**Title: Are the heights of individuals affected by the heights of their parents?**### Regression Statistics- **Multiple R:** 0.631071992- **R Square:** 0.398251859- **Adjusted R Square:** 0.365724932- **Standard Error:** 2.914527039- **Observations:** 40### ANOVA| Source | df | SS | MS | F | Significance F ||--------------|----|-------------|---------|----------|---------------------|| Regression | 2 | 208.0084392 | 104.0042| 12.24376 | 8.30181E-05 || Residual | 37 | 314.2953108 | 8.494468| | || Total | 39 | 522.30375 | | | |### Coefficients| | Coefficients | Standard Error | t Stat | P-value ||---------------|---------------|----------------|---------|----------|| Intercept | 9.804326378 | 12.39987353 | 0.79068 | 0.43417 || Mother's Height | 0.657952815 | 0.147476295 | 4.461414| 7.34E-05 || Father's Height | 0.200358437 | 0.138223638 | 1.449524| 0.155615 |### Questions1. **Write the regression equation that represents the above equation.**2. **Is this a good predictor equation? Why or why not (use appropriate statistics/hypothesis test to prove your point)?**3. **Use the equation to predict the height of someone whose mother is 52 inches tall and whose father is 70 inches tall.**

The regression equation will be Here, xm is the height of the mother xf is the height of the…

Answered: above equation. 2. Is this a good…

above equation. 2. Is this a good predictor equation? Why or why not (use appropriate statistics/hypothesis test to prove your point)?

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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Average Income 19320 17554 17310 15589 15266 15078 12749 9351 8866 4952 3627 3412 2554 2296 1871 1809 1710 1621 1345 1282 1171 1109 1091 810 585 Average Life Expectancy 76.25 75.3 78.65 75.51 76.19 76.36 73.73 73.9 76.38 71.16 64.19 74.35 57.53 56.64 62.71 57.34 54.98 46.93 52.24 53.44 60.99 57.21 49.99 45.91 60.03 In this practice assignment, you will use spreadsheet regression or the online regression calculator at http://www.alcula.com/calculators/statistics/linear- regression to examine the relationship between average annual income and average life expectancy in a country. This relationship is called the Preston curve. Open QR 8D Spreadsheet_Practice, which gives the average annual income and average life expectancy in 25 low-income countries. Each row corresponds to a country. Use technology (spreadsheet regression or the Alcula website given above) to create a scatterplot and regression equation for the Preston curve data. Round all answer values to the nearest ten thousandth…
The correlation coefficient r is a sample statistic. What does it tell us about the value of the population correlation coefficient ?(Greek letter rho)? You do not know how to build the formal structure of hypothesis tests of ? yet. However, there is a quick way to determine if the sample evidence based on ? is strong enough to conclude that there is some population correlation between the variables. In other words, we can use the value of r to determine if ? ≠ 0. We do this by comparing the value |r| to an entry in the correlation table. The value of ? in the table gives us the probability of concluding that ? ≠ 0 when, in fact, ? = 0 and there is no population correlation. We have two choices for ?: ? = 0.05 or ? = 0.01. (a) Look at the data below regarding the variables x = age of a Shetland pony and y = weight of that pony. Is the value of |r| large enough to conclude that weight and age of Shetland ponies are correlated? Use ? = 0.05. (Round your answer for r to four decimal…
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The correlation coefficient r is a sample statistic. What does it tell us about the value of the population correlation coefficient ? (Greek letter rho)? You do not know how to build the formal structure of hypothesis tests of ? yet. However, there is a quick way to determine if the sample evidence based on ? is strong enough to conclude that there is some population correlation between the variables. In other words, we can use the value of r to determine if ? ≠ 0. We do this by comparing the value |r| to an entry in the correlation table. The value of ? in the table gives us the probability of concluding that ? ≠ 0 when, in fact, ? = 0 and there is no population correlation. We have two choices for ?: ? = 0.05 or ? = 0.01.
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