 1 \), same as the SSE, if you want to check the CV approach too :)). **For the following questions, refer to the Regression Output Equations roadmap and the output above.** 2) How many observations were in this dataset? (\( n = \text{number of observations} \)) \( n = \) 3) Identify the SSR, SSE, and SST on the output on page 1. \( SSR = \) \( SSE = \) \( SST = \) Which of these is minimized by the regression procedure, in order to determine the slope and intercept of the regression line? --- *Note: This text refers to hypothetical data and results not included in the image. Be sure to consult the relevant regression output data for completing your analysis.*
Transcribed Image Text:Certainly! Here is a transcription of the text from the image, designed for an educational context: --- **Hypothesis Testing on the Relationship Between Monthly Rent and Distance from Parking** 1) Following the four steps in **Ch 14: Handout #2**, perform a hypothesis test to answer this question: **Is there a statistically significant relationship between Monthly Rent and Distance from parking?** Use an \(\alpha = 0.05\) significance level. You can report the appropriate t-test statistic from the output table above, but be aware that you could calculate it by hand using \( t_{\text{test}} = \frac{b_1}{s_{b_1}} \). You can show the p-value approach only, and just report the p-value from the output (but the df \( = n - p - 1 \), same as the SSE, if you want to check the CV approach too :)). **For the following questions, refer to the Regression Output Equations roadmap and the output above.** 2) How many observations were in this dataset? (\( n = \text{number of observations} \)) \( n = \) 3) Identify the SSR, SSE, and SST on the output on page 1. \( SSR = \) \( SSE = \) \( SST = \) Which of these is minimized by the regression procedure, in order to determine the slope and intercept of the regression line? --- *Note: This text refers to hypothetical data and results not included in the image. Be sure to consult the relevant regression output data for completing your analysis.*
**Worksheet #10** An analyst for a shopping district would like to determine the rent that should be charged for retail spaces depending on how close they are to parking. The analyst takes a random sample of retail spaces in similar shopping districts and measures the monthly rent ($) and distance from parking (in yards) for each retail space. This is the same regression you worked with in Worksheet #9. Here is the complete Excel output for this regression: --- **SUMMARY OUTPUT** **Regression Statistics** - Multiple R: 0.7076 - R Square: 0.5007 - Adjusted R Square: 0.4918 - Standard Error: 978.8761 - Observations: 58 --- **ANOVA** | | df | SS | MS | F | Significance F | |------------------|-----|---------------------|--------------------|-----------|----------------| | Regression | 1 | 53809748.97 | 53809748.97 | 56.1572 | 5.28E-10 | | Residual | 56 | 53659115.74 | 958198.4954 | | | | Total | 57 | 107468864.7 | | | | --- **Coefficients** | Coefficients | Standard Error | t Stat | P-value | Lower 95% | Upper 95% | |--------------|----------------|--------|---------|-----------|-----------| | Intercept | 15003.10 | 249.3464 | 60.17 | 1.4E-52 | 14503.59 | 15502.6 | | Distance | -11.42 | 1.5239 | -7.49 | 5.28E-10 | -14.47 | -8.37 | **NOTE:** Excel uses scientific notation for very small numbers. So the p-value = 5.28E-10 = 5.28 × 10⁻¹⁰ = 0.000000000528. In hypothesis testing, you may truncate that to 0.000. Very tiny p-values with more than four zeroes after the decimal are often expressed as 0.000 or .
Transcribed Image Text:**Worksheet #10** An analyst for a shopping district would like to determine the rent that should be charged for retail spaces depending on how close they are to parking. The analyst takes a random sample of retail spaces in similar shopping districts and measures the monthly rent ($) and distance from parking (in yards) for each retail space. This is the same regression you worked with in Worksheet #9. Here is the complete Excel output for this regression: --- **SUMMARY OUTPUT** **Regression Statistics** - Multiple R: 0.7076 - R Square: 0.5007 - Adjusted R Square: 0.4918 - Standard Error: 978.8761 - Observations: 58 --- **ANOVA** | | df | SS | MS | F | Significance F | |------------------|-----|---------------------|--------------------|-----------|----------------| | Regression | 1 | 53809748.97 | 53809748.97 | 56.1572 | 5.28E-10 | | Residual | 56 | 53659115.74 | 958198.4954 | | | | Total | 57 | 107468864.7 | | | | --- **Coefficients** | Coefficients | Standard Error | t Stat | P-value | Lower 95% | Upper 95% | |--------------|----------------|--------|---------|-----------|-----------| | Intercept | 15003.10 | 249.3464 | 60.17 | 1.4E-52 | 14503.59 | 15502.6 | | Distance | -11.42 | 1.5239 | -7.49 | 5.28E-10 | -14.47 | -8.37 | **NOTE:** Excel uses scientific notation for very small numbers. So the p-value = 5.28E-10 = 5.28 × 10⁻¹⁰ = 0.000000000528. In hypothesis testing, you may truncate that to 0.000. Very tiny p-values with more than four zeroes after the decimal are often expressed as 0.000 or .
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